def run_network(traj):
"""Runs brian network consisting of
200 inhibitory IF neurons"""
eqs = '''
dv/dt=(v0-v)/(5*ms) : volt (unless refractory)
v0 : volt
'''
group = NeuronGroup(100,
model=eqs,
threshold='v>10 * mV',
reset='v = 0*mV',
refractory=5 * ms)
group.v0 = traj.par.v0
group.v = np.random.rand(100) * 10.0 * mV
syn = Synapses(group, group, on_pre='v-=1*mV')
syn.connect('i != j', p=0.2)
spike_monitor = SpikeMonitor(group, variables=['v'])
voltage_monitor = StateMonitor(group, 'v', record=True)
pop_monitor = PopulationRateMonitor(group, name='pop' + str(traj.v_idx))
net = Network(group, syn, spike_monitor, voltage_monitor, pop_monitor)
net.run(0.25 * second, report='text')
traj.f_add_result(Brian2MonitorResult, 'spikes', spike_monitor)
traj.f_add_result(Brian2MonitorResult, 'v', voltage_monitor)
traj.f_add_result(Brian2MonitorResult, 'pop', pop_monitor)
def brian2(self) -> BrianObject:
model = Equations(
'''
w : 1
s_post = w * s : 1 (summed)
ds / dt = - s / tau + alpha * x * (1 - s) : 1 (clock-driven)
dx / dt = - x / tau_rise : 1 (clock-driven)
''',
s_post=f'{self.post_variable_name_tot}_post',
s=self.post_variable_name,
x=self.post_nonlinear_name,
tau=self[IP.TAU],
tau_rise=self[IP.TAU_NMDA_RISE],
alpha=self[IP.ALPHA],
)
eqs_pre = f'''
{self.post_nonlinear_name} += 1
'''
C = Synapses(
self.origin.brian2,
self.target.brian2,
method='euler',
model=model,
on_pre=eqs_pre,
name=self.ref,
)
C.connect()
C.w[:] = 1
return C
def test_store_restore_magic():
source = NeuronGroup(10, '''dv/dt = rates : 1
rates : Hz''', threshold='v>1', reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', pre='v+=w', connect='i==j')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
store() # default time slot
run(10*ms)
store('second')
run(10*ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
restore() # Go back to beginning
assert magic_network.t == 0*ms
run(20*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
restore('second')
assert magic_network.t == 10*ms
run(10*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
def brian2(self) -> BrianObject:
model = Equations('''
x : 1
u : 1
w : 1
''')
U = self[IP.U]
tauf = self[IP.TAU_F]
taud = self[IP.TAU_D]
on_pre = f'''
u = {U} + (u - {U}) * exp(- (t - lastupdate) / ({tauf / units.ms} * ms))
x = 1 + (x - 1) * exp(- (t - lastupdate) / ({taud / units.ms} * ms))
{self.post_variable_name} += w * u * x
x *= (1 - u)
u += {U} * (1 - u)
'''
syn = Synapses(source=self.origin.brian2,
target=self.target.brian2,
method='euler',
model=model,
on_pre=on_pre,
name=self.ref)
self.connection.simulation(syn)
syn.w[:] = self[IP.W]
syn.x[:] = 1
syn.u[:] = 1
return syn
def run_network(traj):
"""Runs brian network consisting of
200 inhibitory IF neurons"""
eqs = '''
dv/dt=(v0-v)/(5*ms) : volt (unless refractory)
v0 : volt
'''
group = NeuronGroup(100, model=eqs, threshold='v>10 * mV',
reset='v = 0*mV', refractory=5*ms)
group.v0 = traj.par.v0
group.v = np.random.rand(100) * 10.0 * mV
syn = Synapses(group, group, on_pre='v-=1*mV')
syn.connect('i != j', p=0.2)
spike_monitor = SpikeMonitor(group, variables=['v'])
voltage_monitor = StateMonitor(group, 'v', record=True)
pop_monitor = PopulationRateMonitor(group, name='pop' + str(traj.v_idx))
net = Network(group, syn, spike_monitor, voltage_monitor, pop_monitor)
net.run(0.25*second, report='text')
traj.f_add_result(Brian2MonitorResult, 'spikes',
spike_monitor)
traj.f_add_result(Brian2MonitorResult, 'v',
voltage_monitor)
traj.f_add_result(Brian2MonitorResult, 'pop',
pop_monitor)
def create_net():
# Use a bit of a complicated spike and connection pattern with
# heterogeneous delays
# Note: it is important that all objects have the same name, this would
# be the case if we were running this in a new process but to not rely
# on garbage collection we will assign explicit names here
source = SpikeGeneratorGroup(
5,
np.arange(5).repeat(3),
[3, 4, 1, 2, 3, 7, 5, 4, 1, 0, 5, 9, 7, 8, 9] * ms,
name='source')
target = NeuronGroup(10, 'v:1', name='target')
synapses = Synapses(source,
target,
model='w:1',
pre='v+=w',
connect='j>=i',
name='synapses')
synapses.w = 'i*1.0 + j*2.0'
synapses.delay = '(5-i)*ms'
state_mon = StateMonitor(target, 'v', record=True, name='statemonitor')
input_spikes = SpikeMonitor(source, name='input_spikes')
net = Network(source, target, synapses, state_mon, input_spikes)
return net
def build(self, traj, brian_list, network_dict):
if not self.pre_built:
group = network_dict['group']
syn = Synapses(group, group, on_pre=traj.pre)
syn.connect('i != j', p=traj.prob)
brian_list.append(syn)
network_dict['synapses'] = syn
def get_synapses(stimulus, neuron, tau_inact, A_SE, U_SE, tau_rec, tau_facil=None):
"""
stimulus -- input stimulus
neuron -- target neuron
tau_inact -- inactivation time constant
A_SE -- absolute synaptic strength
U_SE -- utilization of synaptic efficacy
tau_rec -- recovery time constant
tau_facil -- facilitation time constant (optional)
"""
synapses_eqs = """
dx/dt = z/tau_rec : 1 (clock-driven) # recovered
dy/dt = -y/tau_inact : 1 (clock-driven) # active
A_SE : ampere
U_SE : 1
tau_inact : second
tau_rec : second
z = 1 - x - y : 1 # inactive
I_syn_post = A_SE*y : ampere (summed)
"""
if tau_facil:
synapses_eqs += """
du/dt = -u/tau_facil : 1 (clock-driven)
tau_facil : second
"""
synapses_action = """
u += U_SE*(1-u)
y += u*x # important: update y first
x += -u*x
"""
else:
synapses_action = """
y += U_SE*x # important: update y first
x += -U_SE*x
"""
synapses = Synapses(stimulus,
neuron,
model=synapses_eqs,
on_pre=synapses_action,
method="exponential_euler")
synapses.connect()
# start fully recovered
synapses.x = 1
synapses.tau_inact = tau_inact
synapses.A_SE = A_SE
synapses.U_SE = U_SE
synapses.tau_rec = tau_rec
if tau_facil:
synapses.tau_facil = tau_facil
return synapses
def test_synapse_init():
# check initializations validity for synapse variables
start_scope()
set_device('exporter')
eqn = 'dv/dt = -v/tau :1'
tau = 1 * ms
w = 1
P = NeuronGroup(5, eqn, method='euler', threshold='v>0.8')
Q = NeuronGroup(10, eqn, method='euler', threshold='v>0.9')
S = Synapses(P, Q, 'g :1', on_pre='v += w')
S.connect()
# allowable
S.g['i>10'] = 10
S.g[-1] = -1
S.g[10000] = 'rand() + w + w'
mon = StateMonitor(S, 'g', record=[0, 1])
run(1 * ms)
# not allowable
with pytest.raises(NotImplementedError):
S.g[0:1000] = -1
run(0.5 * ms)
with pytest.raises(NotImplementedError):
S.g[0:1] = 'rand() + 10'
run(0.25 * ms)
with pytest.raises(NotImplementedError):
_ = StateMonitor(S, 'g', S.g[0:10])
device.reinit()
def brian2(self) -> BrianObject:
model = Equations('w : 1')
on_pre = f'{self.post_variable_name} += w'
syn = Synapses(source=self.origin.brian2,
target=self.target.brian2,
method='euler',
model=model,
on_pre=on_pre,
name=self.ref)
self.connection.simulation(syn)
syn.w[:] = self[IP.W]
return syn
def test_simple_syntax():
"""
Simple example
"""
set_device('markdown')
N = 10
tau = 10 * ms
v_th = 0.9 * volt
v_rest = -79 * mV
eqn = 'dv/dt = (v_th - v)/tau :volt'
refractory = 'randn() * tau / N'
rates = 'rand() * 5 * Hz'
group = NeuronGroup(N, eqn, method='euler', threshold='v > v_th',
reset='v = v_rest; v = rand() * v_rest',
refractory=refractory,
events={'custom': 'v > v_th + 10 * mV',
'custom_1': 'v > v_th - 10 * mV'})
group.run_on_event('custom', 'v = v_rest')
group.run_on_event('custom_1', 'v = v_rest - 0.001 * mV')
spikegen = SpikeGeneratorGroup(N, [0, 1, 2], [1, 2, 3] * ms,
period=5 * ms)
po_grp = PoissonGroup(N - 1, rates=rates)
syn = Synapses(spikegen, group, model='w :volt',
on_pre='v = rand() * w + v_th; v = rand() * w',
on_post='v = rand() * w + v_rest; v = rand() * w',
delay=tau, method='euler')
group.v[:] = v_rest
group.v['i%2 == 0'] = 'rand() * v_rest'
group.v[0:5] = 'v_rest + 10 * mV'
condition = 'abs(i-j)<=5'
syn.connect(condition=condition, p=0.999, n=2)
syn.w = '1 * mV'
net = Network(group, spikegen, po_grp, syn)
mon = StateMonitor(syn, 'w', record=True)
mon2 = SpikeMonitor(po_grp)
mon3 = EventMonitor(group, 'custom')
net.add(mon, mon2, mon3)
net.run(0.01 * ms)
md_str = device.md_text
assert _markdown_lint(md_str)
check = 'randn({sin({$w$}|$v_rest$ - $v$|/{\tau}})})'
assert _markdown_lint(check)
# check invalid strings
with pytest.raises(SyntaxError):
check = '**Initializing values at starting:*'
assert _markdown_lint(check)
check = '- Variable v$ of with $-79. mV$ to all members'
assert _markdown_lint(check)
check = 'randn({sin(})})'
assert _markdown_lint(check)
check = 'randn({sin({$w$}|$v_rest$ - $v$|/{\tau})})'
assert _markdown_lint(check)
device.reinit()
def test_synapse_connect_generator():
# connector test 3
start_scope()
set_device('exporter', build_on_run=False)
tau = 1 * ms
eqn = 'dv/dt = (1 - v)/tau :1'
Source = NeuronGroup(10, eqn, method='exact', threshold='v>0.9')
S1 = Synapses(Source, Source)
nett2 = Network(Source, S1)
S1.connect(j='k for k in range(0, i+1)')
nett2.run(1 * ms)
connect3 = device.runs[0]['initializers_connectors'][0]
assert connect3['j'] == 'k for k in range(0, i+1)'
device.reinit()
def brian2(self) -> BrianObject:
model = Equations('''
w1 : 1
w2 : 1
w3 : 1
w4 : 1
''')
on_pre = f'''
{self.post_variable_name[0]} += w1
{self.post_variable_name[1]} += w2
{self.post_variable_name[2]} += w3
{self.post_variable_name[3]} += w4
'''
syn = Synapses(
source=self.origin.brian2,
target=self.target.brian2,
method='euler',
model=model,
on_pre=on_pre,
name=self.ref,
)
syn.connect(j='i')
syn.w1[:] = 1
syn.w2[:] = 1
syn.w3[:] = 1
syn.w4[:] = 1
return syn
def cramer_noise(tr, GExc: NeuronGroup, GInh: NeuronGroup) -> [BrianObject]:
"""
Noise inspired by Cramer et al. 2020 (preprint) where a certain number Kext of incoming
connections to each neuron is replaced by external noise.
Related parameters:
- :data:`net.standard_params.cramer_noise_active`
- :data:`net.standard_params.cramer_noise_Kext`
- :data:`net.standard_params.cramer_noise_rate`
"""
namespace = tr.netw.f_to_dict(short_names=True, fast_access=True)
N = tr.N_e
Kext = tr.cramer_noise_Kext
p_ee_target = tr.p_ee / (1 - Kext)
p_ie_target = tr.p_ie / (1 - Kext)
conductance_prefix = "" if tr.syn_cond_mode == "exp" else "x"
GNoise = PoissonGroup(N, rates=tr.cramer_noise_rate, dt=0.1 * ms)
SynNoiseE = Synapses(source=GNoise,
target=GExc,
on_pre=f"{conductance_prefix}ge_post += a_ee",
namespace=namespace)
SynNoiseE.connect(p=Kext * p_ee_target)
SynNoiseI = Synapses(source=GNoise,
target=GInh,
on_pre=f"{conductance_prefix}ge_post += a_ie",
namespace=namespace)
SynNoiseI.connect(p=Kext * p_ie_target)
return [GNoise, SynNoiseE, SynNoiseI]
def echo_spike(p, tStim, tTot):
start_scope()
prefs.codegen.target = "numpy"
################################
# Compute properties
################################
dvInpSpike = p['nu_DV'] # Voltage increase per noise spike
dvExcSpike = p['LIF_DV'] # Voltage increase per lateral spike
# dvExcSpike = p['LIF_T_0'] / (1.0 * p['N'] * p['p_conn']) # Voltage increase per lateral spike
print("typical spike threshold", p['LIF_T_0'])
print("typical potential change per noise spike", dvInpSpike)
print("typical potential change per lateral spike", dvExcSpike)
################################
# Create input population
################################
nTStimPost = int(tTot / tStim) - 1 # After input switched off, 0-input will be repeated nTStimPost*tStim timesteps
patternInp = (np.random.uniform(0, 1, p['N']) < p['nu_p']).astype(int)
pattern0 = np.zeros(p['N'])
rates_all = np.array([patternInp] + [pattern0]*nTStimPost) * p['nu_FREQ']
rateTimedArray = TimedArray(rates_all, dt=tStim)
gInp = PoissonGroup(p['N'], rates="rateTimedArray(t, i)")
# gInp = PoissonGroup(p['N'], p['nu_FREQ'])
################################
# Create reservoir population
################################
gExc = brian2wrapper.NeuronGroupLIF(p['N'], p['LIF_V_0'], p['LIF_T_0'], p['LIF_V_TAU'])
################################
# Create synapses
################################
sInpExc = Synapses(gInp, gExc, on_pre='v_post += dvInpSpike', method='exact')
sExcExc = Synapses(gExc, gExc, on_pre='v_post += dvExcSpike', method='exact')
################################
# Connect synapses
################################
# * Input and LIF one-to-one
# * LIF neurons to each other sparsely
sInpExc.connect(j='i')
sExcExc.connect(p=p['p_conn'])
################################
# Init Monitors
################################
#spikemonInp = SpikeMonitor(gInp)
spikemonExc = SpikeMonitor(gExc)
################################
# Run simulation
################################
run(tTot)
return np.array(spikemonExc.i), np.array(spikemonExc.t)
def test_store_restore_to_file():
filename = tempfile.mktemp(suffix='state', prefix='brian_test')
source = NeuronGroup(10,
'''dv/dt = rates : 1
rates : Hz''',
threshold='v>1',
reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source,
target,
model='w:1',
pre='v+=w',
connect='i==j')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
net = Network(source, target, synapses, state_mon, spike_mon)
net.store(filename=filename) # default time slot
net.run(10 * ms)
net.store('second', filename=filename)
net.run(10 * ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
net.restore(filename=filename) # Go back to beginning
assert defaultclock.t == 0 * ms
assert net.t == 0 * ms
net.run(20 * ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
net.restore('second', filename=filename)
assert defaultclock.t == 10 * ms
assert net.t == 10 * ms
net.run(10 * ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
try:
os.remove(filename)
except OSError:
pass
def test_magic_collect():
'''
Make sure all expected objects are collected in a magic network
'''
P = PoissonGroup(10, rates=100*Hz)
G = NeuronGroup(10, 'v:1')
S = Synapses(G, G, '')
G_runner = G.custom_operation('')
S_runner = S.custom_operation('')
state_mon = StateMonitor(G, 'v', record=True)
spike_mon = SpikeMonitor(G)
rate_mon = PopulationRateMonitor(G)
objects = collect()
assert len(objects) == 8, ('expected %d objects, got %d' % (8, len(objects)))
def do_synapse(self):
# Maybe should import
from brian2 import Synapses
# Check
if len(self.SynapsingDelayDict) > 0.0:
# map
map(lambda __ItemTuple: self.setDelay(*__ItemTuple), self.SynapsingDelayDict.items())
# debug
"""
self.debug(('self.',self,[
'SynapsingBrianKwargDict'
]))
"""
# init
self.SynapsedBrianVariable = Synapses(**self.SynapsingBrianKwargDict)
# connect
if type(self.SynapsingProbabilityVariable) == float:
# debug
"""
self.debug('we connect with a sparsity of '+str(self.SynapsingProbabilityVariable))
"""
self.SynapsedBrianVariable.connect(True, p=self.SynapsingProbabilityVariable)
# Check
if self.SynapsingWeigthSymbolStr != "":
# debug
"""
self.debug(
('self.',self,[
'SynapsedBrianVariable',
'SynapsingWeigthSymbolStr'
])
)
"""
# connect
self.SynapsedBrianVariable.connect(True)
# get
self.SynapsedWeigthFloatsArray = getattr(self.SynapsedBrianVariable, self.SynapsingWeigthSymbolStr)
# set
self.SynapsedWeigthFloatsArray[:] = np.reshape(
self.SynapsingWeigthFloatsArray,
self.SynapsedBrianVariable.source.N * self.SynapsedBrianVariable.target.N,
)
# debug
self.debug(("self.", self, ["SynapsedWeigthFloatsArray"]))
def test_magic_collect():
'''
Make sure all expected objects are collected in a magic network
'''
P = PoissonGroup(10, rates=100 * Hz)
G = NeuronGroup(10, 'v:1')
S = Synapses(G, G, '')
G_runner = G.custom_operation('')
S_runner = S.custom_operation('')
state_mon = StateMonitor(G, 'v', record=True)
spike_mon = SpikeMonitor(G)
rate_mon = PopulationRateMonitor(G)
objects = collect()
assert len(objects) == 8, ('expected %d objects, got %d' %
(8, len(objects)))
def create_net():
G = NeuronGroup(10, 'v: 1', threshold='False')
dependent_objects = [
StateMonitor(G, 'v', record=True),
SpikeMonitor(G),
PopulationRateMonitor(G),
Synapses(G, G, pre='v+=1', connect=True)
]
return dependent_objects
def create_net():
# Use a bit of a complicated spike and connection pattern with
# heterogeneous delays
# Note: it is important that all objects have the same name, this would
# be the case if we were running this in a new process but to not rely
# on garbage collection we will assign explicit names here
source = SpikeGeneratorGroup(5, np.arange(5).repeat(3),
[3, 4, 1, 2, 3, 7, 5, 4, 1, 0, 5, 9, 7, 8, 9]*ms,
name='source')
target = NeuronGroup(10, 'v:1', name='target')
synapses = Synapses(source, target, model='w:1', pre='v+=w', connect='j>=i',
name='synapses')
synapses.w = 'i*1.0 + j*2.0'
synapses.delay = '(5-i)*ms'
state_mon = StateMonitor(target, 'v', record=True, name='statemonitor')
input_spikes = SpikeMonitor(source, name='input_spikes')
net = Network(source, target, synapses, state_mon, input_spikes)
return net
def test_synapse_connect_ij():
# connector test 2
start_scope()
set_device('exporter', build_on_run=False)
tau = 10 * ms
eqn = 'dv/dt = (1 - v)/tau :1'
my_prob = -1
Source = NeuronGroup(10, eqn, method='exact', threshold='v>0.9')
S1 = Synapses(Source, Source)
nett = Network(Source, S1)
S1.connect(i=[0, 1], j=[1, 2], p='my_prob')
nett.run(1 * ms)
connect2 = device.runs[0]['initializers_connectors'][0]
assert connect2['i'] == [0, 1]
assert connect2['j'] == [1, 2]
assert connect2['identifiers']['my_prob'] == -1
with pytest.raises(KeyError):
connect2['condition']
device.reinit()
def test_store_restore_to_file():
filename = tempfile.mktemp(suffix='state', prefix='brian_test')
source = NeuronGroup(10, '''dv/dt = rates : 1
rates : Hz''', threshold='v>1', reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', on_pre='v+=w')
synapses.connect(j='i')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
net = Network(source, target, synapses, state_mon, spike_mon)
net.store(filename=filename) # default time slot
net.run(10*ms)
net.store('second', filename=filename)
net.run(10*ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
net.restore(filename=filename) # Go back to beginning
assert defaultclock.t == 0*ms
assert net.t == 0*ms
net.run(20*ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
net.restore('second', filename=filename)
assert defaultclock.t == 10*ms
assert net.t == 10*ms
net.run(10*ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
try:
os.remove(filename)
except OSError:
pass
def test_store_restore():
source = NeuronGroup(10, '''dv/dt = rates : 1
rates : Hz''', threshold='v>1', reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', on_pre='v+=w')
synapses.connect(j='i')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
net = Network(source, target, synapses, state_mon, spike_mon)
net.store() # default time slot
net.run(10*ms)
net.store('second')
net.run(10*ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
net.restore() # Go back to beginning
assert defaultclock.t == 0*ms
assert net.t == 0*ms
net.run(20*ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
net.restore('second')
assert defaultclock.t == 10*ms
assert net.t == 10*ms
net.run(10*ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back again (see github issue #681)
net.restore('second')
assert defaultclock.t == 10 * ms
assert net.t == 10 * ms
def test_plot_synapses():
set_device('runtime')
group = NeuronGroup(10, 'dv/dt = -v/(10*ms) : volt', threshold='False')
group.v = np.linspace(0, 1, 10)*mV
synapses = Synapses(group, group, 'w : volt', on_pre='v += w')
synapses.connect('i != j')
synapses.w = 'i*0.1*mV'
# Just checking whether the plotting does not fail with an error and that
# it retuns an Axis object as promised
ax = brian_plot(synapses)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = brian_plot(synapses.w)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = brian_plot(synapses.delay)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, plot_type='scatter')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, plot_type='image')
add_background_pattern(ax)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, plot_type='hexbin')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, synapses.w)
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, synapses.w, plot_type='scatter')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, synapses.w, plot_type='image')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
ax = plot_synapses(synapses.i, synapses.j, synapses.w, plot_type='hexbin')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
synapses.connect('i > 5') # More than one synapse per connection
brian_plot(synapses)
plt.close()
# It should be possible to plot synaptic variables for multiple connections
# with hexbin
ax = plot_synapses(synapses.i, synapses.j, synapses.w, plot_type='hexbin')
assert isinstance(ax, matplotlib.axes.Axes)
plt.close()
def test_Subgroup():
"""
Test Subgroup
"""
eqn = '''
dv/dt = (1 - v) / tau :1
'''
tau = 10 * ms
group = NeuronGroup(10, eqn, threshold='v>10', reset='v=0', method='euler')
sub_a = group[0:5]
sub_b = group[0:10]
syn = Synapses(sub_a, sub_b)
syn.connect(p=0.8)
mon = StateMonitor(group[0:7], 'v', record=1)
sub_a.v = 1
sub_b.v = -1
mon_dict = collect_StateMonitor(mon)
assert mon_dict['source']['group'] == group.name
assert mon_dict['source']['start'] == 0
assert mon_dict['source']['stop'] == 6
syn_dict = collect_Synapses(syn, get_local_namespace(0))
assert syn_dict['source']['group'] == group.name
assert syn_dict['target']['start'] == 0
assert syn_dict['source']['stop'] == 4
def test_synapse_connect_cond():
# check connectors
start_scope()
set_device('exporter')
eqn = 'dv/dt = (1 - v)/tau :1'
tau = 1 * ms
P = NeuronGroup(5, eqn, method='euler', threshold='v>0.8')
Q = NeuronGroup(10, eqn, method='euler', threshold='v>0.9')
w = 1
tata = 2
bye = 2
my_prob = -1
S = Synapses(P, Q, on_pre='v += w')
S.connect('tata > bye', p='my_prob', n=5)
run(1 * ms)
connect = device.runs[0]['initializers_connectors'][0]
assert connect['probability'] == 'my_prob'
assert connect['n_connections'] == 5
assert connect['type'] == 'connect'
assert connect['identifiers']['tata'] == bye
with pytest.raises(KeyError):
connect['i']
connect['j']
device.reinit()
def test_magic_collect():
'''
Make sure all expected objects are collected in a magic network
'''
P = PoissonGroup(10, rates=100*Hz)
G = NeuronGroup(10, 'v:1', threshold='False')
S = Synapses(G, G, '')
state_mon = StateMonitor(G, 'v', record=True)
spike_mon = SpikeMonitor(G)
rate_mon = PopulationRateMonitor(G)
objects = collect()
assert len(objects) == 6, ('expected %d objects, got %d' % (6, len(objects)))
def SynapsesPlastic(G1, G2, plasticity_model):
if plasticity_model['TYPE'][:4] == 'STDP':
# # Extract parameters
# TAU_PRE = plasticity_model['TAU_PRE']
# TAU_POST = plasticity_model['TAU_POST']
# DW_FORW = plasticity_model['DW_FORW']
# DW_BACK = plasticity_model['DW_BACK']
# DV_SPIKE = plasticity_model['DV_SPIKE']
# REL_W_MIN = plasticity_model['REL_W_MIN']
# REL_W_MAX = plasticity_model['REL_W_MAX']
# Two auxiliary variables track decaying trace of
# presynaptic and postsynaptic spikes
syn_eq = '''
dzpre/dt = -zpre/TAU_PRE : 1 (event-driven)
dzpost/dt = -zpost/TAU_POST : 1 (event-driven)
'''
# In case of homeostatic synaptic plasticity, the weight
# also decays to baseline value over time
if 'HP' in plasticity_model['TYPE']:
syn_eq += 'dw/dt = (REL_W_0 - w)/TAU_HP : 1 (event-driven)' + '\n'
else:
syn_eq += 'w : 1' + '\n'
# On spike increase decaying variable by fixed amount
# Increase weight by the value of the decaying variable
# from the other side of the synapse
# Truncate weight if it exceeds maximum
syn_pre_eq = '''
zpre += 1
w = clip(w + DW_FORW * zpost, REL_W_MIN, REL_W_MAX)
v_post += DV_SPIKE * w
'''
syn_post_eq = '''
zpost += 1
w = clip(w + DW_BACK * zpre, REL_W_MIN, REL_W_MAX)
'''
return Synapses(G1, G1, syn_eq, on_pre=syn_pre_eq, on_post=syn_post_eq, namespace=plasticity_model)
else:
raise ValueError('Unexpected Plasticity type', plasticity_model['TYPE'])
def synapse_delays(syn_delay: ms, syn_delay_windowsize: ms, Syn: Synapses,
shape):
"""
Configure pre-synaptic delays. Similar to Brunel 2000 these are uniformly distributed.
The delay is the center of the uniform distribution, while the window size defines the
total width of the distribution.
Related parameters:
- :data:`net.standard_params.synEE_delay`
- :data:`net.standard_params.synEE_delay_windowsize`
- :data:`net.standard_params.synEI_delay`
- :data:`net.standard_params.synEI_delay_windowsize`
"""
# need to create these for all synapses, not only the initially active ones
delays = np.random.uniform(low=syn_delay - syn_delay_windowsize / 2,
high=syn_delay + syn_delay_windowsize / 2,
size=shape)
Syn.delay = delays
return delays
def test_store_restore():
source = NeuronGroup(10,
'''dv/dt = rates : 1
rates : Hz''',
threshold='v>1',
reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', on_pre='v+=w')
synapses.connect(j='i')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
net = Network(source, target, synapses, state_mon, spike_mon)
net.store() # default time slot
net.run(10 * ms)
net.store('second')
net.run(10 * ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
net.restore() # Go back to beginning
assert defaultclock.t == 0 * ms
assert net.t == 0 * ms
net.run(20 * ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
net.restore('second')
assert defaultclock.t == 10 * ms
assert net.t == 10 * ms
net.run(10 * ms)
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back again (see github issue #681)
net.restore('second')
assert defaultclock.t == 10 * ms
assert net.t == 10 * ms
def sim_decision_making_network(N_Excit=384, N_Inhib=96, weight_scaling_factor=5.33,
t_stimulus_start=100 * b2.ms, t_stimulus_duration=9999 * b2.ms, coherence_level=0.,
stimulus_update_interval=30 * b2.ms, mu0_mean_stimulus_Hz=160.,
stimulus_std_Hz=20.,
N_extern=1000, firing_rate_extern=9.8 * b2.Hz,
w_pos=1.90, f_Subpop_size=0.25, # .15 in publication [1]
max_sim_time=1000. * b2.ms, stop_condition_rate=None,
monitored_subset_size=512):
"""
Args:
N_Excit (int): total number of neurons in the excitatory population
N_Inhib (int): nr of neurons in the inhibitory populations
weight_scaling_factor: When increasing the number of neurons by 2, the weights should be scaled down by 1/2
t_stimulus_start (Quantity): time when the stimulation starts
t_stimulus_duration (Quantity): duration of the stimulation
coherence_level (int): coherence of the stimulus.
Difference in mean between the PoissonGroups "left" stimulus and "right" stimulus
stimulus_update_interval (Quantity): the mean of the stimulating PoissonGroups is
re-sampled at this interval
mu0_mean_stimulus_Hz (float): maximum mean firing rate of the stimulus if c=+1 or c=-1. Each neuron
in the populations "Left" and "Right" receives an independent poisson input.
stimulus_std_Hz (float): std deviation of the stimulating PoissonGroups.
N_extern (int): nr of neurons in the stimulus independent poisson background population
firing_rate_extern (int): firing rate of the stimulus independent poisson background population
w_pos (float): Scaling (strengthening) of the recurrent weights within the
subpopulations "Left" and "Right"
f_Subpop_size (float): fraction of the neurons in the subpopulations "Left" and "Right".
#left = #right = int(f_Subpop_size*N_Excit).
max_sim_time (Quantity): simulated time.
stop_condition_rate (Quantity): An optional stopping criteria: If not None, the simulation stops if the
firing rate of either subpopulation "Left" or "Right" is above stop_condition_rate.
monitored_subset_size (int): max nr of neurons for which a state monitor is registered.
Returns:
A dictionary with the following keys (strings):
"rate_monitor_A", "spike_monitor_A", "voltage_monitor_A", "idx_monitored_neurons_A", "rate_monitor_B",
"spike_monitor_B", "voltage_monitor_B", "idx_monitored_neurons_B", "rate_monitor_Z", "spike_monitor_Z",
"voltage_monitor_Z", "idx_monitored_neurons_Z", "rate_monitor_inhib", "spike_monitor_inhib",
"voltage_monitor_inhib", "idx_monitored_neurons_inhib"
"""
print("simulating {} neurons. Start: {}".format(N_Excit + N_Inhib, time.ctime()))
t_stimulus_end = t_stimulus_start + t_stimulus_duration
N_Group_A = int(N_Excit * f_Subpop_size) # size of the excitatory subpopulation sensitive to stimulus A
N_Group_B = N_Group_A # size of the excitatory subpopulation sensitive to stimulus B
N_Group_Z = N_Excit - N_Group_A - N_Group_B # (1-2f)Ne excitatory neurons do not respond to either stimulus.
Cm_excit = 0.5 * b2.nF # membrane capacitance of excitatory neurons
G_leak_excit = 25.0 * b2.nS # leak conductance
E_leak_excit = -70.0 * b2.mV # reversal potential
v_spike_thr_excit = -50.0 * b2.mV # spike condition
v_reset_excit = -60.0 * b2.mV # reset voltage after spike
t_abs_refract_excit = 2. * b2.ms # absolute refractory period
# specify the inhibitory interneurons:
# N_Inhib = 200
Cm_inhib = 0.2 * b2.nF
G_leak_inhib = 20.0 * b2.nS
E_leak_inhib = -70.0 * b2.mV
v_spike_thr_inhib = -50.0 * b2.mV
v_reset_inhib = -60.0 * b2.mV
t_abs_refract_inhib = 1.0 * b2.ms
# specify the AMPA synapses
E_AMPA = 0.0 * b2.mV
tau_AMPA = 2.5 * b2.ms
# specify the GABA synapses
E_GABA = -70.0 * b2.mV
tau_GABA = 5.0 * b2.ms
# specify the NMDA synapses
E_NMDA = 0.0 * b2.mV
tau_NMDA_s = 100.0 * b2.ms
tau_NMDA_x = 2. * b2.ms
alpha_NMDA = 0.5 * b2.kHz
# projections from the external population
g_AMPA_extern2inhib = 1.62 * b2.nS
g_AMPA_extern2excit = 2.1 * b2.nS
# projectsions from the inhibitory populations
g_GABA_inhib2inhib = weight_scaling_factor * 1.25 * b2.nS
g_GABA_inhib2excit = weight_scaling_factor * 1.60 * b2.nS
# projections from the excitatory population
g_AMPA_excit2excit = weight_scaling_factor * 0.012 * b2.nS
g_AMPA_excit2inhib = weight_scaling_factor * 0.015 * b2.nS
g_NMDA_excit2excit = weight_scaling_factor * 0.040 * b2.nS
g_NMDA_excit2inhib = weight_scaling_factor * 0.045 * b2.nS # stronger projection to inhib.
# weights and "adjusted" weights.
w_neg = 1. - f_Subpop_size * (w_pos - 1.) / (1. - f_Subpop_size)
# We use the same postsyn AMPA and NMDA conductances. Adjust the weights coming from different sources:
w_ext2inhib = g_AMPA_extern2inhib / g_AMPA_excit2inhib
w_ext2excit = g_AMPA_extern2excit / g_AMPA_excit2excit
# other weights are 1
# print("w_neg={}, w_ext2inhib={}, w_ext2excit={}".format(w_neg, w_ext2inhib, w_ext2excit))
# Define the inhibitory population
# dynamics:
inhib_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_inhib * (v-E_leak_inhib)
- g_AMPA_excit2inhib * s_AMPA * (v-E_AMPA)
- g_GABA_inhib2inhib * s_GABA * (v-E_GABA)
- g_NMDA_excit2inhib * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_inhib : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
"""
inhib_pop = NeuronGroup(
N_Inhib, model=inhib_lif_dynamics,
threshold="v>v_spike_thr_inhib", reset="v=v_reset_inhib", refractory=t_abs_refract_inhib,
method="rk2")
# initialize with random voltages:
inhib_pop.v = rnd.uniform(v_spike_thr_inhib / b2.mV - 4., high=v_spike_thr_inhib / b2.mV - 1., size=N_Inhib) * b2.mV
# Specify the excitatory population:
# dynamics:
excit_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_excit * (v-E_leak_excit)
- g_AMPA_excit2excit * s_AMPA * (v-E_AMPA)
- g_GABA_inhib2excit * s_GABA * (v-E_GABA)
- g_NMDA_excit2excit * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_excit : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
ds_NMDA/dt = -s_NMDA/tau_NMDA_s + alpha_NMDA * x * (1-s_NMDA) : 1
dx/dt = -x/tau_NMDA_x : 1
"""
# define the three excitatory subpopulations.
# A: subpop receiving stimulus A
excit_pop_A = NeuronGroup(N_Group_A, model=excit_lif_dynamics,
threshold="v>v_spike_thr_excit", reset="v=v_reset_excit",
refractory=t_abs_refract_excit, method="rk2")
excit_pop_A.v = rnd.uniform(E_leak_excit / b2.mV, high=E_leak_excit / b2.mV + 5., size=excit_pop_A.N) * b2.mV
# B: subpop receiving stimulus B
excit_pop_B = NeuronGroup(N_Group_B, model=excit_lif_dynamics, threshold="v>v_spike_thr_excit",
reset="v=v_reset_excit", refractory=t_abs_refract_excit, method="rk2")
excit_pop_B.v = rnd.uniform(E_leak_excit / b2.mV, high=E_leak_excit / b2.mV + 5., size=excit_pop_B.N) * b2.mV
# Z: non-sensitive
excit_pop_Z = NeuronGroup(N_Group_Z, model=excit_lif_dynamics,
threshold="v>v_spike_thr_excit", reset="v=v_reset_excit",
refractory=t_abs_refract_excit, method="rk2")
excit_pop_Z.v = rnd.uniform(v_reset_excit / b2.mV, high=v_spike_thr_excit / b2.mV - 1., size=excit_pop_Z.N) * b2.mV
# now define the connections:
# projections FROM EXTERNAL POISSON GROUP: ####################################################
poisson2Inhib = PoissonInput(target=inhib_pop, target_var="s_AMPA",
N=N_extern, rate=firing_rate_extern, weight=w_ext2inhib)
poisson2A = PoissonInput(target=excit_pop_A, target_var="s_AMPA",
N=N_extern, rate=firing_rate_extern, weight=w_ext2excit)
poisson2B = PoissonInput(target=excit_pop_B, target_var="s_AMPA",
N=N_extern, rate=firing_rate_extern, weight=w_ext2excit)
poisson2Z = PoissonInput(target=excit_pop_Z, target_var="s_AMPA",
N=N_extern, rate=firing_rate_extern, weight=w_ext2excit)
###############################################################################################
# GABA projections FROM INHIBITORY population: ################################################
syn_inhib2inhib = Synapses(inhib_pop, target=inhib_pop, on_pre="s_GABA += 1.0", delay=0.5 * b2.ms)
syn_inhib2inhib.connect(p=1.)
syn_inhib2A = Synapses(inhib_pop, target=excit_pop_A, on_pre="s_GABA += 1.0", delay=0.5 * b2.ms)
syn_inhib2A.connect(p=1.)
syn_inhib2B = Synapses(inhib_pop, target=excit_pop_B, on_pre="s_GABA += 1.0", delay=0.5 * b2.ms)
syn_inhib2B.connect(p=1.)
syn_inhib2Z = Synapses(inhib_pop, target=excit_pop_Z, on_pre="s_GABA += 1.0", delay=0.5 * b2.ms)
syn_inhib2Z.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY A: #########################################################
syn_AMPA_A2A = Synapses(excit_pop_A, target=excit_pop_A, on_pre="s_AMPA += w_pos", delay=0.5 * b2.ms)
syn_AMPA_A2A.connect(p=1.)
syn_AMPA_A2B = Synapses(excit_pop_A, target=excit_pop_B, on_pre="s_AMPA += w_neg", delay=0.5 * b2.ms)
syn_AMPA_A2B.connect(p=1.)
syn_AMPA_A2Z = Synapses(excit_pop_A, target=excit_pop_Z, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_A2Z.connect(p=1.)
syn_AMPA_A2inhib = Synapses(excit_pop_A, target=inhib_pop, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_A2inhib.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY B: #########################################################
syn_AMPA_B2A = Synapses(excit_pop_B, target=excit_pop_A, on_pre="s_AMPA += w_neg", delay=0.5 * b2.ms)
syn_AMPA_B2A.connect(p=1.)
syn_AMPA_B2B = Synapses(excit_pop_B, target=excit_pop_B, on_pre="s_AMPA += w_pos", delay=0.5 * b2.ms)
syn_AMPA_B2B.connect(p=1.)
syn_AMPA_B2Z = Synapses(excit_pop_B, target=excit_pop_Z, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_B2Z.connect(p=1.)
syn_AMPA_B2inhib = Synapses(excit_pop_B, target=inhib_pop, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_B2inhib.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY Z: #########################################################
syn_AMPA_Z2A = Synapses(excit_pop_Z, target=excit_pop_A, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_Z2A.connect(p=1.)
syn_AMPA_Z2B = Synapses(excit_pop_Z, target=excit_pop_B, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_Z2B.connect(p=1.)
syn_AMPA_Z2Z = Synapses(excit_pop_Z, target=excit_pop_Z, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_Z2Z.connect(p=1.)
syn_AMPA_Z2inhib = Synapses(excit_pop_Z, target=inhib_pop, on_pre="s_AMPA += 1.0", delay=0.5 * b2.ms)
syn_AMPA_Z2inhib.connect(p=1.)
###############################################################################################
# NMDA projections FROM EXCITATORY to INHIB, A,B,Z
@network_operation()
def update_nmda_sum():
sum_sNMDA_A = sum(excit_pop_A.s_NMDA)
sum_sNMDA_B = sum(excit_pop_B.s_NMDA)
sum_sNMDA_Z = sum(excit_pop_Z.s_NMDA)
# note the _ at the end of s_NMDA_total_ disables unit checking
inhib_pop.s_NMDA_total_ = (1.0 * sum_sNMDA_A + 1.0 * sum_sNMDA_B + 1.0 * sum_sNMDA_Z)
excit_pop_A.s_NMDA_total_ = (w_pos * sum_sNMDA_A + w_neg * sum_sNMDA_B + w_neg * sum_sNMDA_Z)
excit_pop_B.s_NMDA_total_ = (w_neg * sum_sNMDA_A + w_pos * sum_sNMDA_B + w_neg * sum_sNMDA_Z)
excit_pop_Z.s_NMDA_total_ = (1.0 * sum_sNMDA_A + 1.0 * sum_sNMDA_B + 1.0 * sum_sNMDA_Z)
# set a self-recurrent synapse to introduce a delay when updating the intermediate
# gating variable x
syn_x_A2A = Synapses(excit_pop_A, excit_pop_A, on_pre="x += 1.", delay=0.5 * b2.ms)
syn_x_A2A.connect(j="i")
syn_x_B2B = Synapses(excit_pop_B, excit_pop_B, on_pre="x += 1.", delay=0.5 * b2.ms)
syn_x_B2B.connect(j="i")
syn_x_Z2Z = Synapses(excit_pop_Z, excit_pop_Z, on_pre="x += 1.", delay=0.5 * b2.ms)
syn_x_Z2Z.connect(j="i")
###############################################################################################
# Define the stimulus: two PoissonInput with time time-dependent mean.
poissonStimulus2A = PoissonGroup(N_Group_A, 0. * b2.Hz)
syn_Stim2A = Synapses(poissonStimulus2A, excit_pop_A, on_pre="s_AMPA+=w_ext2excit")
syn_Stim2A.connect(j="i")
poissonStimulus2B = PoissonGroup(N_Group_B, 0. * b2.Hz)
syn_Stim2B = Synapses(poissonStimulus2B, excit_pop_B, on_pre="s_AMPA+=w_ext2excit")
syn_Stim2B.connect(j="i")
@network_operation(dt=stimulus_update_interval)
def update_poisson_stimulus(t):
if t >= t_stimulus_start and t < t_stimulus_end:
offset_A = mu0_mean_stimulus_Hz * (0.5 + 0.5 * coherence_level)
offset_B = mu0_mean_stimulus_Hz * (0.5 - 0.5 * coherence_level)
rate_A = numpy.random.normal(offset_A, stimulus_std_Hz)
rate_A = (max(0, rate_A)) * b2.Hz # avoid negative rate
rate_B = numpy.random.normal(offset_B, stimulus_std_Hz)
rate_B = (max(0, rate_B)) * b2.Hz
poissonStimulus2A.rates = rate_A
poissonStimulus2B.rates = rate_B
# print("stim on. rate_A= {}, rate_B = {}".format(rate_A, rate_B))
else:
# print("stim off")
poissonStimulus2A.rates = 0.
poissonStimulus2B.rates = 0.
###############################################################################################
def get_monitors(pop, monitored_subset_size):
"""
Internal helper.
Args:
pop:
monitored_subset_size:
Returns:
"""
monitored_subset_size = min(monitored_subset_size, pop.N)
idx_monitored_neurons = sample(range(pop.N), monitored_subset_size)
rate_monitor = PopulationRateMonitor(pop)
# record parameter: record=idx_monitored_neurons is not supported???
spike_monitor = SpikeMonitor(pop, record=idx_monitored_neurons)
voltage_monitor = StateMonitor(pop, "v", record=idx_monitored_neurons)
return rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons
# collect data of a subset of neurons:
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib = \
get_monitors(inhib_pop, monitored_subset_size)
rate_monitor_A, spike_monitor_A, voltage_monitor_A, idx_monitored_neurons_A = \
get_monitors(excit_pop_A, monitored_subset_size)
rate_monitor_B, spike_monitor_B, voltage_monitor_B, idx_monitored_neurons_B = \
get_monitors(excit_pop_B, monitored_subset_size)
rate_monitor_Z, spike_monitor_Z, voltage_monitor_Z, idx_monitored_neurons_Z = \
get_monitors(excit_pop_Z, monitored_subset_size)
if stop_condition_rate is None:
b2.run(max_sim_time)
else:
sim_sum = 0. * b2.ms
sim_batch = 100. * b2.ms
samples_in_batch = int(floor(sim_batch / b2.defaultclock.dt))
avg_rate_in_batch = 0
while (sim_sum < max_sim_time) and (avg_rate_in_batch < stop_condition_rate):
b2.run(sim_batch)
avg_A = numpy.mean(rate_monitor_A.rate[-samples_in_batch:])
avg_B = numpy.mean(rate_monitor_B.rate[-samples_in_batch:])
avg_rate_in_batch = max(avg_A, avg_B)
sim_sum += sim_batch
print("sim end: {}".format(time.ctime()))
ret_vals = dict()
ret_vals["rate_monitor_A"] = rate_monitor_A
ret_vals["spike_monitor_A"] = spike_monitor_A
ret_vals["voltage_monitor_A"] = voltage_monitor_A
ret_vals["idx_monitored_neurons_A"] = idx_monitored_neurons_A
ret_vals["rate_monitor_B"] = rate_monitor_B
ret_vals["spike_monitor_B"] = spike_monitor_B
ret_vals["voltage_monitor_B"] = voltage_monitor_B
ret_vals["idx_monitored_neurons_B"] = idx_monitored_neurons_B
ret_vals["rate_monitor_Z"] = rate_monitor_Z
ret_vals["spike_monitor_Z"] = spike_monitor_Z
ret_vals["voltage_monitor_Z"] = voltage_monitor_Z
ret_vals["idx_monitored_neurons_Z"] = idx_monitored_neurons_Z
ret_vals["rate_monitor_inhib"] = rate_monitor_inhib
ret_vals["spike_monitor_inhib"] = spike_monitor_inhib
ret_vals["voltage_monitor_inhib"] = voltage_monitor_inhib
ret_vals["idx_monitored_neurons_inhib"] = idx_monitored_neurons_inhib
return ret_vals
def simulate_brunel_network(
N_Excit=5000,
N_Inhib=None,
N_extern=N_POISSON_INPUT,
connection_probability=CONNECTION_PROBABILITY_EPSILON,
w0=SYNAPTIC_WEIGHT_W0,
g=RELATIVE_INHIBITORY_STRENGTH_G,
synaptic_delay=SYNAPTIC_DELAY,
poisson_input_rate=POISSON_INPUT_RATE,
w_external=None,
v_rest=V_REST,
v_reset=V_RESET,
firing_threshold=FIRING_THRESHOLD,
membrane_time_scale=MEMBRANE_TIME_SCALE,
abs_refractory_period=ABSOLUTE_REFRACTORY_PERIOD,
monitored_subset_size=100,
random_vm_init=False,
sim_time=100.*b2.ms):
"""
Fully parametrized implementation of a sparsely connected network of LIF neurons (Brunel 2000)
Args:
N_Excit (int): Size of the excitatory popluation
N_Inhib (int): optional. Size of the inhibitory population.
If not set (=None), N_Inhib is set to N_excit/4.
N_extern (int): optional. Number of presynaptic excitatory poisson neurons. Note: if set to a value,
this number does NOT depend on N_Excit and NOT depend on connection_probability (this is different
from the book and paper. Only if N_extern is set to 'None', then N_extern is computed as
N_Excit*connection_probability.
connection_probability (float): probability to connect to any of the (N_Excit+N_Inhib) neurons
CE = connection_probability*N_Excit
CI = connection_probability*N_Inhib
Cexternal = N_extern
w0 (float): Synaptic strength J
g (float): relative importance of inhibition. J_exc = w0. J_inhib = -g*w0
synaptic_delay (Quantity): Delay between presynaptic spike and postsynaptic increase of v_m
poisson_input_rate (Quantity): Poisson rate of the external population
w_external (float): optional. Synaptic weight of the excitatory external poisson neurons onto all
neurons in the network. Default is None, in that case w_external is set to w0, which is the
standard value in the book and in the paper Brunel2000.
The purpose of this parameter is to see the effect of external input in the
absence of network feedback(setting w0 to 0mV and w_external>0).
v_rest (Quantity): Resting potential
v_reset (Quantity): Reset potential
firing_threshold (Quantity): Spike threshold
membrane_time_scale (Quantity): tau_m
abs_refractory_period (Quantity): absolute refractory period, tau_ref
monitored_subset_size (int): nr of neurons for which a VoltageMonitor is recording Vm
random_vm_init (bool): if true, the membrane voltage of each neuron is initialized with a
random value drawn from Uniform(v_rest, firing_threshold)
sim_time (Quantity): Simulation time
Returns:
(rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons)
PopulationRateMonitor: Rate Monitor
SpikeMonitor: SpikeMonitor for ALL (N_Excit+N_Inhib) neurons
StateMonitor: membrane voltage for a selected subset of neurons
list: index of monitored neurons. length = monitored_subset_size
"""
if N_Inhib is None:
N_Inhib = int(N_Excit/4)
if N_extern is None:
N_extern = int(N_Excit*connection_probability)
if w_external is None:
w_external = w0
J_excit = w0
J_inhib = -g*w0
lif_dynamics = """
dv/dt = -(v-v_rest) / membrane_time_scale : volt (unless refractory)"""
network = NeuronGroup(
N_Excit+N_Inhib, model=lif_dynamics,
threshold="v>firing_threshold", reset="v=v_reset", refractory=abs_refractory_period,
method="linear")
if random_vm_init:
network.v = random.uniform(v_rest/b2.mV, high=firing_threshold/b2.mV, size=(N_Excit+N_Inhib))*b2.mV
else:
network.v = v_rest
excitatory_population = network[:N_Excit]
inhibitory_population = network[N_Excit:]
exc_synapses = Synapses(excitatory_population, target=network, on_pre="v += J_excit", delay=synaptic_delay)
exc_synapses.connect(p=connection_probability)
inhib_synapses = Synapses(inhibitory_population, target=network, on_pre="v += J_inhib", delay=synaptic_delay)
inhib_synapses.connect(p=connection_probability)
external_poisson_input = PoissonInput(target=network, target_var="v", N=N_extern,
rate=poisson_input_rate, weight=w_external)
# collect data of a subset of neurons:
monitored_subset_size = min(monitored_subset_size, (N_Excit+N_Inhib))
idx_monitored_neurons = sample(range(N_Excit+N_Inhib), monitored_subset_size)
rate_monitor = PopulationRateMonitor(network)
# record= some_list is not supported? :-(
spike_monitor = SpikeMonitor(network, record=idx_monitored_neurons)
voltage_monitor = StateMonitor(network, "v", record=idx_monitored_neurons)
b2.run(sim_time)
return rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons
def run_net(tr):
# prefs.codegen.target = 'numpy'
# prefs.codegen.target = 'cython'
set_device('cpp_standalone',
directory='./builds/%.4d' % (tr.v_idx),
build_on_run=False)
print("Started process with id ", str(tr.v_idx))
namespace = tr.netw.f_to_dict(short_names=True, fast_access=True)
namespace['idx'] = tr.v_idx
defaultclock.dt = tr.netw.sim.dt
GExc = NeuronGroup(
N=tr.N_e,
model=tr.condlif_sig,
threshold=tr.nrnEE_thrshld,
reset=tr.nrnEE_reset, #method=tr.neuron_method,
namespace=namespace)
GInh = NeuronGroup(
N=tr.N_i,
model=tr.condlif_sig,
threshold='V > Vt',
reset='V=Vr_i', #method=tr.neuron_method,
namespace=namespace)
# set initial thresholds fixed, init. potentials uniformly distrib.
GExc.sigma, GInh.sigma = tr.sigma_e, tr.sigma_i
GExc.Vt, GInh.Vt = tr.Vt_e, tr.Vt_i
GExc.V , GInh.V = np.random.uniform(tr.Vr_e/mV, tr.Vt_e/mV,
size=tr.N_e)*mV, \
np.random.uniform(tr.Vr_i/mV, tr.Vt_i/mV,
size=tr.N_i)*mV
synEE_pre_mod = mod.synEE_pre
synEE_post_mod = mod.synEE_post
if tr.stdp_active:
synEE_pre_mod = '''%s
%s''' % (synEE_pre_mod, mod.synEE_pre_STDP)
synEE_post_mod = '''%s
%s''' % (synEE_post_mod, mod.synEE_post_STDP)
if tr.synEE_rec:
synEE_pre_mod = '''%s
%s''' % (synEE_pre_mod, mod.synEE_pre_rec)
synEE_post_mod = '''%s
%s''' % (synEE_post_mod, mod.synEE_post_rec)
# E<-E advanced synapse model, rest simple
SynEE = Synapses(
target=GExc,
source=GExc,
model=tr.synEE_mod,
on_pre=synEE_pre_mod,
on_post=synEE_post_mod,
#method=tr.synEE_method,
namespace=namespace)
SynIE = Synapses(target=GInh,
source=GExc,
on_pre='ge_post += a_ie',
namespace=namespace)
SynEI = Synapses(target=GExc,
source=GInh,
on_pre='gi_post += a_ei',
namespace=namespace)
SynII = Synapses(target=GInh,
source=GInh,
on_pre='gi_post += a_ii',
namespace=namespace)
if tr.strct_active:
sEE_src, sEE_tar = generate_full_connectivity(tr.N_e, same=True)
SynEE.connect(i=sEE_src, j=sEE_tar)
SynEE.syn_active = 0
else:
srcs_full, tars_full = generate_full_connectivity(tr.N_e, same=True)
SynEE.connect(i=srcs_full, j=tars_full)
SynEE.syn_active = 0
sIE_src, sIE_tar = generate_connections(tr.N_i, tr.N_e, tr.p_ie)
sEI_src, sEI_tar = generate_connections(tr.N_e, tr.N_i, tr.p_ei)
sII_src, sII_tar = generate_connections(tr.N_i, tr.N_i, tr.p_ii, same=True)
SynIE.connect(i=sIE_src, j=sIE_tar)
SynEI.connect(i=sEI_src, j=sEI_tar)
SynII.connect(i=sII_src, j=sII_tar)
tr.f_add_result('sIE_src', sIE_src)
tr.f_add_result('sIE_tar', sIE_tar)
tr.f_add_result('sEI_src', sEI_src)
tr.f_add_result('sEI_tar', sEI_tar)
tr.f_add_result('sII_src', sII_src)
tr.f_add_result('sII_tar', sII_tar)
# if tr.strct_active:
# SynEE.a = 0
# else:
SynEE.a = tr.a_ee
SynEE.insert_P = tr.insert_P
# make synapse active at beginning
#if not tr.strct_active:
SynEE.run_regularly(tr.synEE_p_activate, dt=tr.T, when='start', order=-100)
# synaptic scaling
if tr.netw.config.scl_active:
SynEE.summed_updaters['Asum_post']._clock = Clock(
dt=tr.dt_synEE_scaling)
SynEE.run_regularly(tr.synEE_scaling,
dt=tr.dt_synEE_scaling,
when='end')
# intrinsic plasticity
if tr.netw.config.it_active:
GExc.h_ip = tr.h_ip
GExc.run_regularly(tr.intrinsic_mod, dt=tr.it_dt, when='end')
# structural plasticity
if tr.netw.config.strct_active:
SynEE.run_regularly(tr.strct_mod, dt=tr.strct_dt, when='end')
# -------------- recording ------------------
#run(tr.sim.preT)
GExc_recvars = []
if tr.memtraces_rec:
GExc_recvars.append('V')
if tr.vttraces_rec:
GExc_recvars.append('Vt')
if tr.getraces_rec:
GExc_recvars.append('ge')
if tr.gitraces_rec:
GExc_recvars.append('gi')
GInh_recvars = GExc_recvars
GExc_stat = StateMonitor(GExc,
GExc_recvars,
record=[0, 1, 2],
dt=tr.GExc_stat_dt)
GInh_stat = StateMonitor(GInh,
GInh_recvars,
record=[0, 1, 2],
dt=tr.GInh_stat_dt)
SynEE_recvars = []
if tr.synee_atraces_rec:
SynEE_recvars.append('a')
if tr.synee_Apretraces_rec:
SynEE_recvars.append('Apre')
if tr.synee_Aposttraces_rec:
SynEE_recvars.append('Apost')
SynEE_stat = StateMonitor(SynEE,
SynEE_recvars,
record=range(tr.n_synee_traces_rec),
when='end',
dt=tr.synEE_stat_dt)
GExc_spks = SpikeMonitor(GExc)
GInh_spks = SpikeMonitor(GInh)
SynEE_a = StateMonitor(SynEE, ['a', 'syn_active'],
record=range(tr.N_e * (tr.N_e - 1)),
dt=tr.sim.T / 10.,
when='end',
order=100)
run(tr.sim.T, report='text')
SynEE_a.record_single_timestep()
device.build(directory='../builds/%.4d' % (tr.v_idx))
tr.v_standard_result = Brian2MonitorResult
tr.f_add_result('GExc_stat', GExc_stat)
tr.f_add_result('SynEE_stat', SynEE_stat)
print("Saving exc spikes... ", GExc_spks.get_states()['N'])
tr.f_add_result('GExc_spks', GExc_spks)
tr.f_add_result('GInh_stat', GInh_stat)
print("Saving inh spikes... ", GInh_spks.get_states()['N'])
tr.f_add_result('GInh_spks', GInh_spks)
tr.f_add_result('SynEE_a', SynEE_a)
# ----------------- add raw data ------------------------
fpath = '../builds/%.4d/' % (tr.v_idx)
from pathlib import Path
Path(fpath + 'turnover').touch()
turnover_data = np.genfromtxt(fpath + 'turnover', delimiter=',')
tr.f_add_result('turnover', turnover_data)
os.remove(fpath + 'turnover')
Path(fpath + 'spk_register').touch()
spk_register_data = np.genfromtxt(fpath + 'spk_register', delimiter=',')
tr.f_add_result('spk_register', spk_register_data)
os.remove(fpath + 'spk_register')
def run_net(tr):
# prefs.codegen.target = 'numpy'
# prefs.codegen.target = 'cython'
set_device('cpp_standalone',
directory='./builds/%.4d' % (tr.v_idx),
build_on_run=False)
print("Started process with id ", str(tr.v_idx))
T = tr.T1 + tr.T2 + tr.T3
namespace = tr.netw.f_to_dict(short_names=True, fast_access=True)
namespace['idx'] = tr.v_idx
defaultclock.dt = tr.netw.sim.dt
GExc = NeuronGroup(
N=tr.N_e,
model=tr.condlif_sig,
threshold=tr.nrnEE_thrshld,
reset=tr.nrnEE_reset, #method=tr.neuron_method,
namespace=namespace)
GInh = NeuronGroup(
N=tr.N_i,
model=tr.condlif_sig,
threshold='V > Vt',
reset='V=Vr_i', #method=tr.neuron_method,
namespace=namespace)
# set initial thresholds fixed, init. potentials uniformly distrib.
GExc.sigma, GInh.sigma = tr.sigma_e, tr.sigma_i
GExc.Vt, GInh.Vt = tr.Vt_e, tr.Vt_i
GExc.V , GInh.V = np.random.uniform(tr.Vr_e/mV, tr.Vt_e/mV,
size=tr.N_e)*mV, \
np.random.uniform(tr.Vr_i/mV, tr.Vt_i/mV,
size=tr.N_i)*mV
print("need to fix?")
synEE_pre_mod = mod.synEE_pre
synEE_post_mod = mod.synEE_post
if tr.PInp_mode == 'pool':
PInp = PoissonGroup(tr.NPInp, rates=tr.PInp_rate, namespace=namespace)
sPN = Synapses(target=GExc,
source=PInp,
model=tr.poisson_mod,
on_pre='ge_post += a_EPoi',
namespace=namespace)
sPN_src, sPN_tar = generate_connections(N_tar=tr.N_e,
N_src=tr.NPInp,
p=tr.p_EPoi)
elif tr.PInp_mode == 'indep':
PInp = PoissonGroup(tr.N_e, rates=tr.PInp_rate, namespace=namespace)
sPN = Synapses(target=GExc,
source=PInp,
model=tr.poisson_mod,
on_pre='ge_post += a_EPoi',
namespace=namespace)
sPN_src, sPN_tar = range(tr.N_e), range(tr.N_e)
sPN.connect(i=sPN_src, j=sPN_tar)
if tr.PInp_mode == 'pool':
sPNInh = Synapses(target=GInh,
source=PInp,
model=tr.poisson_mod,
on_pre='ge_post += a_EPoi',
namespace=namespace)
sPNInh_src, sPNInh_tar = generate_connections(N_tar=tr.N_i,
N_src=tr.NPInp,
p=tr.p_EPoi)
elif tr.PInp_mode == 'indep':
PInp_inh = PoissonGroup(tr.N_i,
rates=tr.PInp_rate,
namespace=namespace)
sPNInh = Synapses(target=GInh,
source=PInp_inh,
model=tr.poisson_mod,
on_pre='ge_post += a_EPoi',
namespace=namespace)
sPNInh_src, sPNInh_tar = range(tr.N_i), range(tr.N_i)
sPNInh.connect(i=sPNInh_src, j=sPNInh_tar)
if tr.stdp_active:
synEE_pre_mod = '''%s
%s''' % (synEE_pre_mod, mod.synEE_pre_STDP)
synEE_post_mod = '''%s
%s''' % (synEE_post_mod, mod.synEE_post_STDP)
if tr.synEE_rec:
synEE_pre_mod = '''%s
%s''' % (synEE_pre_mod, mod.synEE_pre_rec)
synEE_post_mod = '''%s
%s''' % (synEE_post_mod, mod.synEE_post_rec)
# E<-E advanced synapse model, rest simple
SynEE = Synapses(
target=GExc,
source=GExc,
model=tr.synEE_mod,
on_pre=synEE_pre_mod,
on_post=synEE_post_mod,
#method=tr.synEE_method,
namespace=namespace)
SynIE = Synapses(target=GInh,
source=GExc,
on_pre='ge_post += a_ie',
namespace=namespace)
SynEI = Synapses(target=GExc,
source=GInh,
on_pre='gi_post += a_ei',
namespace=namespace)
SynII = Synapses(target=GInh,
source=GInh,
on_pre='gi_post += a_ii',
namespace=namespace)
if tr.strct_active:
sEE_src, sEE_tar = generate_full_connectivity(tr.N_e, same=True)
SynEE.connect(i=sEE_src, j=sEE_tar)
SynEE.syn_active = 0
else:
srcs_full, tars_full = generate_full_connectivity(tr.N_e, same=True)
SynEE.connect(i=srcs_full, j=tars_full)
SynEE.syn_active = 0
sIE_src, sIE_tar = generate_connections(tr.N_i, tr.N_e, tr.p_ie)
sEI_src, sEI_tar = generate_connections(tr.N_e, tr.N_i, tr.p_ei)
sII_src, sII_tar = generate_connections(tr.N_i, tr.N_i, tr.p_ii, same=True)
SynIE.connect(i=sIE_src, j=sIE_tar)
SynEI.connect(i=sEI_src, j=sEI_tar)
SynII.connect(i=sII_src, j=sII_tar)
tr.f_add_result('sIE_src', sIE_src)
tr.f_add_result('sIE_tar', sIE_tar)
tr.f_add_result('sEI_src', sEI_src)
tr.f_add_result('sEI_tar', sEI_tar)
tr.f_add_result('sII_src', sII_src)
tr.f_add_result('sII_tar', sII_tar)
SynEE.a = tr.a_ee
SynEE.insert_P = tr.insert_P
SynEE.p_inactivate = tr.p_inactivate
# make synapse active at beginning
SynEE.run_regularly(tr.synEE_p_activate, dt=T, when='start', order=-100)
# synaptic scaling
if tr.netw.config.scl_active:
SynEE.summed_updaters['Asum_post']._clock = Clock(
dt=tr.dt_synEE_scaling)
SynEE.run_regularly(tr.synEE_scaling,
dt=tr.dt_synEE_scaling,
when='end')
# intrinsic plasticity
if tr.netw.config.it_active:
GExc.h_ip = tr.h_ip
GExc.run_regularly(tr.intrinsic_mod, dt=tr.it_dt, when='end')
# structural plasticity
if tr.netw.config.strct_active:
if tr.strct_mode == 'zero':
if tr.turnover_rec:
strct_mod = '''%s
%s''' % (tr.strct_mod, tr.turnover_rec_mod)
else:
strct_mod = tr.strct_mod
SynEE.run_regularly(strct_mod, dt=tr.strct_dt, when='end')
elif tr.strct_mode == 'thrs':
if tr.turnover_rec:
strct_mod_thrs = '''%s
%s''' % (tr.strct_mod_thrs,
tr.turnover_rec_mod)
else:
strct_mod_thrs = tr.strct_mod_thrs
SynEE.run_regularly(strct_mod_thrs, dt=tr.strct_dt, when='end')
# -------------- recording ------------------
#run(tr.sim.preT)
GExc_recvars = []
if tr.memtraces_rec:
GExc_recvars.append('V')
if tr.vttraces_rec:
GExc_recvars.append('Vt')
if tr.getraces_rec:
GExc_recvars.append('ge')
if tr.gitraces_rec:
GExc_recvars.append('gi')
GInh_recvars = GExc_recvars
GExc_stat = StateMonitor(GExc,
GExc_recvars,
record=[0, 1, 2],
dt=tr.GExc_stat_dt)
GInh_stat = StateMonitor(GInh,
GInh_recvars,
record=[0, 1, 2],
dt=tr.GInh_stat_dt)
SynEE_recvars = []
if tr.synee_atraces_rec:
SynEE_recvars.append('a')
if tr.synee_Apretraces_rec:
SynEE_recvars.append('Apre')
if tr.synee_Aposttraces_rec:
SynEE_recvars.append('Apost')
SynEE_stat = StateMonitor(SynEE,
SynEE_recvars,
record=range(tr.n_synee_traces_rec),
when='end',
dt=tr.synEE_stat_dt)
GExc_spks = SpikeMonitor(GExc)
GInh_spks = SpikeMonitor(GInh)
PInp_spks = SpikeMonitor(PInp)
GExc_rate = PopulationRateMonitor(GExc)
GInh_rate = PopulationRateMonitor(GInh)
PInp_rate = PopulationRateMonitor(PInp)
SynEE_a = StateMonitor(SynEE, ['a', 'syn_active'],
record=range(tr.N_e * (tr.N_e - 1)),
dt=T / tr.synee_a_nrecpoints,
when='end',
order=100)
if tr.PInp_mode == 'indep':
net = Network(GExc, GInh, PInp, sPN, sPNInh, SynEE, SynEI, SynIE,
SynII, GExc_stat, GInh_stat, SynEE_stat, SynEE_a,
GExc_spks, GInh_spks, PInp_spks, GExc_rate, GInh_rate,
PInp_rate, PInp_inh)
else:
net = Network(GExc, GInh, PInp, sPN, sPNInh, SynEE, SynEI, SynIE,
SynII, GExc_stat, GInh_stat, SynEE_stat, SynEE_a,
GExc_spks, GInh_spks, PInp_spks, GExc_rate, GInh_rate,
PInp_rate)
net.run(tr.sim.T1, report='text')
# SynEE_a.record_single_timestep()
recorders = [
GExc_spks, GInh_spks, PInp_spks, SynEE_stat, GExc_stat, GInh_stat
]
rate_recorders = [GExc_rate, GInh_rate, PInp_rate]
for rcc in recorders:
rcc.active = False
net.run(tr.sim.T2, report='text')
recorders = [
SynEE_stat, GExc_stat, GInh_stat, GExc_rate, GInh_rate, PInp_rate
]
for rcc in recorders:
rcc.active = True
if tr.spks_rec:
GExc_spks.active = True
GInh_spks.active = True
# PInp_spks.active=True
net.run(tr.sim.T3, report='text')
device.build(directory='../builds/%.4d' % (tr.v_idx), clean=True)
# save monitors as raws in build directory
raw_dir = '../builds/%.4d/raw/' % (tr.v_idx)
if not os.path.exists(raw_dir):
os.makedirs(raw_dir)
with open(raw_dir + 'namespace.p', 'wb') as pfile:
pickle.dump(namespace, pfile)
with open(raw_dir + 'gexc_stat.p', 'wb') as pfile:
pickle.dump(GExc_stat.get_states(), pfile)
with open(raw_dir + 'ginh_stat.p', 'wb') as pfile:
pickle.dump(GInh_stat.get_states(), pfile)
with open(raw_dir + 'synee_stat.p', 'wb') as pfile:
pickle.dump(SynEE_stat.get_states(), pfile)
with open(raw_dir + 'synee_a.p', 'wb') as pfile:
pickle.dump(SynEE_a.get_states(), pfile)
with open(raw_dir + 'gexc_spks.p', 'wb') as pfile:
pickle.dump(GExc_spks.get_states(), pfile)
with open(raw_dir + 'ginh_spks.p', 'wb') as pfile:
pickle.dump(GInh_spks.get_states(), pfile)
with open(raw_dir + 'pinp_spks.p', 'wb') as pfile:
pickle.dump(PInp_spks.get_states(), pfile)
with open(raw_dir + 'gexc_rate.p', 'wb') as pfile:
pickle.dump(GExc_rate.get_states(), pfile)
pickle.dump(GExc_rate.smooth_rate(width=25 * ms), pfile)
with open(raw_dir + 'ginh_rate.p', 'wb') as pfile:
pickle.dump(GInh_rate.get_states(), pfile)
pickle.dump(GInh_rate.smooth_rate(width=25 * ms), pfile)
with open(raw_dir + 'pinp_rate.p', 'wb') as pfile:
pickle.dump(PInp_rate.get_states(), pfile)
pickle.dump(PInp_rate.smooth_rate(width=25 * ms), pfile)
# ----------------- add raw data ------------------------
fpath = '../builds/%.4d/' % (tr.v_idx)
from pathlib import Path
Path(fpath + 'turnover').touch()
turnover_data = np.genfromtxt(fpath + 'turnover', delimiter=',')
os.remove(fpath + 'turnover')
with open(raw_dir + 'turnover.p', 'wb') as pfile:
pickle.dump(turnover_data, pfile)
Path(fpath + 'spk_register').touch()
spk_register_data = np.genfromtxt(fpath + 'spk_register', delimiter=',')
os.remove(fpath + 'spk_register')
with open(raw_dir + 'spk_register.p', 'wb') as pfile:
pickle.dump(spk_register_data, pfile)
def brianInteraction(self):
#/########################/#
# Postlet level
#
#debug
self.debug(
[
'It is a Synapser level, we set the Synapser',
('self.',self,[
#'BrianingSynapsesDict'
]
)
]
)
#/####################/#
# Set the parent
#
#Check
if self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable.BrianedParentSingularStr=='Interactome':
#debug
'''
self.debug(
[
'We are in a projectome structure'
]
)
'''
#set
self.BrianedParentInteractomeDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.BrianedParentInteractomeDeriveBrianerVariable.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
else:
#debug
'''
self.debug(
[
'There is no projectome structure'
]
)
'''
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the ConnectedTo Variable
#
#debug
self.debug(
[
'Check if we have to get the connected to variable',
('self.',self,['ConnectedToVariable'])
]
)
#Check
if self.ConnectedToVariable==None:
#debug
self.debug(
[
'We setConnection here'
]
)
#setConnection
self.setConnection(
self.ManagementTagStr,
self,
self.BrianedParentNeurongroupDeriveBrianerVariable
)
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#debug
self.debug(
[
'Do we have to make parent-brian the connected variable ?',
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable)
]
)
#Check
if self.ConnectedToVariable.BrianedNeurongroupVariable==None:
#parent brian
self.ConnectedToVariable.parent(
).brian(
)
#set
BrianedNameStr=self.BrianedParentNeurongroupDeriveBrianerVariable.StructureTagStr+'_To_'+self.ConnectedToVariable.StructureTagStr
#debug
self.debug(
[
'We set the synapses',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable),
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable),
'Maybe we have to make brian the post',
'BrianedNameStr is '+BrianedNameStr
]
)
#import
from brian2 import Synapses
#init
self.BrianedSynapsesVariable=Synapses(
source=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
target=self.ConnectedToVariable.BrianedNeurongroupVariable,
name=BrianedNameStr.replace('/','_'),
**self.BrianingSynapsesDict
)
#/####################/#
# Connect options
#
#connect
if type(self.BrianingConnectVariable)==float:
#debug
self.debug(
[
'we connect with a sparsity of ',
('self.',self,[
'BrianingConnectVariable'
])
]
)
#connect
self.BrianedSynapsesVariable.connect(
True,
p=self.BrianingConnectVariable
)
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedSynapsesVariable
)
class SynapserClass(BaseClass):
def default_init(
self,
_SynapsingBrianKwargDict=None,
_SynapsingProbabilityVariable=None,
_SynapsingTagStr="",
_SynapsingWeigthSymbolStr="",
_SynapsingWeigthFloatsArray=None,
_SynapsingDelayDict=0.0,
_SynapsedBrianVariable=None,
_SynapsedWeigthFloatsArray=None,
_SynapsedCustomOperationStr="",
_SynapsedDelayStateStrsList="",
**_KwargVariablesDict
):
# init
self.PostModelInsertStrsList = []
self.PostModelAddDict = {}
# Call the parent __init__ method
BaseClass.__init__(self, **_KwargVariablesDict)
def setDelay(self, _SymbolStr, _DelayVariable):
# debug
"""
self.debug(
[
"self.SynapsingBrianKwargDict['source'].clock is ",
self.SynapsingBrianKwargDict['source'].clock
]
)
"""
# define
if type(_DelayVariable).__name__ == "Quantity":
# divide
DelayEquationsInt = (int)(_DelayVariable / self.SynapsingBrianKwargDict["source"].clock.dt)
else:
# debug
"""
self.debug(
[
"float of dt is ",
float(
self.SynapsingBrianKwargDict['source'].clock.dt
)
]
)
"""
# divide and put that in ms...(rough)
DelayEquationsInt = (int)(_DelayVariable * 0.001 / float(self.SynapsingBrianKwargDict["source"].clock.dt))
# join
self.SynapsedCustomOperationStr = "\n".join(
map(
lambda __EquationIndexInt: _SymbolStr
+ "_delayer_"
+ str(__EquationIndexInt)
+ "="
+ _SymbolStr
+ "_delayer_"
+ str(__EquationIndexInt - 1),
xrange(DelayEquationsInt, 1, -1),
)
+ [_SymbolStr + "delayer_1=" + _SymbolStr]
)
# debug
self.debug(("self.", self, ["SynapsedCustomOperationStr"]))
# custom
self.SynapsingBrianKwargDict["source"].custom_operation(self.SynapsedCustomOperationStr)
# join
self.SynapsedDelayStateStrsListsList = map(
lambda __EquationIndexInt: _SymbolStr + "_delayer_ : 1", xrange(DelayEquationsInt)
)
# debug
self.debug(("self.", self, ["SynapsedDelayStateStrsList"]))
# add in the PreModelInsertStrsList
if hasattr(self, "AttentionUpdateVariable") == False:
self.AttentionUpdateVariable = {"PreModelInsertStrsList": []}
self.AttentionUpdateVariable["PreModelInsertStrsList"] += self.SynapsedDelayStateStrsListsList
def do_synapse(self):
# Maybe should import
from brian2 import Synapses
# Check
if len(self.SynapsingDelayDict) > 0.0:
# map
map(lambda __ItemTuple: self.setDelay(*__ItemTuple), self.SynapsingDelayDict.items())
# debug
"""
self.debug(('self.',self,[
'SynapsingBrianKwargDict'
]))
"""
# init
self.SynapsedBrianVariable = Synapses(**self.SynapsingBrianKwargDict)
# connect
if type(self.SynapsingProbabilityVariable) == float:
# debug
"""
self.debug('we connect with a sparsity of '+str(self.SynapsingProbabilityVariable))
"""
self.SynapsedBrianVariable.connect(True, p=self.SynapsingProbabilityVariable)
# Check
if self.SynapsingWeigthSymbolStr != "":
# debug
"""
self.debug(
('self.',self,[
'SynapsedBrianVariable',
'SynapsingWeigthSymbolStr'
])
)
"""
# connect
self.SynapsedBrianVariable.connect(True)
# get
self.SynapsedWeigthFloatsArray = getattr(self.SynapsedBrianVariable, self.SynapsingWeigthSymbolStr)
# set
self.SynapsedWeigthFloatsArray[:] = np.reshape(
self.SynapsingWeigthFloatsArray,
self.SynapsedBrianVariable.source.N * self.SynapsedBrianVariable.target.N,
)
# debug
self.debug(("self.", self, ["SynapsedWeigthFloatsArray"]))
def brianInteraction(self):
# /########################/#
# Postlet level
#
# debug
"""
self.debug(
[
'It is an Interaction level',
('self.',self,[
#'BrianingSynapsesDict'
]
)
]
)
"""
# /####################/#
# Set the parent
#
# Check
if self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable.BrianedParentSingularStr == "Interactome":
# debug
"""
self.debug(
[
'We are in a projectome structure'
]
)
"""
# set
self.BrianedParentInteractomeDeriveBrianerVariable = (
self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
# get
self.BrianedParentPopulationDeriveBrianerVariable = (
self.BrianedParentInteractomeDeriveBrianerVariable.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
else:
# debug
"""
self.debug(
[
'There is no projectome structure'
]
)
"""
# get
self.BrianedParentPopulationDeriveBrianerVariable = (
self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
# get
self.BrianedParentNetworkDeriveBrianerVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
)
# /####################/#
# Set the ConnectedTo Variable
#
# debug
"""
self.debug(
[
'Check if we have to get the connected to variable',
('self.',self,['ConnectedToVariable'])
]
)
"""
# Check
if self.ConnectedToVariable == None:
# debug
"""
self.debug(
[
'We setConnection here'
]
)
"""
# setConnection
self.setConnection(self.ManagementTagStr, self, self.BrianedParentPopulationDeriveBrianerVariable)
# /####################/#
# Set the BrianedParentPopulationDeriveBrianerVariable
#
# debug
"""
self.debug(
[
'Do we have to make parent-brian the connected variable ?',
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable)
]
)
"""
# Check
if self.ConnectedToVariable.BrianedNeurongroupVariable == None:
# parent brian
self.ConnectedToVariable.parent().brian()
# set
BrianedNameStr = (
self.BrianedParentPopulationDeriveBrianerVariable.StructureTagStr
+ "_To_"
+ self.ConnectedToVariable.StructureTagStr
)
# debug
"""
self.debug(
[
'We set the synapses',
'self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable),
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable),
'Maybe we have to make brian the post',
'BrianedNameStr is '+BrianedNameStr
]
)
"""
# import
from brian2 import Synapses
# init
self.BrianedSynapsesVariable = Synapses(
source=self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable,
target=self.ConnectedToVariable.BrianedNeurongroupVariable,
# name=BrianedNameStr.replace('/','_'),
**self.BrianingSynapsesDict
)
# /####################/#
# Connect options
#
# connect
if type(self.BrianingConnectVariable) == float:
# debug
"""
self.debug(
[
'we connect with a sparsity of ',
('self.',self,[
'BrianingConnectVariable'
])
]
)
"""
# connect
self.BrianedSynapsesVariable.connect(True, p=self.BrianingConnectVariable)
# /####################/#
# add to the structure
#
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(self.BrianedSynapsesVariable)
# /##################/#
# Define a debug
#
# Check
if self.BrianingDebugVariable > 0:
# import
from brian2 import network_operation, ms
@network_operation(
dt=self.BrianingDebugVariable
* self.BrianedParentNetworkDeriveBrianerVariable.BrianedTimeQuantityVariable
)
def debugSynapses():
# init
PrintStr = "At time t=" + str(self.BrianedSynapsesVariable.clock.t) + ", \n"
PrintStr += "In the Synapses " + self.BrianedSynapsesVariable.name + " : \n"
# loop
for __KeyStr in self.BrianedSynapsesVariable.equations._equations.keys():
# set
PrintStr += __KeyStr + " is " + str(getattr(self.BrianedSynapsesVariable, __KeyStr)) + "\n"
# add
PrintStr += "\n"
# print
print PrintStr
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(debugSynapses)
def _build_connections(self, traj, brian_list, network_dict):
"""Connects neuron groups `neurons_i` and `neurons_e`.
Adds all connections to `brian_list` and adds a list of connections
with the key 'connections' to the `network_dict`.
"""
connections = traj.connections
neurons_i = network_dict['neurons_i']
neurons_e = network_dict['neurons_e']
print('Connecting ii')
self.conn_ii = Synapses(neurons_i,neurons_i, on_pre='y_i += %f' % connections.J_ii)
self.conn_ii.connect('i != j', p=connections.p_ii)
print('Connecting ei')
self.conn_ei = Synapses(neurons_i,neurons_e, on_pre='y_i += %f' % connections.J_ei)
self.conn_ei.connect('i != j', p=connections.p_ei)
print('Connecting ie')
self.conn_ie = Synapses(neurons_e,neurons_i, on_pre='y_e += %f' % connections.J_ie)
self.conn_ie.connect('i != j', p=connections.p_ie)
conns_list = [self.conn_ii, self.conn_ei, self.conn_ie]
if connections.R_ee > 1.0:
# If we come here we want to create clusters
cluster_list=[]
cluster_conns_list=[]
model=traj.model
# Compute the number of clusters
clusters = int(model.N_e/connections.clustersize_e)
traj.f_add_derived_parameter('connections.clusters', clusters, comment='Number of clusters')
# Compute outgoing connection probability
p_out = (connections.p_ee*model.N_e) / \
(connections.R_ee*connections.clustersize_e+model.N_e- connections.clustersize_e)
# Compute within cluster connection probability
p_in = p_out * connections.R_ee
# We keep these derived parameters
traj.f_add_derived_parameter('connections.p_ee_in', p_in ,
comment='Connection prob within cluster')
traj.f_add_derived_parameter('connections.p_ee_out', p_out ,
comment='Connection prob to outside of cluster')
low_index = 0
high_index = connections.clustersize_e
# Iterate through cluster and connect within clusters and to the rest of the neurons
for irun in range(clusters):
cluster = neurons_e[low_index:high_index]
# Connections within cluster
print('Connecting ee cluster #%d of %d' % (irun, clusters))
conn = Synapses(cluster,cluster,
on_pre='y_e += %f' % (connections.J_ee*connections.strength_factor))
conn.connect('i != j', p=p_in)
cluster_conns_list.append(conn)
# Connections reaching out from cluster
# A cluster consists of `clustersize_e` neurons with consecutive indices.
# So usually the outside world consists of two groups, neurons with lower
# indices than the cluster indices, and neurons with higher indices.
# Only the clusters at the index boundaries project to neurons with only either
# lower or higher indices
if low_index > 0:
rest_low = neurons_e[0:low_index]
print('Connecting cluster with other neurons of lower index')
low_conn = Synapses(cluster,rest_low,
on_pre='y_e += %f' % connections.J_ee)
low_conn.connect('i != j', p=p_out)
cluster_conns_list.append(low_conn)
if high_index < model.N_e:
rest_high = neurons_e[high_index:model.N_e]
print('Connecting cluster with other neurons of higher index')
high_conn = Synapses(cluster,rest_high,
on_pre='y_e += %f' % connections.J_ee)
high_conn.connect('i != j', p=p_out)
cluster_conns_list.append(high_conn)
low_index=high_index
high_index+=connections.clustersize_e
self.cluster_conns=cluster_conns_list
conns_list+=cluster_conns_list
else:
# Here we don't cluster and connection probabilities are homogeneous
print('Connectiong ee')
self.conn_ee = Synapses(neurons_e,neurons_e,
on_pre='y_e += %f' % connections.J_ee)
self.conn_ee.connect('i != j', p=connections.p_ee)
conns_list.append(self.conn_ee)
# Add the connections to the `brian_list` and the network dict
brian_list.extend(conns_list)
network_dict['connections'] = conns_list
class CNConnections(NetworkComponent):
"""Class to connect neuron groups.
In case of no clustering `R_ee=1,0` there are 4 connection instances (i->i, i->e, e->i, e->e).
Otherwise there are 3 + 3*N_c-2 connections with N_c the number of clusters
(i->i, i->e, e->i, N_c conns within cluster, 2*N_c-2 connections from cluster to outside).
"""
@staticmethod
def add_parameters(traj):
"""Adds all neuron group parameters to `traj`."""
assert(isinstance(traj,Trajectory))
traj.v_standard_parameter = Brian2Parameter
scale = traj.simulation.scale
traj.f_add_parameter('connections.R_ee', 1.0, comment='Scaling factor for clustering')
traj.f_add_parameter('connections.clustersize_e', 100, comment='Size of a cluster')
traj.f_add_parameter('connections.strength_factor', 2.5,
comment='Factor for scaling cluster weights')
traj.f_add_parameter('connections.p_ii', 0.25,
comment='Connection probability from inhibitory to inhibitory' )
traj.f_add_parameter('connections.p_ei', 0.25,
comment='Connection probability from inhibitory to excitatory' )
traj.f_add_parameter('connections.p_ie', 0.25,
comment='Connection probability from excitatory to inhibitory' )
traj.f_add_parameter('connections.p_ee', 0.1,
comment='Connection probability from excitatory to excitatory' )
traj.f_add_parameter('connections.J_ii', 0.027/np.sqrt(scale),
comment='Connection strength from inhibitory to inhibitory')
traj.f_add_parameter('connections.J_ei', 0.032/np.sqrt(scale),
comment='Connection strength from inhibitory to excitatroy')
traj.f_add_parameter('connections.J_ie', 0.009/np.sqrt(scale),
comment='Connection strength from excitatory to inhibitory')
traj.f_add_parameter('connections.J_ee', 0.012/np.sqrt(scale),
comment='Connection strength from excitatory to excitatory')
def pre_build(self, traj, brian_list, network_dict):
"""Pre-builds the connections.
Pre-build is only performed if none of the
relevant parameters is explored and the relevant neuron groups
exist.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Connections, amount depends on clustering
:param network_dict:
Dictionary of elements shared among the components
Expects:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
Adds:
Connections, amount depends on clustering
"""
self._pre_build = not _explored_parameters_in_group(traj, traj.parameters.connections)
self._pre_build = (self._pre_build and 'neurons_i' in network_dict and
'neurons_e' in network_dict)
if self._pre_build:
self._build_connections(traj, brian_list, network_dict)
def build(self, traj, brian_list, network_dict):
"""Builds the connections.
Build is only performed if connections have not
been pre-build.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Connections, amount depends on clustering
:param network_dict:
Dictionary of elements shared among the components
Expects:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
Adds:
Connections, amount depends on clustering
"""
if not hasattr(self, '_pre_build') or not self._pre_build:
self._build_connections(traj, brian_list, network_dict)
def _build_connections(self, traj, brian_list, network_dict):
"""Connects neuron groups `neurons_i` and `neurons_e`.
Adds all connections to `brian_list` and adds a list of connections
with the key 'connections' to the `network_dict`.
"""
connections = traj.connections
neurons_i = network_dict['neurons_i']
neurons_e = network_dict['neurons_e']
print('Connecting ii')
self.conn_ii = Synapses(neurons_i,neurons_i, on_pre='y_i += %f' % connections.J_ii)
self.conn_ii.connect('i != j', p=connections.p_ii)
print('Connecting ei')
self.conn_ei = Synapses(neurons_i,neurons_e, on_pre='y_i += %f' % connections.J_ei)
self.conn_ei.connect('i != j', p=connections.p_ei)
print('Connecting ie')
self.conn_ie = Synapses(neurons_e,neurons_i, on_pre='y_e += %f' % connections.J_ie)
self.conn_ie.connect('i != j', p=connections.p_ie)
conns_list = [self.conn_ii, self.conn_ei, self.conn_ie]
if connections.R_ee > 1.0:
# If we come here we want to create clusters
cluster_list=[]
cluster_conns_list=[]
model=traj.model
# Compute the number of clusters
clusters = int(model.N_e/connections.clustersize_e)
traj.f_add_derived_parameter('connections.clusters', clusters, comment='Number of clusters')
# Compute outgoing connection probability
p_out = (connections.p_ee*model.N_e) / \
(connections.R_ee*connections.clustersize_e+model.N_e- connections.clustersize_e)
# Compute within cluster connection probability
p_in = p_out * connections.R_ee
# We keep these derived parameters
traj.f_add_derived_parameter('connections.p_ee_in', p_in ,
comment='Connection prob within cluster')
traj.f_add_derived_parameter('connections.p_ee_out', p_out ,
comment='Connection prob to outside of cluster')
low_index = 0
high_index = connections.clustersize_e
# Iterate through cluster and connect within clusters and to the rest of the neurons
for irun in range(clusters):
cluster = neurons_e[low_index:high_index]
# Connections within cluster
print('Connecting ee cluster #%d of %d' % (irun, clusters))
conn = Synapses(cluster,cluster,
on_pre='y_e += %f' % (connections.J_ee*connections.strength_factor))
conn.connect('i != j', p=p_in)
cluster_conns_list.append(conn)
# Connections reaching out from cluster
# A cluster consists of `clustersize_e` neurons with consecutive indices.
# So usually the outside world consists of two groups, neurons with lower
# indices than the cluster indices, and neurons with higher indices.
# Only the clusters at the index boundaries project to neurons with only either
# lower or higher indices
if low_index > 0:
rest_low = neurons_e[0:low_index]
print('Connecting cluster with other neurons of lower index')
low_conn = Synapses(cluster,rest_low,
on_pre='y_e += %f' % connections.J_ee)
low_conn.connect('i != j', p=p_out)
cluster_conns_list.append(low_conn)
if high_index < model.N_e:
rest_high = neurons_e[high_index:model.N_e]
print('Connecting cluster with other neurons of higher index')
high_conn = Synapses(cluster,rest_high,
on_pre='y_e += %f' % connections.J_ee)
high_conn.connect('i != j', p=p_out)
cluster_conns_list.append(high_conn)
low_index=high_index
high_index+=connections.clustersize_e
self.cluster_conns=cluster_conns_list
conns_list+=cluster_conns_list
else:
# Here we don't cluster and connection probabilities are homogeneous
print('Connectiong ee')
self.conn_ee = Synapses(neurons_e,neurons_e,
on_pre='y_e += %f' % connections.J_ee)
self.conn_ee.connect('i != j', p=connections.p_ee)
conns_list.append(self.conn_ee)
# Add the connections to the `brian_list` and the network dict
brian_list.extend(conns_list)
network_dict['connections'] = conns_list
def simulate_wm(
N_excitatory=1024, N_inhibitory=256,
N_extern_poisson=1000, poisson_firing_rate=1.4 * b2.Hz, weight_scaling_factor=2.,
sigma_weight_profile=20., Jpos_excit2excit=1.6,
stimulus_center_deg=180, stimulus_width_deg=40, stimulus_strength=0.07 * b2.namp,
t_stimulus_start=0 * b2.ms, t_stimulus_duration=0 * b2.ms,
distractor_center_deg=90, distractor_width_deg=40, distractor_strength=0.0 * b2.namp,
t_distractor_start=0 * b2.ms, t_distractor_duration=0 * b2.ms,
G_inhib2inhib=.35 * 1.024 * b2.nS,
G_inhib2excit=.35 * 1.336 * b2.nS,
G_excit2excit=.35 * 0.381 * b2.nS,
G_excit2inhib=.35 * 1.2 * 0.292 * b2.nS,
monitored_subset_size=1024, sim_time=800. * b2.ms):
"""
Args:
N_excitatory (int): Size of the excitatory population
N_inhibitory (int): Size of the inhibitory population
weight_scaling_factor (float): weight prefactor. When increasing the size of the populations,
the synaptic weights have to be decreased. Using the default values, we have
N_excitatory*weight_scaling_factor = 2048 and N_inhibitory*weight_scaling_factor=512
N_extern_poisson (int): Size of the external input population (Poisson input)
poisson_firing_rate (Quantity): Firing rate of the external population
sigma_weight_profile (float): standard deviation of the gaussian input profile in
the excitatory population.
Jpos_excit2excit (float): Strength of the recurrent input within the excitatory population.
Jneg_excit2excit is computed from sigma_weight_profile, Jpos_excit2excit and the normalization
condition.
stimulus_center_deg (float): Center of the stimulus in [0, 360]
stimulus_width_deg (float): width of the stimulus. All neurons in
stimulus_center_deg +\- (stimulus_width_deg/2) receive the same input current
stimulus_strength (Quantity): Input current to the neurons at stimulus_center_deg +\- (stimulus_width_deg/2)
t_stimulus_start (Quantity): time when the input stimulus is turned on
t_stimulus_duration (Quantity): duration of the stimulus.
distractor_center_deg (float): Center of the distractor in [0, 360]
distractor_width_deg (float): width of the distractor. All neurons in
distractor_center_deg +\- (distractor_width_deg/2) receive the same input current
distractor_strength (Quantity): Input current to the neurons at
distractor_center_deg +\- (distractor_width_deg/2)
t_distractor_start (Quantity): time when the distractor is turned on
t_distractor_duration (Quantity): duration of the distractor.
G_inhib2inhib (Quantity): projections from inhibitory to inhibitory population (later
rescaled by weight_scaling_factor)
G_inhib2excit (Quantity): projections from inhibitory to excitatory population (later
rescaled by weight_scaling_factor)
G_excit2excit (Quantity): projections from excitatory to excitatory population (later
rescaled by weight_scaling_factor)
G_excit2inhib (Quantity): projections from excitatory to inhibitory population (later
rescaled by weight_scaling_factor)
monitored_subset_size (int): nr of neurons for which a Spike- and Voltage monitor
is registered.
sim_time (Quantity): simulation time
Returns:
results (tuple):
rate_monitor_excit (Brian2 PopulationRateMonitor for the excitatory population),
spike_monitor_excit, voltage_monitor_excit, idx_monitored_neurons_excit,\
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib,\
weight_profile_45 (The weights profile for the neuron with preferred direction = 45deg).
"""
# specify the excitatory pyramidal cells:
Cm_excit = 0.5 * b2.nF # membrane capacitance of excitatory neurons
G_leak_excit = 25.0 * b2.nS # leak conductance
E_leak_excit = -70.0 * b2.mV # reversal potential
v_firing_threshold_excit = -50.0 * b2.mV # spike condition
v_reset_excit = -60.0 * b2.mV # reset voltage after spike
t_abs_refract_excit = 2.0 * b2.ms # absolute refractory period
# specify the weight profile in the recurrent population
# std-dev of the gaussian weight profile around the prefered direction
# sigma_weight_profile = 12.0 # std-dev of the gaussian weight profile around the prefered direction
#
# Jneg_excit2excit = 0
# specify the inhibitory interneurons:
Cm_inhib = 0.2 * b2.nF
G_leak_inhib = 20.0 * b2.nS
E_leak_inhib = -70.0 * b2.mV
v_firing_threshold_inhib = -50.0 * b2.mV
v_reset_inhib = -60.0 * b2.mV
t_abs_refract_inhib = 1.0 * b2.ms
# specify the AMPA synapses
E_AMPA = 0.0 * b2.mV
tau_AMPA = .9 * 2.0 * b2.ms
# specify the GABA synapses
E_GABA = -70.0 * b2.mV
tau_GABA = 10.0 * b2.ms
# specify the NMDA synapses
E_NMDA = 0.0 * b2.mV
tau_NMDA_s = .65 * 100.0 * b2.ms # orig: 100
tau_NMDA_x = .94 * 2.0 * b2.ms
alpha_NMDA = 0.5 * b2.kHz
# projections from the external population
G_extern2inhib = 2.38 * b2.nS
G_extern2excit = 3.1 * b2.nS
# projectsions from the inhibitory populations
G_inhib2inhib *= weight_scaling_factor
G_inhib2excit *= weight_scaling_factor
# projections from the excitatory population
G_excit2excit *= weight_scaling_factor
G_excit2inhib *= weight_scaling_factor # todo: verify this scaling
t_stimulus_end = t_stimulus_start + t_stimulus_duration
t_distractor_end = t_distractor_start + t_distractor_duration
# compute the simulus index
stim_center_idx = int(round(N_excitatory / 360. * stimulus_center_deg))
stim_width_idx = int(round(N_excitatory / 360. * stimulus_width_deg / 2))
stim_target_idx = [idx % N_excitatory
for idx in range(stim_center_idx - stim_width_idx, stim_center_idx + stim_width_idx + 1)]
# compute the distractor index
distr_center_idx = int(round(N_excitatory / 360. * distractor_center_deg))
distr_width_idx = int(round(N_excitatory / 360. * distractor_width_deg / 2))
distr_target_idx = [idx % N_excitatory for idx in range(distr_center_idx - distr_width_idx,
distr_center_idx + distr_width_idx + 1)]
# precompute the weight profile for the recurrent population
tmp = math.sqrt(2. * math.pi) * sigma_weight_profile * erf(180. / math.sqrt(2.) / sigma_weight_profile) / 360.
Jneg_excit2excit = (1. - Jpos_excit2excit * tmp) / (1. - tmp)
presyn_weight_kernel = \
[(Jneg_excit2excit +
(Jpos_excit2excit - Jneg_excit2excit) *
math.exp(-.5 * (360. * min(j, N_excitatory - j) / N_excitatory) ** 2 / sigma_weight_profile ** 2))
for j in range(N_excitatory)]
# validate the normalization condition: (360./N_excitatory)*sum(presyn_weight_kernel)/360.
fft_presyn_weight_kernel = rfft(presyn_weight_kernel)
weight_profile_45 = deque(presyn_weight_kernel)
rot_dist = int(round(len(weight_profile_45) / 8))
weight_profile_45.rotate(rot_dist)
# define the inhibitory population
inhib_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_inhib * (v-E_leak_inhib)
- G_extern2inhib * s_AMPA * (v-E_AMPA)
- G_inhib2inhib * s_GABA * (v-E_GABA)
- G_excit2inhib * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_inhib : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
"""
inhib_pop = NeuronGroup(
N_inhibitory, model=inhib_lif_dynamics,
threshold="v>v_firing_threshold_inhib", reset="v=v_reset_inhib", refractory=t_abs_refract_inhib,
method="rk2")
# initialize with random voltages:
inhib_pop.v = numpy.random.uniform(v_reset_inhib / b2.mV, high=v_firing_threshold_inhib / b2.mV,
size=N_inhibitory) * b2.mV
# set the connections: inhib2inhib
syn_inhib2inhib = Synapses(inhib_pop, target=inhib_pop, on_pre="s_GABA += 1.0", delay=0.0 * b2.ms)
syn_inhib2inhib.connect(condition="i!=j", p=1.0)
# set the connections: extern2inhib
input_ext2inhib = PoissonInput(target=inhib_pop, target_var="s_AMPA",
N=N_extern_poisson, rate=poisson_firing_rate, weight=1.0)
# specify the excitatory population:
excit_lif_dynamics = """
I_stim : amp
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_excit * (v-E_leak_excit)
- G_extern2excit * s_AMPA * (v-E_AMPA)
- G_inhib2excit * s_GABA * (v-E_GABA)
- G_excit2excit * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
+ I_stim
)/Cm_excit : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
ds_NMDA/dt = -s_NMDA/tau_NMDA_s + alpha_NMDA * x * (1-s_NMDA) : 1
dx/dt = -x/tau_NMDA_x : 1
"""
excit_pop = NeuronGroup(N_excitatory, model=excit_lif_dynamics,
threshold="v>v_firing_threshold_excit", reset="v=v_reset_excit; x+=1.0",
refractory=t_abs_refract_excit, method="rk2")
# initialize with random voltages:
excit_pop.v = numpy.random.uniform(v_reset_excit / b2.mV, high=v_firing_threshold_excit / b2.mV,
size=N_excitatory) * b2.mV
excit_pop.I_stim = 0. * b2.namp
# set the connections: extern2excit
input_ext2excit = PoissonInput(target=excit_pop, target_var="s_AMPA",
N=N_extern_poisson, rate=poisson_firing_rate, weight=1.0)
# set the connections: inhibitory to excitatory
syn_inhib2excit = Synapses(inhib_pop, target=excit_pop, on_pre="s_GABA += 1.0")
syn_inhib2excit.connect(p=1.0)
# set the connections: excitatory to inhibitory NMDA connections
syn_excit2inhib = Synapses(excit_pop, inhib_pop,
model="s_NMDA_total_post = s_NMDA_pre : 1 (summed)", method="rk2")
syn_excit2inhib.connect(p=1.0)
# # set the connections: UNSTRUCTURED excitatory to excitatory
# syn_excit2excit = Synapses(excit_pop, excit_pop,
# model= "s_NMDA_total_post = s_NMDA_pre : 1 (summed)", method="rk2")
# syn_excit2excit.connect(condition="i!=j", p=1.)
# set the STRUCTURED recurrent input. use a network_operation
@network_operation()
def update_nmda_sum():
fft_s_NMDA = rfft(excit_pop.s_NMDA)
fft_s_NMDA_total = numpy.multiply(fft_presyn_weight_kernel, fft_s_NMDA)
s_NMDA_tot = irfft(fft_s_NMDA_total)
excit_pop.s_NMDA_total_ = s_NMDA_tot
@network_operation(dt=1 * b2.ms)
def stimulate_network(t):
if t >= t_stimulus_start and t < t_stimulus_end:
# excit_pop[stim_start_i - 15:stim_start_i + 15].I_stim = 0.25 * b2.namp
# Todo: review indexing
# print("stim on")
excit_pop.I_stim[stim_target_idx] = stimulus_strength
else:
# print("stim off")
excit_pop.I_stim = 0. * b2.namp
# add distractor
if t >= t_distractor_start and t < t_distractor_end:
excit_pop.I_stim[distr_target_idx] = distractor_strength
def get_monitors(pop, nr_monitored, N):
nr_monitored = min(nr_monitored, (N))
idx_monitored_neurons = \
[int(math.ceil(k))
for k in numpy.linspace(0, N - 1, nr_monitored + 2)][1:-1] # sample(range(N), nr_monitored)
rate_monitor = PopulationRateMonitor(pop)
# record= some_list is not supported? :-(
spike_monitor = SpikeMonitor(pop, record=idx_monitored_neurons)
voltage_monitor = StateMonitor(pop, "v", record=idx_monitored_neurons)
return rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons
# collect data of a subset of neurons:
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib = \
get_monitors(inhib_pop, monitored_subset_size, N_inhibitory)
rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, idx_monitored_neurons_excit = \
get_monitors(excit_pop, monitored_subset_size, N_excitatory)
b2.run(sim_time)
return \
rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, idx_monitored_neurons_excit,\
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib,\
weight_profile_45
lsonGrp.m = 1. / (1 + exp(-(vu + 38.) / 7.))
lsonGrp.h = 1. / (1 + exp((vu + 65.) / 6.))
lsonGrp.n = (1 + exp(-(vu + 15) / 5.))**-0.5
lsonGrp.p = 1. / (1 + exp(-(vu + 23) / 6.))
lsonGrp.r = 1. / (1 + exp((vu + 76.) / 7.))
lsonGrp.w = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25
lsonGrp.z = zss + ((1. - zss) / (1 + exp((vu + 71.) / 10.)))
lsonGrp.a = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25
lsonGrp.b = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.c = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.h1 = 1. / (1 + exp((vu + 66.) / 7.))
lsonGrp.h2 = 1. / (1 + exp((vu + 66.) / 7.))
lsonSynI = Synapses(gbCoSpkGenGrp,
lsonGrp,
model='''dg_i/dt = -g_i/tausI : siemens (clock-driven)
gs_i_post = g_i : siemens (summed)''',
on_pre='g_i += w_ilson*siemens',
method='exact')
lsonSynI.connect(i=np.arange(nGbcsCoPerLson), j=0)
#lsonSynE = Synapses(sbIpSpkGenGrp, lsonGrp,
# model='''dg_e/dt = -g_e/tausE : siemens (clock-driven)
# gs_e_post = g_e : siemens (summed)''',
# on_pre='g_e += w_elson*siemens',
# method = 'exact')
#lsonSynE.connect(i=np.arange(nSbcsIpPerLson), j=0)
lsonSpks = SpikeMonitor(lsonGrp)
lsonState = StateMonitor(lsonGrp, ['gs_e', 'Is_e', 'gs_i', 'Is_i', 'v'],
record=True)
def do_brian(self):
#debug
self.debug(
[
'We brian here',
('self.',self,['ParentedTotalSingularListDict'])
]
)
#Check
if self.ParentDeriveTeamerVariable==None or 'Neurongroups' in self.TeamDict or self.ParentDeriveTeamerVariable.TeamTagStr not in [
'Traces','Samples'
]:
#/###################/#
# Recorder level
# structure
#debug
self.debug(
[
'It is a Network level',
('self.',self,[
])
]
)
#/########################/#
# Network case
#
#import
from brian2 import Network
#structure
self.BrianedNetworkVariable=Network()
#/########################/#
# make brian the neurongroups
#
#structure
self.structure(
[
'Neurongroups',
'Postlets',
'Traces',
'Events',
'Samples'
],
None
)
"""
#map
map(
lambda __DeriveBrianer:
__DeriveBrianer.brian(),
self.TeamDict['Neurongroups'].ManagementDict.values()
)
"""
#Check
elif len(self.BrianingNeurongroupDict)>0:
#/##################/#
# Neurongroup case
# adapt the BrianingNeurongroupDict and init
#debug
'''
self.debug(
[
'It is a Neurongroup level, we set the Neurongroup',
'We adapt the shape of BrianingNeurongroupDict',
('self.',self,[
'BrianingNeurongroupDict',
'TracingKeyVariable'
])
]
)
'''
#Check
if 'N' not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict['N']=self.SimulatingUnitsInt
else:
self.SimulatingUnitsInt=self.BrianingNeurongroupDict['N']
#Check
if 'model' not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict['model']=''
#maybe should import
from brian2 import NeuronGroup
#debug
'''
self.debug(
[
('self.',self,[
'BrianingNeurongroupDict'
])
]
)
'''
#init
self.BrianedNeurongroupVariable=NeuronGroup(
**self.BrianingNeurongroupDict
)
#debug
'''
self.debug(
[
'Ok we have setted the Neurongroup',
('self.',self,[
'BrianedNeurongroupVariable'
])
]
)
'''
#/##################/#
# set the BrianedParentNeurongroupDeriveBrianerVariable
#
#debug
'''
self.debug(
[
'We get the parent structure',
'self.TeamDict is ',
SYS._str(self.TeamDict.keys())
]
)
'''
#Check
if 'Networks' in self.TeamDict:
if 'Brianer' in self.TeamDict['Networks'].ManagementDict:
#debug
'''
self.debug(
[
'Brianer neurongroup is contained in a structureing brianer structure'
]
)
'''
#set
self.BrianedParentNetworkDeriveBrianerVariable=self.TeamDict[
'Networks'
].ManagementDict['Brianer']
if self.ParentDeriveTeamerVariable!=None and self.ParentDeriveTeamerVariable.TeamTagStr=='Neurongroups':
#debug
'''
self.debug(
[
'Brianer neurongroup is contained in a parenting brianer structure'
]
)
'''
#set
self.BrianedParentNetworkDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#debug
'''
self.debug(
[
'We get the parent structure',
('self.',self,['BrianedParentNeurongroupDeriveBrianerVariable'])
]
)
'''
#/##################/#
# team States first all the brian variables
#
#get
self.BrianedTraceKeyStrsList=self.BrianedNeurongroupVariable.equations._equations.keys()
#Check
if len(self.BrianedTraceKeyStrsList)>0:
#debug
'''
self.debug(
[
'We simulate with neurongroup',
'adapt the initial conditions of all the brian variables',
'so first we team Traces and put Tracers inside or get it and mapSet'
]
)
'''
#Check
if 'Traces' not in self.TeamDict:
BrianedDeriveTraces=self.team(
'Traces'
).TeamedValueVariable
else:
BrianedDeriveTraces=self.TeamDict[
'Traces'
]
#map
self.BrianedTraceDeriveBrianersList=map(
lambda __ManagementKeyStr,__TraceKeyStr:
BrianedDeriveTraces.manage(
__ManagementKeyStr,
{
'TracingKeyVariable':getattr(
self.BrianedNeurongroupVariable,
__TraceKeyStr
),
'TraceKeyStr':__TraceKeyStr
}
).ManagedValueVariable
if __ManagementKeyStr not in BrianedDeriveTraces.ManagementDict
else BrianedDeriveTraces.ManagementDict[__ManagementKeyStr].mapSet(
{
'TracingKeyVariable':getattr(
self.BrianedNeurongroupVariable,
__TraceKeyStr
),
'TraceKeyStr':__TraceKeyStr
}
),
map(
lambda __BrianedTraceKeyStr:
Tracer.TracerPrefixStr+__BrianedTraceKeyStr,
self.BrianedTraceKeyStrsList
),
self.BrianedTraceKeyStrsList
)
#/##################/#
# Case of one only pop without parent Network
#
#set
BrianedNetworkBool=False
#Check
if 'Networks' in self.TeamDict:
if 'Brianer' in self.TeamDict['Networks'].ManagementDict:
#set
BrianedNetworkBool=True
#Check
if self.ParentDeriveTeamerVariable!=None:
if self.ParentDeriveTeamerVariable.TeamTagStr=='Neurongroups':
#set
BrianedNetworkBool=True
#Check
if BrianedNetworkBool==False:
#/################/#
# Set a structure at this place
#
#debug
'''
self.debug(
[
'This is just one neurongroup without parent structure'
]
)
'''
#import
from brian2 import Network
#structure
self.BrianedNetworkVariable=Network()
#/################/#
# Maybe structure
#
#debug
self.debug(
[
'We structure Traces,Events,Samples'
]
)
#structure
self.structure(
[
'Postlets',
'Traces',
'Events',
'Samples'
],
None
)
else:
#alias
self.BrianedNetworkVariable=self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable
#debug
'''
self.debug(
[
'Ok we know the structure ',
('self.',self,['BrianedNetworkVariable'])
]
)
'''
#/##################/#
# add in the net
#
#add
self.BrianedNetworkVariable.add(
self.BrianedNeurongroupVariable
)
#/##################/#
# We make brian the Tracers
#
#debug
'''
self.debug(
[
'Make brian the tracers',
('self.',self,['BrianedTraceDeriveBrianersList'])
]
)
'''
#map
"""
map(
lambda __BrianedDeriveTracer:
__BrianedDeriveTracer.brian(),
self.BrianedTraceDeriveBrianersList
)
"""
#debug
'''
self.debug(
[
'Ok the Tracers have brianed',
'Now we check if it is a wrapped neurongroup into a structure or not'
]
)
'''
"""
#/##################/#
# Make brian the spikes
#
#debug
self.debug(
[
'We make brian the Spikes'
]
)
#Check
if 'Events' in self.TeamDict:
#map
map(
lambda __DeriveBrianer:
__DeriveBrianer.brian(),
self.TeamDict['Events'].ManagementDict.values()
)
#debug
self.debug(
[
'We have made brian the spikes'
]
)
"""
elif self.ParentDeriveTeamerVariable.TeamTagStr=='Postlets':
#debug
self.debug(
[
'It is a Synapser level, we set the Synapser',
('self.',self,[
'BrianingSynapsesDict'
]
),
'self.PointToVariable.BrianedNeurongroupVariable is ',
str(self.PointToVariable.BrianedNeurongroupVariable)
]
)
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#import
from brian2 import Synapses
#init
self.BrianedSynapsesVariable=Synapses(
source=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
target=self.PointToVariable.BrianedNeurongroupVariable,
**self.BrianingSynapsesDict
)
#/####################/#
# Connect options
#
#connect
if type(self.BrianingConnectVariable)==float:
#debug
self.debug(
[
'we connect with a sparsity of ',
('self.',self,[
'BrianingConnectVariable'
])
]
)
#connect
self.BrianedSynapsesVariable.connect(
True,
p=self.BrianingConnectVariable
)
"""
#/####################/#
# Reshape the weigths
#
#Check
if self.SynapsingWeigthSymbolStr!="":
#debug
'''
self.debug(
('self.',self,[
'BrianedSynapsesVariable',
'SynapsingWeigthSymbolStr'
])
)
'''
#connect
self.BrianedSynapsesVariable.connect(True)
#get
self.SynapsedWeigthFloatsArray=getattr(
self.BrianedSynapsesVariable,
self.SynapsingWeigthSymbolStr
)
#set
self.SynapsedWeigthFloatsArray[:]=np.reshape(
self.SynapsingWeigthFloatsArray,
self.BrianedSynapsesVariable.source.N*self.BrianedSynapsesVariable.target.N
)
#debug
self.debug(
('self.',self,[
'SynapsedWeigthFloatsArray'
])
)
"""
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedSynapsesVariable
)
elif self.ParentDeriveTeamerVariable.TeamTagStr=='Traces':
#debug
self.debug(
[
'It is a Tracer level, we set the Samples',
('self.',self,[
#'TracingKeyVariable',
'TraceKeyStr'
])
]
)
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#/###################/#
# Build the samples and maybe one default moniter
#
#Check
if 'Samples' not in self.TeamDict:
BrianedDeriveSamples=self.team(
'Samples'
).TeamedValueVariable
else:
BrianedDeriveSamples=self.TeamDict[
'Samples'
]
#debug
'''
self.debug(
[
'Do we have to set a default moniter ?',
'len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedTraceKeyStrsList) is ',
str(len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedTraceKeyStrsList))
]
)
'''
#Check
if len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedTraceKeyStrsList)==1:
#Check
if len(BrianedDeriveSamples.ManagementDict)==0:
BrianedDefaultMoniter=BrianedDeriveSamples.manage(
'Default',
).ManagedValueVariable
BrianedDefaultMoniter.MoniteringLabelIndexIntsArray=[0] if self.BrianedParentNeurongroupDeriveBrianerVariable.BrianingNeurongroupDict[
'N']>0 else []
"""
#/###################/#
# We trace and set to the brian value
#
#debug
'''
self.debug(
[
'We trace and alias the init in the brian object',
('self.',self,['TracingKeyVariable'])
]
)
'''
#trace
self.trace()
#debug
'''
self.debug(
[
'We have traced, alias the init in the brian object',
('self.',self,[
'TracedValueFloatsArray',
'TracedInitFloatsArray'
])
]
)
'''
#alias
self.TracedValueFloatsArray[:]=self.TracedInitFloatsArray*self.TracedValueFloatsArray.unit
#debug
'''
self.debug(
[
('self.',self,['TracedValueFloatsArray'])
]
)
'''
"""
#
"""
#/###################/#
# We make brian the Moniters
#
#debug
'''
self.debug(
[
'We make brian the moniters',
'BrianedDeriveSamples.ManagementDict.keys() is ',
str(BrianedDeriveSamples.ManagementDict.keys())
]
)
'''
#map
map(
lambda __DeriveMoniter:
__DeriveMoniter.brian(),
BrianedDeriveSamples.ManagementDict.values()
)
#debug
'''
self.debug(
[
'We have made brian the moniters',
]
)
'''
"""
elif self.ParentDeriveTeamerVariable.TeamTagStr=='Samples':
#debug
'''
self.debug(
[
'It is a State Moniter level',
('self.',self,[
'MoniteringLabelIndexIntsArray',
])
]
)
'''
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#get
self.BrianedParentDeriveTracerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.BrianedParentDeriveTracerVariable.BrianedParentNeurongroupDeriveBrianerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the brian monitor
#
#debug
self.debug(
[
'We set the state monitor',
('self.',self,[
#'BrianedParentNeurongroupDeriveBrianerVariable'
]),
#'self.BrianedParentDeriveTracerVariable.TraceKeyStr is ',
#str(self.BrianedParentDeriveTracerVariable.TraceKeyStr)
'self.ParentedTotalManagementOrderedDict.keys() is ',
str(self.ParentedTotalManagementOrderedDict.keys())
]
)
#import
from brian2 import StateMonitor
#init
self.BrianedStateMonitorVariable=StateMonitor(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
self.BrianedParentDeriveTracerVariable.TraceKeyStr,
self.MoniteringLabelIndexIntsArray,
)
#debug
'''
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedStateMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable)
]
)
'''
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedStateMonitorVariable
)
elif self.ParentDeriveTeamerVariable.TeamTagStr=='Events':
#debug
'''
self.debug(
[
'It is a Spike Moniter level',
('self.',self,[
])
]
)
'''
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the brian monitor
#
#debug
'''
self.debug(
[
'We set the spike monitor'
]
)
'''
#import
from brian2 import SpikeMonitor
#init
self.BrianedSpikeMonitorVariable=SpikeMonitor(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
)
#debug
'''
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedSpikeMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable)
]
)
'''
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedSpikeMonitorVariable
)
def lson(lsoInputSpkFileTuple, temp_degC=37):
defaultclock.dt = 0.02 * ms # for better precision
neuron_type = 'type2' # medium-fast membrane f0 180-260Hz, CF4kHz
#temp_degC=37.
Vrest = -63.6 * mV # resting potential for type1c from RM2003
nLsons = 1 # number of LSO neurons
nGbcsCo = 4
# nGbcsIp = 4
# nSbcsCo = 4
nSbcsIp = 4
# nSbcs = 4
# nAnfsPerSbc = 3
# nGbcs = 4
# nAnfsPerGbc=30
nAnfsPerInputFile = 40
nGbcsCoPerLson = 8 # Gjoni et al. 2018
nSbcsIpPerLson = 40 # Gjoni et al. 2018
# sbCoSpkFile = inputSpkFileTuple[0]
# sbIpSpkFile = lsoInputSpkFileTuple[1]
# gbCoSpkFile = lsoInputSpkFileTuple[2]
# gbIpSpkFile = inputSpkFileTuple[3]
anCoSpkFile = lsoInputSpkFileTuple[0]
anIpSpkFile = lsoInputSpkFileTuple[1]
# sbCoIdxSpktimeArray = np.loadtxt(sbCoSpkFile)
# sbCoCellIndices = sbCoIdxSpktimeArray[:, 0].astype(int)
# sbCoSpkTimes = sbCoIdxSpktimeArray[:, 1] * ms
# sbCoSpkGenGrp = SpikeGeneratorGroup(nSbcsCo, sbCoCellIndices, sbCoSpkTimes)
gbCoIdxSpktimeArray = np.loadtxt(anCoSpkFile)
gbCoCellIndices = gbCoIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
gbCoSpkTimes = gbCoIdxSpktimeArray[:, 1] * 1000. * ms
gbCoSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, gbCoCellIndices,
gbCoSpkTimes)
sbIpIdxSpktimeArray = np.loadtxt(anIpSpkFile)
sbIpCellIndices = sbIpIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
sbIpSpkTimes = sbIpIdxSpktimeArray[:, 1] * 1000. * ms
sbIpSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, sbIpCellIndices,
sbIpSpkTimes)
# gbIpIdxSpktimeArray = np.loadtxt(gbIpSpkFile)
# gbIpCellIndices = gbIpIdxSpktimeArray[:, 0].astype(int)
# gbIpSpkTimes = gbIpIdxSpktimeArray[:, 1] * ms
# gbIpSpkGenGrp = SpikeGeneratorGroup(nGbcsIp, gbIpCellIndices, gbIpSpkTimes)
# anfIdxSpktimeArray = np.loadtxt(anfSpkFile)
# anfIndices = anfIdxSpktimeArray[:, 0].astype(int)
# nANF = 132
# #anfSpkTimes = [i * second for i in anfIdxSpktimeArray[:, 1]]
# anfSpkTimes = anfIdxSpktimeArray[:, 1] * 1000*ms
# anfSpkGeneratorGrp = SpikeGeneratorGroup(nANF, anfIndices, anfSpkTimes)
# Membrane and Ion-Channel parameters
C = 12 * pF
EH = -43 * mV
EK = -70 * mV # -77*mV in mod file
EL = -65 * mV
ENa = 55 * mV # 55*mV in RM2003; 50*mv by Brette
nf = 0.85 # proportion of n vs p kinetics
zss = 0.5 # steady state inactivation of glt
# default temp_degC = 37., human body temperature in degree celcius
# q10 for ion-channel time-constants (RM2003, p.3106):
q10 = 3.**((temp_degC - 22.) / 10.)
# q10 for ion-channel gbar parameters (RM2003, p.3106):
q10gbar = 2.**((temp_degC - 22.) / 10.)
# hcno current (octopus cell)
frac = 0.0
qt = 4.5**((temp_degC - 33.) / 10.)
# Synaptic parameters:
Es_e = 0. * mV
tausE = 0.5 * ms
Es_i = -90 * mV
tausI = 1.0 * ms
'''Synaptic weights are unitless according to Brian2.
The effective unit is siemens, so they can work in amp, volt, siemens eqns.
We multiply synaptic weight w_e by unit siemens when adding it to g_e.
We use a local variable w_e for synaptic weight rather than the standard w:'''
w_elson = 5e-9
w_ilson = 50e-9 # 6e-9 @ 200Hz; 12e-9 @ 600 Hz
'''Here's why:
The synapses sbc3SynE.w synaptic weight references the Same State Variable as
as the neuron group sbc3Grp.w (klt activation w).
Update sbc3Grp.w, and you set sbc3SynE.w to same value, and vice versa.
This way klt activation and synaptic weight are identical and quite ridiculous.
So use a local variable other than w for the synaptic weight!'''
# Maximal conductances of different cell types in nS
maximal_conductances = dict(
type1c=(1000, 150, 0, 0, 0.5, 0, 2),
type1t=(1000, 80, 0, 65, 0.5, 0, 2),
type12=(1000, 150, 20, 0, 2, 0, 2),
type21=(1000, 150, 35, 0, 3.5, 0, 2),
type2=(1000, 150, 200, 0, 20, 0, 2),
type2g1p5x=(1000, 150, 300, 0, 30, 0, 2),
type2g0p5x=(1000, 150, 100, 0, 10, 0, 2),
type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell
)
gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [
x * nS for x in maximal_conductances[neuron_type]
]
# Classical Na channel
eqs_na = """
ina = gnabar*m**3*h*(ENa-v) : amp
dm/dt=q10*(minf-m)/mtau : 1
dh/dt=q10*(hinf-h)/htau : 1
minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1
hinf = 1./(1+exp((vu + 65.) / 6.)) : 1
mtau = ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : second
htau = ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : second
"""
# KHT channel (delayed-rectifier K+)
eqs_kht = """
ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp
dn/dt=q10*(ninf-n)/ntau : 1
dp/dt=q10*(pinf-p)/ptau : 1
ninf = (1 + exp(-(vu + 15) / 5.))**-0.5 : 1
pinf = 1. / (1 + exp(-(vu + 23) / 6.)) : 1
ntau = ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : second
ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : second
"""
# Ih channel (subthreshold adaptive, non-inactivating)
eqs_ih = """
ih = ghbar*r*(EH-v) : amp
dr/dt=q10*(rinf-r)/rtau : 1
rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1
rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : second
"""
# KLT channel (low threshold K+)
eqs_klt = """
iklt = gkltbar*w**4*z*(EK-v) : amp
dw/dt=q10*(winf-w)/wtau : 1
dz/dt=q10*(zinf-z)/ztau : 1
winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1
zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1
wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : second
ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : second
"""
# Ka channel (transient K+)
eqs_ka = """
ika = gkabar*a**4*b*c*(EK-v): amp
da/dt=q10*(ainf-a)/atau : 1
db/dt=q10*(binf-b)/btau : 1
dc/dt=q10*(cinf-c)/ctau : 1
ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1
binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
atau = ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : second
btau = ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : second
ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : second
"""
# Leak
eqs_leak = """
ileak = gl*(EL-v) : amp
"""
# h current for octopus cells
eqs_hcno = """
ihcno = gbarno*(h1*frac + h2*(1-frac))*(EH-v) : amp
dh1/dt=(hinfno-h1)/tau1 : 1
dh2/dt=(hinfno-h2)/tau2 : 1
hinfno = 1./(1+exp((vu+66.)/7.)) : 1
tau1 = bet1/(qt*0.008*(1+alp1))*ms : second
tau2 = bet2/(qt*0.0029*(1+alp2))*ms : second
alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
"""
eqs = """
dv/dt = (ileak + ina + ikht + iklt + ika + ih + ihcno + I + Is_e + Is_i)/C : volt
Is_e = gs_e * (Es_e - v) : amp
gs_e : siemens
Is_i = gs_i * (Es_i - v) : amp
gs_i : siemens
vu = v/mV : 1 # unitless v
I : amp
"""
#Added Is_i to RM2003
eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno
lsonGrp = NeuronGroup(nLsons,
eqs,
method='exponential_euler',
threshold='v > -30*mV',
refractory='v > -45*mV')
#gbcGrp.I = 2500.0*pA
lsonGrp.I = 0.0 * pA
# Initialize model near v_rest with no inputs
lsonGrp.v = Vrest
#vu = EL/mV # unitless v
vu = lsonGrp.v / mV # unitless v
lsonGrp.m = 1. / (1 + exp(-(vu + 38.) / 7.))
lsonGrp.h = 1. / (1 + exp((vu + 65.) / 6.))
lsonGrp.n = (1 + exp(-(vu + 15) / 5.))**-0.5
lsonGrp.p = 1. / (1 + exp(-(vu + 23) / 6.))
lsonGrp.r = 1. / (1 + exp((vu + 76.) / 7.))
lsonGrp.w = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25
lsonGrp.z = zss + ((1. - zss) / (1 + exp((vu + 71.) / 10.)))
lsonGrp.a = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25
lsonGrp.b = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.c = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.h1 = 1. / (1 + exp((vu + 66.) / 7.))
lsonGrp.h2 = 1. / (1 + exp((vu + 66.) / 7.))
#lsonGrp.gs_e = 0.0*siemens
#netGbcEq = Network(gbcGrp, report='text')
#netGbcEq.run(50*ms, report='text')
lsonSynI = Synapses(gbCoSpkGenGrp,
lsonGrp,
model='''dg_i/dt = -g_i/tausI : siemens (clock-driven)
gs_i_post = g_i : siemens (summed)''',
on_pre='g_i += w_ilson*siemens',
method='exact')
lsonSynI.connect(i=np.arange(nGbcsCoPerLson), j=0)
lsonSynE = Synapses(sbIpSpkGenGrp,
lsonGrp,
model='''dg_e/dt = -g_e/tausE : siemens (clock-driven)
gs_e_post = g_e : siemens (summed)''',
on_pre='g_e += w_elson*siemens',
method='exact')
lsonSynE.connect(i=np.arange(nSbcsIpPerLson), j=0)
lsonSpks = SpikeMonitor(lsonGrp)
lsonState = StateMonitor(lsonGrp, ['v', 'gs_e'], record=True)
run(300 * ms, report='text')
# Console Output Won't Clear from Script
# Memory issue with so many repeated simulations:
# Comment out the plt commands
#plt.plot(lsonState.t / ms, lsonState[0].v / mV)
#plt.xlabel('t (ms)')
#plt.ylabel('v (mV)')
#plt.show()
# Output file - EIPD in output filename. Spiketimes in file
EPhsStrCo = anCoSpkFile[27:31]
EPhsStrIp = anIpSpkFile[27:31]
if (EPhsStrCo[0] == 'N'):
EPhsIntCo = -1 * int(EPhsStrCo[1:4])
else:
EPhsIntCo = int(EPhsStrCo[0:3])
if (EPhsStrIp[0] == 'N'):
EPhsIntIp = -1 * int(EPhsStrIp[1:4])
else:
EPhsIntIp = int(EPhsStrIp[0:3])
# EIPD = (EPhsIntCo - EPhsIntIp) % 360
EIPDint = (EPhsIntCo - EPhsIntIp) # unwrapped Envelope IPD
#EIPDstr = str(EIPDint)
if (EIPDint == 15):
EIPDstr = 'EIPDP015'
elif (EIPDint == 30):
EIPDstr = 'EIPDP030'
elif (EIPDint == 45):
EIPDstr = 'EIPDP045'
elif (EIPDint == 60):
EIPDstr = 'EIPDP060'
elif (EIPDint == 75):
EIPDstr = 'EIPDP075'
elif (EIPDint == 90):
EIPDstr = 'EIPDP090'
elif (EIPDint == -15):
EIPDstr = 'EIPDN015'
elif (EIPDint == -30):
EIPDstr = 'EIPDN030'
elif (EIPDint == -45):
EIPDstr = 'EIPDN045'
elif (EIPDint == -60):
EIPDstr = 'EIPDN060'
elif (EIPDint == -75):
EIPDstr = 'EIPDN075'
elif (EIPDint == -90):
EIPDstr = 'EIPDN090'
elif (EIPDint > 0):
EIPDstr = 'EIPDP' + str(EIPDint)
elif (EIPDint < 0):
EIPDstr = 'EIPDN' + str(-EIPDint)
elif (EIPDint == 0):
EIPDstr = 'EIPDP000'
# if (EIPDint < 0):
# EIPDstr = EIPDstr.replace('-','N')
# Synaptic parameters in output filename
if (abs(round(tausE / ms) - (tausE / ms)) < 0.1):
Te = str(round(tausE / ms))
else:
Te = str(tausE / ms)
Te = Te.replace('.', 'p')
if (abs(round(w_elson / 1e-9) - (w_elson / 1e-9)) < 0.1):
We = str(round(w_elson / 1e-9))
else:
We = str(w_elson / 1e-9)
We = We.replace('.', 'p')
if (abs(round(tausI / ms) - (tausI / ms)) < 0.1):
Ti = str(round(tausI / ms))
else:
Ti = str(tausI / ms)
Ti = Ti.replace('.', 'p')
if (abs(round(w_ilson / 1e-9) - (w_ilson / 1e-9)) < 0.1):
Wi = str(round(w_ilson / 1e-9))
else:
Wi = str(w_ilson / 1e-9)
Wi = Wi.replace('.', 'p')
lsonSpkFile = 'Lso2SpTms' + anCoSpkFile[6:13] + anCoSpkFile[
16:
23] + 'Te' + Te + 'We' + We + 'Ti' + Ti + 'Wi' + Wi + EIPDstr + 'Co' + anCoSpkFile[
38:40] + anCoSpkFile[23:31] + anCoSpkFile[45:]
file0 = open(lsonSpkFile, 'w')
for index in range(len(lsonSpks.t)):
file0.write(
str(lsonSpks.i[index]) + " " + str(lsonSpks.t[index] / ms) + '\n')
file0.close()
return (lsonGrp, lsonSpks, lsonState)
# end of mkgbcs
class BrianerClass(BaseClass):
def default_init(
self,
_BrianingNeurongroupDict=None,
_BrianingSynapsesDict=None,
_BrianingConnectVariable=None,
_BrianingTraceDict=None,
_BrianingMoniterTuple=None,
_BrianingSpikesDict=None,
_BrianingPyplotDict=None,
_BrianingTimeQuantityStr="ms",
_BrianingPyplotBool=True,
_BrianingStepTimeFloat=0.1,
_BrianingDebugVariable=0,
_BrianingRecordBool=True,
_BrianedTimeQuantityVariable=None,
_BrianedNetworkVariable=None,
_BrianedNeurongroupVariable=None,
_BrianedSynapsesVariable=None,
_BrianedStateMonitorVariable=None,
_BrianedSpikeMonitorVariable=None,
_BrianedClockVariable=None,
_BrianedParentSingularStr=None,
_BrianedRecordKeyStrsList=None,
_BrianedTraceDeriveBrianersList=None,
_BrianedSynapsesDeriveBrianersList=None,
_BrianedStateDeriveBrianersList=None,
_BrianedSpikeDeriveBrianersList=None,
_BrianedParentNetworkDeriveBrianerVariable=None,
_BrianedParentPopulationDeriveBrianerVariable=None,
_BrianedParentInteractomeDeriveBrianerVariable=None,
_BrianedParentDeriveRecorderVariable=None,
**_KwargVariablesDict
):
# Call the parent __init__ method
BaseClass.__init__(self, **_KwargVariablesDict)
def do_brian(self):
# /#################/#
# Determine if it is an inside structure or the top
#
# debug
"""
self.debug(
[
'We brian here',
'First look for deeper teams in the structure',
]
)
"""
# Check
if self.ParentedTotalSingularListDict != None and len(self.ParentedTotalSingularListDict) > 0:
# debug
"""
self.debug(
[
'self.ParentedTotalSingularListDict.keys() is ',
str(self.ParentedTotalSingularListDict.keys())
]
)
"""
# get
self.BrianedParentSingularStr = self.ParentedTotalSingularListDict.keys()[0]
# debug
"""
self.debug(
[
'Ok',
('self.',self,[
'BrianedParentSingularStr'
])
]
)
"""
# /########################/#
# Network level
#
# Check
if (
self.ParentDeriveTeamerVariable == None
or "Populations" in self.TeamDict
or self.ParentDeriveTeamerVariable.TeamTagStr
not in ["Clocks", "Traces", "Samples", "Events", "Interactomes", "Interactions"]
) and self.BrianedParentSingularStr != "Population":
# debug
"""
self.debug(
[
'It is a Network level',
'We set the brian network'
]
)
"""
# set
self.brianNetwork()
# /########################/#
# Special Network-Neurongroup level
#
# Check
if "Populations" not in self.TeamDict:
# debug
"""
self.debug(
[
'It is a network with a one level pop',
'So set the neurongroup'
]
)
"""
# brianPopulation
self.brianPopulation()
# debug
"""
self.debug(
[
'We end to set the neuron group here'
]
)
"""
# /########################/#
# structure
#
# debug
"""
self.debug(
[
'We brian structure in all the brian children...',
]
)
"""
# structure
self.structure(
["Clocks", "Populations", "Traces", "Events", "Samples", "Interactomes", "Interactions"],
"#all",
_ManagerCommandSetList=["brian"],
)
# debug
"""
self.debug(
[
'Ok we have brian structured all the brian children...',
]
)
"""
else:
# debug
"""
self.debug(
[
'Ok we check if this parentsingular has a special method ',
('self.',self,[
'BrianedParentSingularStr'
])
]
)
"""
# set
BrianedMethodKeyStr = "brian" + self.BrianedParentSingularStr
# Check
if hasattr(self, BrianedMethodKeyStr):
# /########################/#
# call the special brian<BrianedParentSingularStr> method
#
# debug
"""
self.debug(
[
'It is a '+self.BrianedParentSingularStr+' level',
'We brian<BrianedParentSingularStr>'
]
)
"""
# call
getattr(self, BrianedMethodKeyStr)()
# debug
"""
self.debug(
[
'Ok we have setted brian'+self.BrianedParentSingularStr
]
)
"""
# debug
"""
self.debug(
[
'end of brian here'
]
)
"""
def brianNetwork(self):
# /####################/#
# init the Network
#
# maybe should import
import brian2
# set
self.BrianedNetworkVariable = brian2.Network()
# get
self.BrianedTimeQuantityVariable = getattr(brian2, self.BrianingTimeQuantityStr)
# /####################/#
# init a simulation clock
#
# debug
"""
self.debug(
[
'We set a simulation clock at least'
]
)
"""
# Check
if "Clocks" not in self.TeamDict:
ClocksDeriveManager = self.team("Clocks").TeamedValueVariable
else:
ClocksDeriveManager = self.TeamDict["Clocks"]
# manage
if "Simulation" not in ClocksDeriveManager.ManagementDict:
# debug
"""
self.debug(
[
'We init a simulation clock here'
]
)
"""
# manage
SimulationDeriveBrianer = ClocksDeriveManager.manage(
"Simulation", {"BrianingStepTimeFloat": self.BrianingStepTimeFloat}
)
def brianClock(self):
# /####################/#
# Determine the parents
#
# get
self.BrianedParentNetworkDeriveBrianerVariable = self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
# /####################/#
# Set the brian clock
#
# import
from brian2 import Clock
# debug
"""
self.debug(
[
'We set the brian clock',
('self.',self,['StructureTagStr'])
]
)
"""
# init
self.BrianedClockVariable = Clock(
dt=self.BrianingStepTimeFloat * self.BrianedParentNetworkDeriveBrianerVariable.BrianedTimeQuantityVariable,
# name=self.StructureTagStr
)
# debug
"""
self.debug(
[
'We have setted the clock',
('self.',self,[
'BrianedClockVariable'
])
]
)
"""
def brianPopulation(self):
# debug
"""
self.debug(
[
'It is a Neurongroup level, we set the Neurongroup',
'We adapt the shape of BrianingNeurongroupDict',
('self.',self,[
'BrianingNeurongroupDict',
'RecordingKeyVariable'
])
]
)
"""
# /########################/#
# Determine parents
#
# Check
if self.ParentDeriveTeamerVariable != None:
# set the parent
self.BrianedParentNetworkDeriveBrianerVariable = self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
else:
# set the parent
self.BrianedParentNetworkDeriveBrianerVariable = self
# /################/#
# Adapt the arg
#
# Check
if "N" not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict["N"] = 0
# Check
if "model" not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict["model"] = ""
# maybe should import
from brian2 import NeuronGroup
# debug
"""
self.debug(
[
('self.',self,[
'BrianingNeurongroupDict'
]),
'We now set the model system Neurongroup if N>0 and model!=""'
]
)
"""
# /################/#
# Set the brian neurongroup
#
# Check
if self.BrianingNeurongroupDict["N"] > 0 and self.BrianingNeurongroupDict["model"] != "":
# init
self.BrianedNeurongroupVariable = NeuronGroup(
**dict(
self.BrianingNeurongroupDict,
**{
#'name':self.ParentedTotalPathStr.replace('/','_')+'_'+self.ManagementTagStr,
#'clock':self.BrianedParentNetworkDeriveBrianerVariable.TeamDict[
# 'Clocks'
# ].ManagementDict['Simulation'].BrianedClockVariable
}
)
)
# debug
"""
self.debug(
[
'Ok we have setted the Neurongroup',
('self.',self,[
'BrianedNeurongroupVariable'
])
]
)
"""
# /##################/#
# Define a debug
#
# Check
if self.BrianingDebugVariable > 0:
# import
from brian2 import network_operation, ms
@network_operation(
dt=self.BrianingDebugVariable
* self.BrianedParentNetworkDeriveBrianerVariable.BrianedTimeQuantityVariable
)
def debugNeurongroup():
# init
PrintStr = "At time t=" + str(self.BrianedNeurongroupVariable.clock.t) + ", \n"
PrintStr += "In the NeuronGroup " + self.BrianedNeurongroupVariable.name + " : \n"
# loop
for __KeyStr in self.BrianedNeurongroupVariable.equations._equations.keys():
# set
PrintStr += __KeyStr + " is " + str(getattr(self.BrianedNeurongroupVariable, __KeyStr)) + "\n"
# add
PrintStr += "\n"
# print
print PrintStr
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(debugNeurongroup)
# Check
if self.BrianingNeurongroupDict["N"] > 0:
# /##################/#
# team States first all the brian variables
#
# get
self.BrianedRecordKeyStrsList = self.BrianedNeurongroupVariable.equations._equations.keys()
# Check
if len(self.BrianedRecordKeyStrsList) > 0:
# debug
"""
self.debug(
[
'We simulate with neurongroup',
'adapt the initial conditions of all the brian variables',
'so first we team Traces and put Recorders inside or get it and mapSet'
]
)
"""
# Check
if "Traces" not in self.TeamDict:
BrianedDeriveTraces = self.team("Traces").TeamedValueVariable
else:
BrianedDeriveTraces = self.TeamDict["Traces"]
# debug
"""
self.debug(
[
'We set the tracers',
('self.',self,['BrianedRecordKeyStrsList'])
]
)
"""
# map
self.BrianedTraceDeriveBrianersList = map(
lambda __ManagementKeyStr, __RecordKeyStr: BrianedDeriveTraces.manage(
__ManagementKeyStr,
{
"RecordingKeyVariable": getattr(self.BrianedNeurongroupVariable, __RecordKeyStr),
"RecordKeyStr": __RecordKeyStr,
},
).ManagedValueVariable
if __ManagementKeyStr not in BrianedDeriveTraces.ManagementDict
else BrianedDeriveTraces.ManagementDict[__ManagementKeyStr].mapSet(
{
"RecordingKeyVariable": getattr(self.BrianedNeurongroupVariable, __RecordKeyStr),
"RecordKeyStr": __RecordKeyStr,
}
),
map(
lambda __BrianedRecordKeyStr: Recorder.RecordPrefixStr + __BrianedRecordKeyStr,
self.BrianedRecordKeyStrsList,
),
self.BrianedRecordKeyStrsList,
)
# /##################/#
# add in the net
#
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(self.BrianedNeurongroupVariable)
"""
#/####################/#
# maybe view a draw plot
#
#call
self.viewPopulation()
"""
# debug
"""
self.debug(
[
'End of brianPopulation'
]
)
"""
def brianInteraction(self):
# /########################/#
# Postlet level
#
# debug
"""
self.debug(
[
'It is an Interaction level',
('self.',self,[
#'BrianingSynapsesDict'
]
)
]
)
"""
# /####################/#
# Set the parent
#
# Check
if self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable.BrianedParentSingularStr == "Interactome":
# debug
"""
self.debug(
[
'We are in a projectome structure'
]
)
"""
# set
self.BrianedParentInteractomeDeriveBrianerVariable = (
self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
# get
self.BrianedParentPopulationDeriveBrianerVariable = (
self.BrianedParentInteractomeDeriveBrianerVariable.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
else:
# debug
"""
self.debug(
[
'There is no projectome structure'
]
)
"""
# get
self.BrianedParentPopulationDeriveBrianerVariable = (
self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
)
# get
self.BrianedParentNetworkDeriveBrianerVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
)
# /####################/#
# Set the ConnectedTo Variable
#
# debug
"""
self.debug(
[
'Check if we have to get the connected to variable',
('self.',self,['ConnectedToVariable'])
]
)
"""
# Check
if self.ConnectedToVariable == None:
# debug
"""
self.debug(
[
'We setConnection here'
]
)
"""
# setConnection
self.setConnection(self.ManagementTagStr, self, self.BrianedParentPopulationDeriveBrianerVariable)
# /####################/#
# Set the BrianedParentPopulationDeriveBrianerVariable
#
# debug
"""
self.debug(
[
'Do we have to make parent-brian the connected variable ?',
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable)
]
)
"""
# Check
if self.ConnectedToVariable.BrianedNeurongroupVariable == None:
# parent brian
self.ConnectedToVariable.parent().brian()
# set
BrianedNameStr = (
self.BrianedParentPopulationDeriveBrianerVariable.StructureTagStr
+ "_To_"
+ self.ConnectedToVariable.StructureTagStr
)
# debug
"""
self.debug(
[
'We set the synapses',
'self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable),
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable),
'Maybe we have to make brian the post',
'BrianedNameStr is '+BrianedNameStr
]
)
"""
# import
from brian2 import Synapses
# init
self.BrianedSynapsesVariable = Synapses(
source=self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable,
target=self.ConnectedToVariable.BrianedNeurongroupVariable,
# name=BrianedNameStr.replace('/','_'),
**self.BrianingSynapsesDict
)
# /####################/#
# Connect options
#
# connect
if type(self.BrianingConnectVariable) == float:
# debug
"""
self.debug(
[
'we connect with a sparsity of ',
('self.',self,[
'BrianingConnectVariable'
])
]
)
"""
# connect
self.BrianedSynapsesVariable.connect(True, p=self.BrianingConnectVariable)
# /####################/#
# add to the structure
#
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(self.BrianedSynapsesVariable)
# /##################/#
# Define a debug
#
# Check
if self.BrianingDebugVariable > 0:
# import
from brian2 import network_operation, ms
@network_operation(
dt=self.BrianingDebugVariable
* self.BrianedParentNetworkDeriveBrianerVariable.BrianedTimeQuantityVariable
)
def debugSynapses():
# init
PrintStr = "At time t=" + str(self.BrianedSynapsesVariable.clock.t) + ", \n"
PrintStr += "In the Synapses " + self.BrianedSynapsesVariable.name + " : \n"
# loop
for __KeyStr in self.BrianedSynapsesVariable.equations._equations.keys():
# set
PrintStr += __KeyStr + " is " + str(getattr(self.BrianedSynapsesVariable, __KeyStr)) + "\n"
# add
PrintStr += "\n"
# print
print PrintStr
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(debugSynapses)
def brianTrace(self):
# debug
"""
self.debug(
[
'It is a Trace level, we set the Samples',
('self.',self,[
#'RecordingKeyVariable',
'RecordKeyStr'
])
]
)
"""
# /####################/#
# Set the parent
#
# get
self.BrianedParentPopulationDeriveBrianerVariable = self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
# get
self.BrianedParentNetworkDeriveBrianerVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
)
# Check
if self.BrianedParentPopulationDeriveBrianerVariable.BrianingNeurongroupDict["N"] > 0:
# /####################/#
# we record
#
# Check
if self.BrianingRecordBool:
# debug
"""
self.debug(
[
'We record here'
]
)
"""
# record
self.record()
# /###################/#
# Build the samples and maybe one default moniter
#
# debug
"""
self.debug(
[
'Look if we have samples here',
"'Samples' not in self.TeamDict is ",
str('Samples' not in self.TeamDict)
]
)
"""
# Check
if "Samples" not in self.TeamDict:
BrianedSamplesDeriveManager = self.team("Samples").TeamedValueVariable
else:
BrianedSamplesDeriveManager = self.TeamDict["Samples"]
# debug
"""
self.debug(
[
'Do we have to set a default moniter ?',
#'len(self.BrianedParentPopulationDeriveBrianerVariable.BrianedRecordKeyStrsList) is ',
#str(len(self.BrianedParentPopulationDeriveBrianerVariable.BrianedRecordKeyStrsList)),
'self.BrianedParentPopulationDeriveBrianerVariable.BrianedRecordKeyStrsList) is ',
str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedRecordKeyStrsList),
]
)
"""
# Check
if len(self.BrianedParentPopulationDeriveBrianerVariable.BrianedRecordKeyStrsList) == 1:
# debug
"""
self.debug(
[
'BrianedSamplesDeriveManager.ManagementDict.keys() is',
str(BrianedSamplesDeriveManager.ManagementDict.keys())
]
)
"""
# Check
if len(BrianedSamplesDeriveManager.ManagementDict) == 0 or (
len(BrianedSamplesDeriveManager.ManagementDict) == 1
and "Default" in BrianedSamplesDeriveManager.ManagementDict
):
# debug
"""
self.debug(
[
'There is just one variable that we sample',
'we manage and make it brian'
]
)
"""
# manage
BrianedDefaultBrianer = BrianedSamplesDeriveManager.manage("Default").ManagedValueVariable
# Check
if BrianedDefaultBrianer.RecordingLabelVariable == None:
# Check
if self.BrianedParentPopulationDeriveBrianerVariable.RecordingLabelVariable != None:
# get
BrianedDefaultBrianer.RecordingLabelVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.RecordingLabelVariable
)
else:
# set the record labels
BrianedDefaultBrianer.RecordingLabelVariable = (
[0]
if self.BrianedParentPopulationDeriveBrianerVariable.BrianingNeurongroupDict["N"] > 0
else []
)
# brian
BrianedDefaultBrianer.parent().brian()
def brianSample(self):
# debug
"""
self.debug(
[
'It is a Sample State Moniter level',
('self.',self,[
'RecordingLabelVariable',
])
]
)
"""
# /####################/#
# Set the parent
#
# get
self.BrianedParentDeriveRecorderVariable = self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
# get
self.BrianedParentPopulationDeriveBrianerVariable = (
self.BrianedParentDeriveRecorderVariable.BrianedParentPopulationDeriveBrianerVariable
)
# get
self.BrianedParentNetworkDeriveBrianerVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
)
# Check
if self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable != None:
# /####################/#
# Set the brian monitor
#
# debug
"""
self.debug(
[
'We set the state monitor',
('self.',self,[
#'BrianedParentPopulationDeriveBrianerVariable'
]),
#'self.BrianedParentDeriveRecorderVariable.RecordKeyStr is ',
#str(self.BrianedParentDeriveRecorderVariable.RecordKeyStr)
'self.ParentedTotalManagementOrderedDict.keys() is ',
str(self.ParentedTotalManagementOrderedDict.keys())
]
)
"""
# import
from brian2 import StateMonitor
# init
self.BrianedStateMonitorVariable = StateMonitor(
self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable,
self.BrianedParentDeriveRecorderVariable.RecordKeyStr,
self.RecordingLabelVariable,
)
# debug
"""
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedStateMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable)
]
)
"""
# /####################/#
# add to the structure
#
# debug
"""
self.debug(
[
'We add to the structure'
]
)
"""
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(self.BrianedStateMonitorVariable)
"""
#/####################/#
# maybe view a draw plot
#
#call
self.viewSample()
"""
def brianEvent(self):
# debug
"""
self.debug(
[
'It is a Spike Moniter level',
('self.',self,[
])
]
)
"""
# /####################/#
# Set the BrianedParentPopulationDeriveBrianerVariable
#
# get
self.BrianedParentPopulationDeriveBrianerVariable = self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
# get
self.BrianedParentNetworkDeriveBrianerVariable = (
self.BrianedParentPopulationDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
)
# Check
if self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable != None:
# /####################/#
# Set the brian monitor
#
# debug
"""
self.debug(
[
'We set the spike monitor'
]
)
"""
# import
from brian2 import SpikeMonitor
# init
self.BrianedSpikeMonitorVariable = SpikeMonitor(
self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable
)
# debug
"""
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedSpikeMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentPopulationDeriveBrianerVariable.BrianedNetworkVariable is ',
str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedNetworkVariable)
]
)
"""
# /####################/#
# add to the structure
#
# add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(self.BrianedSpikeMonitorVariable)
"""
#/####################/#
# maybe view a draw plot
#
#call
self.viewEvent()
"""
def mimic_view(self):
# /########################/#
# Network level
#
# Check
if (
self.ParentDeriveTeamerVariable == None
or "Populations" in self.TeamDict
or self.ParentDeriveTeamerVariable.TeamTagStr
not in ["Clocks", "Traces", "Samples", "Events", "Interactomes", "Interactions"]
) and self.BrianedParentSingularStr != "Population":
# debug
"""
self.debug(
[
'It is a Network level',
'We sructure view'
]
)
"""
# /########################/#
# structure
#
# debug
"""
self.debug(
[
'We view structure in all the brian children...',
]
)
"""
# structure
self.structure(
["Clocks", "Populations", "Traces", "Events", "Samples", "Interactomes", "Interactions"],
"#all",
_ManagerCommandSetList=["view"],
)
# debug
"""
self.debug(
[
'Ok we have view structured all the brian children...',
]
)
"""
else:
# debug
"""
self.debug(
[
'Ok we check if this parentsingular has a special method ',
('self.',self,[
'BrianedParentSingularStr'
])
]
)
"""
# set
BrianedMethodKeyStr = "view" + self.BrianedParentSingularStr
# Check
if hasattr(self, BrianedMethodKeyStr):
# /########################/#
# call the special view<BrianedParentSingularStr> method
#
# debug
"""
self.debug(
[
'It is a '+self.BrianedParentSingularStr+' level',
'We view<BrianedParentSingularStr>'
]
)
"""
# call
getattr(self, BrianedMethodKeyStr)()
# debug
"""
self.debug(
[
'Ok we have setted view'+self.BrianedParentSingularStr
]
)
"""
def viewPopulation(self):
# debug
"""
self.debug(
[
'We complete a view so first fill the draw'
]
)
"""
# Check
if "Charts" not in self.TeamDict:
BrianedChartsDeriveTeamer = self.team("Charts").TeamedValueVariable
else:
BrianedChartsDeriveTeamer = self.TeamDict["Charts"]
def viewSample(self):
# debug
self.debug(
[
"We complete a view so first fill the draw",
("self.", self, ["RecordingLabelVariable", "ViewedLegendLabelStr"]),
]
)
# /##################/#
# Determine the way of labeling the variable names
#
# set
if self.ViewedLegendLabelStr == "":
# set
self.ViewedLegendLabelStr = "$" + self.BrianedParentDeriveRecorderVariable.RecordKeyStr
"""
self.ViewedLegendLabelStr+='_{'+str(
#self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable.name
#self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable.
).replace('_','/')+'}'
"""
# debug
self.debug(["We have specified the legend label", ("self.", self, ["ViewedLegendLabelStr"])])
# set
self.PyplotingDrawVariable = map(
lambda __IndexInt: (
"plot",
{
"#liarg:#map@get": [
"#IdGetStr.BrianedStateMonitorVariable.t",
">>SYS.IdDict[#IdStr].BrianedStateMonitorVariable."
+ self.BrianedParentDeriveRecorderVariable.RecordKeyStr
+ "["
+ str(__IndexInt)
+ ",:]",
],
"#kwarg": dict(
{
"label": self.ViewedLegendLabelStr + "^{" + str(__IndexInt) + "}$",
"linestyle": "-",
"color": "b",
},
**self.BrianingPyplotDict
),
},
),
self.RecordingLabelVariable,
)
# /####################/#
# maybe set for the Chart
#
# init
self.PyplotingChartVariable = []
# /####################/#
# maybe set the X Chart also
#
# get
BrianedTimeUnit = self.BrianedStateMonitorVariable.t.unit
# scale
# self.ViewingXVariable=self.BrianedStateMonitorVariable.t[:]
self.ViewingXVariable = self.BrianedStateMonitorVariable.t / (
# (1./self.ViewingXScaleFloat)*BrianedTimeUnit
BrianedTimeUnit
)
# set
self.ViewingXLabelStr = "$t\ (" + str((1.0 / self.ViewingXScaleFloat) * BrianedTimeUnit).split(".")[-1] + ")$"
# /####################/#
# maybe set the Y Chart also
#
# debug
self.debug(
[
"We set the y axis",
"self.BrianedParentDeriveRecorderVariable.RecordedTraceFloatsArray is ",
str(self.BrianedParentDeriveRecorderVariable.RecordedTraceFloatsArray),
]
)
# import
import brian2
# get
ViewingYVariable = getattr(
self.BrianedStateMonitorVariable, self.BrianedParentDeriveRecorderVariable.RecordKeyStr
)
# split
# BrianedDimensionStr=str(ViewingYVariable).split(' ')[-1]
BrianedActivityUnit = getattr(
self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable,
self.BrianedParentDeriveRecorderVariable.RecordKeyStr,
).unit
# divide
# self.ViewingYVariable=ViewingYVariable/getattr(
# brian2,BrianedDimensionStr
# )
self.ViewingYVariable = ViewingYVariable / (BrianedActivityUnit)
# self.ViewingYVariable=ViewingYVariable
self.ViewingYVariable = self.ViewingYVariable[:]
print (self.ViewingYVariable)
# set
self.ViewingYLabelStr = "$" + self.BrianedParentDeriveRecorderVariable.RecordKeyStr
"""
self.ViewingYLabelStr+='_{'+str(
self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable.name
).replace('_','/')+'}(t)\ ('+str(
self.BrianedParentDeriveRecorderVariable.RecordedTraceFloatsArray.unit
)+')'
"""
# self.ViewingYLabelStr+='\ ('+BrianedDimensionStr+')'
self.ViewingYLabelStr += "\ (" + str((1.0 / self.ViewingYScaleFloat) * BrianedActivityUnit).split(".")[-1] + ")"
self.ViewingYLabelStr += "$"
# /################/#
# call the base view method
#
# debug
self.debug([("self.", self, ["ViewingXVariable", "ViewingYVariable"]), "Now we call the view"])
# call
BaseClass.view(self)
# /####################/#
# maybe set global Chart also
#
self.PyplotingChartVariable += [
("tick_params", {"#kwarg": {"length": 10, "width": 5, "which": "major"}}),
("tick_params", {"#kwarg": {"length": 5, "width": 2, "which": "minor"}}),
("xaxis.set_ticks_position", {"#liarg": ["bottom"]}),
("yaxis.set_ticks_position", {"#liarg": ["left"]}),
(
"legend",
{
"#liarg": [],
"#kwarg": {
"fontsize": 10,
"shadow": True,
"fancybox": True,
"ncol": max(
1,
len(
getattr(
self.BrianedStateMonitorVariable,
self.BrianedParentDeriveRecorderVariable.RecordKeyStr,
)
)
/ 2,
),
"loc": 2,
"bbox_to_anchor": (1.05, 1),
},
},
),
]
# /####################/#
# maybe replace Chart also
#
# debug
"""
self.debug(
[
'Before replace',
('self.',self,[
'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
"""
# mapReplace
[self.PyplotingDrawVariable, self.PyplotingChartVariable] = map(
lambda __Variable: SYS.replace(
__Variable, {"#IdStr": str(self.PrintIdInt), "#IdGetStr": "#id:" + str(self.PrintIdInt)}, self
)
if __Variable != None
else None,
map(lambda __KeyStr: getattr(self, __KeyStr), ["PyplotingDrawVariable", "PyplotingChartVariable"]),
)
# debug
"""
self.debug(
[
'After replace',
('self.',self,[
#'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
"""
# /####################/#
# Update maybe the
# parent neuron group
# debug
self.debug(["Maybe we also update the view in the parent population"])
# Check
if "Charts" in self.BrianedParentPopulationDeriveBrianerVariable.TeamDict:
# get
BrianedChartsDeriveManager = self.BrianedParentPopulationDeriveBrianerVariable.TeamDict["Charts"]
# manage
BrianedChartDerivePyploter = BrianedChartsDeriveManager.manage(
self.BrianedParentDeriveRecorderVariable.ManagementTagStr
).ManagedValueVariable
# debug
"""
self.debug(
[
'We update in the parent neurongroup chart',
'BrianedChartDerivePyploter is ',
SYS._str(BrianedChartDerivePyploter),
('self.',self,[])
]
)
"""
# team
BrianedDrawDeriveManager = BrianedChartDerivePyploter.team("Draws").TeamedValueVariable
# manage
BrianedDrawDeriveManager.manage(
str(self.ManagementIndexInt), {"PyplotingDrawVariable": self.PyplotingDrawVariable}
)
def viewEvent(self):
# debug
"""
self.debug(
[
'We complete a view so first fill the draw'
]
)
"""
# set
self.ViewedLabelStr = (
"$"
+ self.ManagementTagStr
+ "_{"
+ str(self.BrianedParentPopulationDeriveBrianerVariable.BrianedNeurongroupVariable.name).replace("_", "/")
+ "}"
)
# set
self.PyplotingDrawVariable = [
(
"plot",
{
"#liarg:#map@get": [
"#IdGetStr.BrianedSpikeMonitorVariable.t",
">>SYS.IdDict[#IdStr].BrianedSpikeMonitorVariable.i",
],
"#kwarg": dict(
{"label": self.ViewedLabelStr, "linestyle": "", "marker": ".", "color": "b"},
**self.BrianingPyplotDict
),
},
)
]
# /####################/#
# maybe replace Chart also
#
# debug
"""
self.debug(
[
'Before replace',
('self.',self,[
'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
"""
# mapReplace
[self.PyplotingDrawVariable, self.PyplotingChartVariable] = map(
lambda __Variable: SYS.replace(
__Variable, {"#IdStr": str(self.PrintIdInt), "#IdGetStr": "#id:" + str(self.PrintIdInt)}, self
)
if __Variable != None
else None,
map(lambda __KeyStr: getattr(self, __KeyStr), ["PyplotingDrawVariable", "PyplotingChartVariable"]),
)
# debug
"""
self.debug(
[
'After replace',
('self.',self,[
#'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
"""
# /####################/#
# Update maybe the
# parent neuron group
# get
BrianedChartDeriveManager = self.BrianedParentPopulationDeriveBrianerVariable.TeamDict["Charts"]
# manage
BrianedChartDerivePyploter = BrianedChartDeriveManager.manage(self.ManagementTagStr).ManagedValueVariable
# debug
"""
self.debug(
[
'We update in the parent neurongroup chart',
'BrianedChartDerivePyploter is ',
SYS._str(BrianedChartDerivePyploter),
('self.',self,[])
]
)
"""
# team
BrianedDrawDeriveManager = BrianedChartDerivePyploter.team("Draws").TeamedValueVariable
# manage
BrianedDrawDeriveManager.manage(
str(self.ManagementIndexInt), {"PyplotingDrawVariable": self.PyplotingDrawVariable}
)
def mimic_simulate(self):
# parent method
BaseClass.simulate(self)
# debug
"""
self.debug('We start simulate in brian')
"""
# run with the brian method
self.BrianedNetworkVariable.run(self.SimulatingStopTimeFloat * self.BrianedTimeQuantityVariable)
# debug
"""
self.debug('We stop running in brian')
"""
def mimic_record(self):
# base method
BaseClass.record(self)
# debug
"""
self.debug(
[
'We have traced, alias the init in the brian object',
('self.',self,[
'RecordedTraceFloatsArray',
'RecordedInitFloatsArray'
])
]
)
"""
# alias
self.RecordedTraceFloatsArray[:] = self.RecordedInitFloatsArray * self.RecordedTraceFloatsArray.unit
# debug
"""
self.debug(
[
('self.',self,['RecordedTraceFloatsArray'])
]
)
"""
def mimic__print(self, **_KwargVariablesDict):
# /##################/#
# Modify the printing Variable
#
# Check
if self.PrintingSelfBool:
# /##################/#
# Remove the brian objects that are non setted
#
# map
map(
lambda __KeyStr: self.forcePrint([__KeyStr], "BrianerClass")
if getattr(self.PrintingCopyVariable, __KeyStr) != None
else None,
[
"BrianedNetworkVariable",
"BrianedNeurongroupVariable",
"BrianedSynapsesVariable",
"BrianedStateMonitorVariable",
"BrianedSpikeMonitorVariable",
"BrianedClockVariable",
],
)
# /##################/#
# Call the base method
#
# call
BaseClass._print(self, **_KwargVariablesDict)
def sim_decision_making_network(
N_Excit=384,
N_Inhib=96,
weight_scaling_factor=5.33,
t_stimulus_start=100 * b2.ms,
t_stimulus_duration=9999 * b2.ms,
coherence_level=0.,
stimulus_update_interval=30 * b2.ms,
mu0_mean_stimulus_Hz=160.,
stimulus_std_Hz=20.,
N_extern=1000,
firing_rate_extern=9.8 * b2.Hz,
w_pos=1.90,
f_Subpop_size=0.25, # .15 in publication [1]
max_sim_time=1000. * b2.ms,
stop_condition_rate=None,
monitored_subset_size=512):
"""
Args:
N_Excit (int): total number of neurons in the excitatory population
N_Inhib (int): nr of neurons in the inhibitory populations
weight_scaling_factor: When increasing the number of neurons by 2, the weights should be scaled down by 1/2
t_stimulus_start (Quantity): time when the stimulation starts
t_stimulus_duration (Quantity): duration of the stimulation
coherence_level (int): coherence of the stimulus.
Difference in mean between the PoissonGroups "left" stimulus and "right" stimulus
stimulus_update_interval (Quantity): the mean of the stimulating PoissonGroups is
re-sampled at this interval
mu0_mean_stimulus_Hz (float): maximum mean firing rate of the stimulus if c=+1 or c=-1. Each neuron
in the populations "Left" and "Right" receives an independent poisson input.
stimulus_std_Hz (float): std deviation of the stimulating PoissonGroups.
N_extern (int): nr of neurons in the stimulus independent poisson background population
firing_rate_extern (int): firing rate of the stimulus independent poisson background population
w_pos (float): Scaling (strengthening) of the recurrent weights within the
subpopulations "Left" and "Right"
f_Subpop_size (float): fraction of the neurons in the subpopulations "Left" and "Right".
#left = #right = int(f_Subpop_size*N_Excit).
max_sim_time (Quantity): simulated time.
stop_condition_rate (Quantity): An optional stopping criteria: If not None, the simulation stops if the
firing rate of either subpopulation "Left" or "Right" is above stop_condition_rate.
monitored_subset_size (int): max nr of neurons for which a state monitor is registered.
Returns:
A dictionary with the following keys (strings):
"rate_monitor_A", "spike_monitor_A", "voltage_monitor_A", "idx_monitored_neurons_A", "rate_monitor_B",
"spike_monitor_B", "voltage_monitor_B", "idx_monitored_neurons_B", "rate_monitor_Z", "spike_monitor_Z",
"voltage_monitor_Z", "idx_monitored_neurons_Z", "rate_monitor_inhib", "spike_monitor_inhib",
"voltage_monitor_inhib", "idx_monitored_neurons_inhib"
"""
print("simulating {} neurons. Start: {}".format(N_Excit + N_Inhib,
time.ctime()))
t_stimulus_end = t_stimulus_start + t_stimulus_duration
N_Group_A = int(
N_Excit * f_Subpop_size
) # size of the excitatory subpopulation sensitive to stimulus A
N_Group_B = N_Group_A # size of the excitatory subpopulation sensitive to stimulus B
N_Group_Z = N_Excit - N_Group_A - N_Group_B # (1-2f)Ne excitatory neurons do not respond to either stimulus.
Cm_excit = 0.5 * b2.nF # membrane capacitance of excitatory neurons
G_leak_excit = 25.0 * b2.nS # leak conductance
E_leak_excit = -70.0 * b2.mV # reversal potential
v_spike_thr_excit = -50.0 * b2.mV # spike condition
v_reset_excit = -60.0 * b2.mV # reset voltage after spike
t_abs_refract_excit = 2. * b2.ms # absolute refractory period
# specify the inhibitory interneurons:
# N_Inhib = 200
Cm_inhib = 0.2 * b2.nF
G_leak_inhib = 20.0 * b2.nS
E_leak_inhib = -70.0 * b2.mV
v_spike_thr_inhib = -50.0 * b2.mV
v_reset_inhib = -60.0 * b2.mV
t_abs_refract_inhib = 1.0 * b2.ms
# specify the AMPA synapses
E_AMPA = 0.0 * b2.mV
tau_AMPA = 2.5 * b2.ms
# specify the GABA synapses
E_GABA = -70.0 * b2.mV
tau_GABA = 5.0 * b2.ms
# specify the NMDA synapses
E_NMDA = 0.0 * b2.mV
tau_NMDA_s = 100.0 * b2.ms
tau_NMDA_x = 2. * b2.ms
alpha_NMDA = 0.5 * b2.kHz
# projections from the external population
g_AMPA_extern2inhib = 1.62 * b2.nS
g_AMPA_extern2excit = 2.1 * b2.nS
# projectsions from the inhibitory populations
g_GABA_inhib2inhib = weight_scaling_factor * 1.25 * b2.nS
g_GABA_inhib2excit = weight_scaling_factor * 1.60 * b2.nS
# projections from the excitatory population
g_AMPA_excit2excit = weight_scaling_factor * 0.012 * b2.nS
g_AMPA_excit2inhib = weight_scaling_factor * 0.015 * b2.nS
g_NMDA_excit2excit = weight_scaling_factor * 0.040 * b2.nS
g_NMDA_excit2inhib = weight_scaling_factor * 0.045 * b2.nS # stronger projection to inhib.
# weights and "adjusted" weights.
w_neg = 1. - f_Subpop_size * (w_pos - 1.) / (1. - f_Subpop_size)
# We use the same postsyn AMPA and NMDA conductances. Adjust the weights coming from different sources:
w_ext2inhib = g_AMPA_extern2inhib / g_AMPA_excit2inhib
w_ext2excit = g_AMPA_extern2excit / g_AMPA_excit2excit
# other weights are 1
# print("w_neg={}, w_ext2inhib={}, w_ext2excit={}".format(w_neg, w_ext2inhib, w_ext2excit))
# Define the inhibitory population
# dynamics:
inhib_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_inhib * (v-E_leak_inhib)
- g_AMPA_excit2inhib * s_AMPA * (v-E_AMPA)
- g_GABA_inhib2inhib * s_GABA * (v-E_GABA)
- g_NMDA_excit2inhib * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_inhib : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
"""
inhib_pop = NeuronGroup(N_Inhib,
model=inhib_lif_dynamics,
threshold="v>v_spike_thr_inhib",
reset="v=v_reset_inhib",
refractory=t_abs_refract_inhib,
method="rk2")
# initialize with random voltages:
inhib_pop.v = rnd.uniform(v_spike_thr_inhib / b2.mV - 4.,
high=v_spike_thr_inhib / b2.mV - 1.,
size=N_Inhib) * b2.mV
# Specify the excitatory population:
# dynamics:
excit_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_excit * (v-E_leak_excit)
- g_AMPA_excit2excit * s_AMPA * (v-E_AMPA)
- g_GABA_inhib2excit * s_GABA * (v-E_GABA)
- g_NMDA_excit2excit * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_excit : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
ds_NMDA/dt = -s_NMDA/tau_NMDA_s + alpha_NMDA * x * (1-s_NMDA) : 1
dx/dt = -x/tau_NMDA_x : 1
"""
# define the three excitatory subpopulations.
# A: subpop receiving stimulus A
excit_pop_A = NeuronGroup(N_Group_A,
model=excit_lif_dynamics,
threshold="v>v_spike_thr_excit",
reset="v=v_reset_excit",
refractory=t_abs_refract_excit,
method="rk2")
excit_pop_A.v = rnd.uniform(E_leak_excit / b2.mV,
high=E_leak_excit / b2.mV + 5.,
size=excit_pop_A.N) * b2.mV
# B: subpop receiving stimulus B
excit_pop_B = NeuronGroup(N_Group_B,
model=excit_lif_dynamics,
threshold="v>v_spike_thr_excit",
reset="v=v_reset_excit",
refractory=t_abs_refract_excit,
method="rk2")
excit_pop_B.v = rnd.uniform(E_leak_excit / b2.mV,
high=E_leak_excit / b2.mV + 5.,
size=excit_pop_B.N) * b2.mV
# Z: non-sensitive
excit_pop_Z = NeuronGroup(N_Group_Z,
model=excit_lif_dynamics,
threshold="v>v_spike_thr_excit",
reset="v=v_reset_excit",
refractory=t_abs_refract_excit,
method="rk2")
excit_pop_Z.v = rnd.uniform(v_reset_excit / b2.mV,
high=v_spike_thr_excit / b2.mV - 1.,
size=excit_pop_Z.N) * b2.mV
# now define the connections:
# projections FROM EXTERNAL POISSON GROUP: ####################################################
poisson2Inhib = PoissonInput(target=inhib_pop,
target_var="s_AMPA",
N=N_extern,
rate=firing_rate_extern,
weight=w_ext2inhib)
poisson2A = PoissonInput(target=excit_pop_A,
target_var="s_AMPA",
N=N_extern,
rate=firing_rate_extern,
weight=w_ext2excit)
poisson2B = PoissonInput(target=excit_pop_B,
target_var="s_AMPA",
N=N_extern,
rate=firing_rate_extern,
weight=w_ext2excit)
poisson2Z = PoissonInput(target=excit_pop_Z,
target_var="s_AMPA",
N=N_extern,
rate=firing_rate_extern,
weight=w_ext2excit)
###############################################################################################
# GABA projections FROM INHIBITORY population: ################################################
syn_inhib2inhib = Synapses(inhib_pop,
target=inhib_pop,
on_pre="s_GABA += 1.0",
delay=0.5 * b2.ms)
syn_inhib2inhib.connect(p=1.)
syn_inhib2A = Synapses(inhib_pop,
target=excit_pop_A,
on_pre="s_GABA += 1.0",
delay=0.5 * b2.ms)
syn_inhib2A.connect(p=1.)
syn_inhib2B = Synapses(inhib_pop,
target=excit_pop_B,
on_pre="s_GABA += 1.0",
delay=0.5 * b2.ms)
syn_inhib2B.connect(p=1.)
syn_inhib2Z = Synapses(inhib_pop,
target=excit_pop_Z,
on_pre="s_GABA += 1.0",
delay=0.5 * b2.ms)
syn_inhib2Z.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY A: #########################################################
syn_AMPA_A2A = Synapses(excit_pop_A,
target=excit_pop_A,
on_pre="s_AMPA += w_pos",
delay=0.5 * b2.ms)
syn_AMPA_A2A.connect(p=1.)
syn_AMPA_A2B = Synapses(excit_pop_A,
target=excit_pop_B,
on_pre="s_AMPA += w_neg",
delay=0.5 * b2.ms)
syn_AMPA_A2B.connect(p=1.)
syn_AMPA_A2Z = Synapses(excit_pop_A,
target=excit_pop_Z,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_A2Z.connect(p=1.)
syn_AMPA_A2inhib = Synapses(excit_pop_A,
target=inhib_pop,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_A2inhib.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY B: #########################################################
syn_AMPA_B2A = Synapses(excit_pop_B,
target=excit_pop_A,
on_pre="s_AMPA += w_neg",
delay=0.5 * b2.ms)
syn_AMPA_B2A.connect(p=1.)
syn_AMPA_B2B = Synapses(excit_pop_B,
target=excit_pop_B,
on_pre="s_AMPA += w_pos",
delay=0.5 * b2.ms)
syn_AMPA_B2B.connect(p=1.)
syn_AMPA_B2Z = Synapses(excit_pop_B,
target=excit_pop_Z,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_B2Z.connect(p=1.)
syn_AMPA_B2inhib = Synapses(excit_pop_B,
target=inhib_pop,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_B2inhib.connect(p=1.)
###############################################################################################
# AMPA projections FROM EXCITATORY Z: #########################################################
syn_AMPA_Z2A = Synapses(excit_pop_Z,
target=excit_pop_A,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_Z2A.connect(p=1.)
syn_AMPA_Z2B = Synapses(excit_pop_Z,
target=excit_pop_B,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_Z2B.connect(p=1.)
syn_AMPA_Z2Z = Synapses(excit_pop_Z,
target=excit_pop_Z,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_Z2Z.connect(p=1.)
syn_AMPA_Z2inhib = Synapses(excit_pop_Z,
target=inhib_pop,
on_pre="s_AMPA += 1.0",
delay=0.5 * b2.ms)
syn_AMPA_Z2inhib.connect(p=1.)
###############################################################################################
# NMDA projections FROM EXCITATORY to INHIB, A,B,Z
@network_operation()
def update_nmda_sum():
sum_sNMDA_A = sum(excit_pop_A.s_NMDA)
sum_sNMDA_B = sum(excit_pop_B.s_NMDA)
sum_sNMDA_Z = sum(excit_pop_Z.s_NMDA)
# note the _ at the end of s_NMDA_total_ disables unit checking
inhib_pop.s_NMDA_total_ = (1.0 * sum_sNMDA_A + 1.0 * sum_sNMDA_B +
1.0 * sum_sNMDA_Z)
excit_pop_A.s_NMDA_total_ = (w_pos * sum_sNMDA_A +
w_neg * sum_sNMDA_B + w_neg * sum_sNMDA_Z)
excit_pop_B.s_NMDA_total_ = (w_neg * sum_sNMDA_A +
w_pos * sum_sNMDA_B + w_neg * sum_sNMDA_Z)
excit_pop_Z.s_NMDA_total_ = (1.0 * sum_sNMDA_A + 1.0 * sum_sNMDA_B +
1.0 * sum_sNMDA_Z)
# set a self-recurrent synapse to introduce a delay when updating the intermediate
# gating variable x
syn_x_A2A = Synapses(excit_pop_A,
excit_pop_A,
on_pre="x += 1.",
delay=0.5 * b2.ms)
syn_x_A2A.connect(j="i")
syn_x_B2B = Synapses(excit_pop_B,
excit_pop_B,
on_pre="x += 1.",
delay=0.5 * b2.ms)
syn_x_B2B.connect(j="i")
syn_x_Z2Z = Synapses(excit_pop_Z,
excit_pop_Z,
on_pre="x += 1.",
delay=0.5 * b2.ms)
syn_x_Z2Z.connect(j="i")
###############################################################################################
# Define the stimulus: two PoissonInput with time time-dependent mean.
poissonStimulus2A = PoissonGroup(N_Group_A, 0. * b2.Hz)
syn_Stim2A = Synapses(poissonStimulus2A,
excit_pop_A,
on_pre="s_AMPA+=w_ext2excit")
syn_Stim2A.connect(j="i")
poissonStimulus2B = PoissonGroup(N_Group_B, 0. * b2.Hz)
syn_Stim2B = Synapses(poissonStimulus2B,
excit_pop_B,
on_pre="s_AMPA+=w_ext2excit")
syn_Stim2B.connect(j="i")
@network_operation(dt=stimulus_update_interval)
def update_poisson_stimulus(t):
if t >= t_stimulus_start and t < t_stimulus_end:
offset_A = mu0_mean_stimulus_Hz * (0.5 + 0.5 * coherence_level)
offset_B = mu0_mean_stimulus_Hz * (0.5 - 0.5 * coherence_level)
rate_A = numpy.random.normal(offset_A, stimulus_std_Hz)
rate_A = (max(0, rate_A)) * b2.Hz # avoid negative rate
rate_B = numpy.random.normal(offset_B, stimulus_std_Hz)
rate_B = (max(0, rate_B)) * b2.Hz
poissonStimulus2A.rates = rate_A
poissonStimulus2B.rates = rate_B
# print("stim on. rate_A= {}, rate_B = {}".format(rate_A, rate_B))
else:
# print("stim off")
poissonStimulus2A.rates = 0.
poissonStimulus2B.rates = 0.
###############################################################################################
def get_monitors(pop, monitored_subset_size):
"""
Internal helper.
Args:
pop:
monitored_subset_size:
Returns:
"""
monitored_subset_size = min(monitored_subset_size, pop.N)
idx_monitored_neurons = sample(range(pop.N), monitored_subset_size)
rate_monitor = PopulationRateMonitor(pop)
# record parameter: record=idx_monitored_neurons is not supported???
spike_monitor = SpikeMonitor(pop, record=idx_monitored_neurons)
voltage_monitor = StateMonitor(pop, "v", record=idx_monitored_neurons)
return rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons
# collect data of a subset of neurons:
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib = \
get_monitors(inhib_pop, monitored_subset_size)
rate_monitor_A, spike_monitor_A, voltage_monitor_A, idx_monitored_neurons_A = \
get_monitors(excit_pop_A, monitored_subset_size)
rate_monitor_B, spike_monitor_B, voltage_monitor_B, idx_monitored_neurons_B = \
get_monitors(excit_pop_B, monitored_subset_size)
rate_monitor_Z, spike_monitor_Z, voltage_monitor_Z, idx_monitored_neurons_Z = \
get_monitors(excit_pop_Z, monitored_subset_size)
if stop_condition_rate is None:
b2.run(max_sim_time)
else:
sim_sum = 0. * b2.ms
sim_batch = 100. * b2.ms
samples_in_batch = int(floor(sim_batch / b2.defaultclock.dt))
avg_rate_in_batch = 0
while (sim_sum < max_sim_time) and (avg_rate_in_batch <
stop_condition_rate):
b2.run(sim_batch)
avg_A = numpy.mean(rate_monitor_A.rate[-samples_in_batch:])
avg_B = numpy.mean(rate_monitor_B.rate[-samples_in_batch:])
avg_rate_in_batch = max(avg_A, avg_B)
sim_sum += sim_batch
print("sim end: {}".format(time.ctime()))
ret_vals = dict()
ret_vals["rate_monitor_A"] = rate_monitor_A
ret_vals["spike_monitor_A"] = spike_monitor_A
ret_vals["voltage_monitor_A"] = voltage_monitor_A
ret_vals["idx_monitored_neurons_A"] = idx_monitored_neurons_A
ret_vals["rate_monitor_B"] = rate_monitor_B
ret_vals["spike_monitor_B"] = spike_monitor_B
ret_vals["voltage_monitor_B"] = voltage_monitor_B
ret_vals["idx_monitored_neurons_B"] = idx_monitored_neurons_B
ret_vals["rate_monitor_Z"] = rate_monitor_Z
ret_vals["spike_monitor_Z"] = spike_monitor_Z
ret_vals["voltage_monitor_Z"] = voltage_monitor_Z
ret_vals["idx_monitored_neurons_Z"] = idx_monitored_neurons_Z
ret_vals["rate_monitor_inhib"] = rate_monitor_inhib
ret_vals["spike_monitor_inhib"] = spike_monitor_inhib
ret_vals["voltage_monitor_inhib"] = voltage_monitor_inhib
ret_vals["idx_monitored_neurons_inhib"] = idx_monitored_neurons_inhib
return ret_vals
class BrianerClass(BaseClass):
def default_init(self,
_BrianingNeurongroupDict=None,
_BrianingSynapsesDict=None,
_BrianingConnectVariable=None,
_BrianingTraceDict=None,
_BrianingMoniterTuple=None,
_BrianingSpikesDict=None,
_BrianingPyplotDict=None,
_BrianingTimeDimensionVariable=None,
_BrianingPyplotBool=True,
_BrianingStepTimeFloat=0.1,
_BrianedNetworkVariable=None,
_BrianedNeurongroupVariable=None,
_BrianedSynapsesVariable=None,
_BrianedStateMonitorVariable=None,
_BrianedSpikeMonitorVariable=None,
_BrianedClockVariable=None,
_BrianedParentSingularStr=None,
_BrianedRecordKeyStrsList=None,
_BrianedTraceDeriveBrianersList=None,
_BrianedSynapsesDeriveBrianersList=None,
_BrianedStateDeriveBrianersList=None,
_BrianedSpikeDeriveBrianersList=None,
_BrianedParentNetworkDeriveBrianerVariable=None,
_BrianedParentNeurongroupDeriveBrianerVariable=None,
_BrianedParentInteractomeDeriveBrianerVariable=None,
_BrianedParentDeriveRecorderVariable=None,
**_KwargVariablesDict
):
#Call the parent __init__ method
BaseClass.__init__(self,**_KwargVariablesDict)
def do_brian(self):
#/#################/#
# Determine if it is an inside structure or the top
#
#debug
'''
self.debug(
[
'We brian here',
'First look for deeper teams in the structure',
]
)
'''
#Check
if self.ParentedTotalSingularListDict!=None and len(self.ParentedTotalSingularListDict)>0:
#debug
'''
self.debug(
[
'self.ParentedTotalSingularListDict.keys() is ',
str(self.ParentedTotalSingularListDict.keys())
]
)
'''
#get
self.BrianedParentSingularStr=self.ParentedTotalSingularListDict.keys()[0]
#debug
self.debug(
[
'Ok',
('self.',self,[
'BrianedParentSingularStr'
])
]
)
#/########################/#
# Network level
#
#Check
if (self.ParentDeriveTeamerVariable==None or 'Populations' in self.TeamDict or self.ParentDeriveTeamerVariable.TeamTagStr not in [
'Clocks',
'Traces',
'Samples',
'Events',
'Interactomes',
'Interactions'
]) and self.BrianedParentSingularStr!='Population':
#debug
'''
self.debug(
[
'It is a Network level',
'We set the brian network'
]
)
'''
#set
self.brianNetwork()
#/########################/#
# Special Network-Neurongroup level
#
#Check
if 'Populations' not in self.TeamDict:
#debug
'''
self.debug(
[
'It is a network with a one level pop',
'So set the neurongroup'
]
)
'''
#brianPopulation
self.brianPopulation()
#debug
'''
self.debug(
[
'We end to set the neuron group here'
]
)
'''
#/########################/#
# structure
#
#debug
'''
self.debug(
[
'We brian structure in all the brian children...',
]
)
'''
#structure
self.structure(
[
'Clocks',
'Populations',
'Traces',
'Events',
'Samples',
'Interactomes',
'Interactions'
],
'#all',
_ManagerCommandSetList=['brian']
)
#debug
'''
self.debug(
[
'Ok we have brian structured all the brian children...',
]
)
'''
else:
#debug
self.debug(
[
'Ok we check if this parentsingular has a special method ',
('self.',self,[
'BrianedParentSingularStr'
])
]
)
#set
BrianedMethodKeyStr='brian'+self.BrianedParentSingularStr
#Check
if hasattr(self,BrianedMethodKeyStr):
#/########################/#
# call the special brian<BrianedParentSingularStr> method
#
#debug
'''
self.debug(
[
'It is a '+self.BrianedParentSingularStr+' level',
'We brian<BrianedParentSingularStr>'
]
)
'''
#call
getattr(
self,
BrianedMethodKeyStr
)()
#debug
'''
self.debug(
[
'Ok we have setted brian'+self.BrianedParentSingularStr
]
)
'''
#debug
'''
self.debug(
[
'end of brian here'
]
)
'''
def brianNetwork(self):
#/####################/#
# init the Network
#
#maybe should import
from brian2 import Network
#set
self.BrianedNetworkVariable=Network()
#/####################/#
# adapt dimension time
#
#Check
if self.BrianingTimeDimensionVariable==None:
from brian2 import ms
self.BrianingTimeDimensionVariable=ms
#/####################/#
# init a simulation clock
#
#debug
self.debug(
[
'We set a simulation clock at least'
]
)
#Check
if 'Clocks' not in self.TeamDict:
ClocksDeriveManager=self.team('Clocks').TeamedValueVariable
else:
ClocksDeriveManager=self.TeamDict['Clocks']
#manage
if 'Simulation' not in ClocksDeriveManager.ManagementDict:
#debug
self.debug(
[
'We init a simulation clock here'
]
)
#manage
SimulationDeriveBrianer=ClocksDeriveManager.manage(
'Simulation',
{
'BrianingStepTimeFloat':self.BrianingStepTimeFloat
}
)
def brianClock(self):
#/####################/#
# Determine the parents
#
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#/####################/#
# Set the brian clock
#
#import
from brian2 import Clock
#debug
self.debug(
[
'We set the brian clock',
('self.',self,['StructureTagStr'])
]
)
#init
self.BrianedClockVariable=Clock(
dt=self.BrianingStepTimeFloat*self.BrianedParentNetworkDeriveBrianerVariable.BrianingTimeDimensionVariable,
name=self.StructureTagStr
)
#debug
'''
self.debug(
[
'We have setted the clock',
('self.',self,[
'BrianedClockVariable'
])
]
)
'''
def brianPopulation(self):
#debug
'''
self.debug(
[
'It is a Neurongroup level, we set the Neurongroup',
'We adapt the shape of BrianingNeurongroupDict',
('self.',self,[
'BrianingNeurongroupDict',
'RecordingKeyVariable'
])
]
)
'''
#/########################/#
# Determine parents
#
#Check
if self.ParentDeriveTeamerVariable!=None:
#set the parent
self.BrianedParentNetworkDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
else:
#set the parent
self.BrianedParentNetworkDeriveBrianerVariable=self
#/################/#
# Adapt the arg
#
#Check
if 'N' not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict['N']=0
#Check
if 'model' not in self.BrianingNeurongroupDict:
self.BrianingNeurongroupDict['model']=''
#maybe should import
from brian2 import NeuronGroup
#debug
self.debug(
[
('self.',self,[
'BrianingNeurongroupDict'
]),
'We now set the model system Neurongroup if N>0 and model!=""'
]
)
#/################/#
# Set the brian neurongroup
#
#Check
if self.BrianingNeurongroupDict['N']>0 and self.BrianingNeurongroupDict['model']!="":
#init
self.BrianedNeurongroupVariable=NeuronGroup(
**dict(
self.BrianingNeurongroupDict,
**{
'name':self.ParentedTotalPathStr.replace('/','_')+'_'+self.ManagementTagStr,
#'clock':self.BrianedParentNetworkDeriveBrianerVariable.TeamDict[
# 'Clocks'
#].ManagementDict['Simulation'].BrianedClockVariable
}
)
)
#debug
self.debug(
[
'Ok we have setted the Neurongroup',
('self.',self,[
'BrianedNeurongroupVariable'
])
]
)
#/##################/#
# team States first all the brian variables
#
#get
self.BrianedRecordKeyStrsList=self.BrianedNeurongroupVariable.equations._equations.keys()
#Check
if len(self.BrianedRecordKeyStrsList)>0:
#debug
'''
self.debug(
[
'We simulate with neurongroup',
'adapt the initial conditions of all the brian variables',
'so first we team Traces and put Recorders inside or get it and mapSet'
]
)
'''
#Check
if 'Traces' not in self.TeamDict:
BrianedDeriveTraces=self.team(
'Traces'
).TeamedValueVariable
else:
BrianedDeriveTraces=self.TeamDict[
'Traces'
]
#debug
self.debug(
[
'We set the tracers',
('self.',self,['BrianedRecordKeyStrsList'])
]
)
#map
self.BrianedTraceDeriveBrianersList=map(
lambda __ManagementKeyStr,__RecordKeyStr:
BrianedDeriveTraces.manage(
__ManagementKeyStr,
{
'RecordingKeyVariable':getattr(
self.BrianedNeurongroupVariable,
__RecordKeyStr
),
'RecordKeyStr':__RecordKeyStr
}
).ManagedValueVariable
if __ManagementKeyStr not in BrianedDeriveTraces.ManagementDict
else BrianedDeriveTraces.ManagementDict[__ManagementKeyStr].mapSet(
{
'RecordingKeyVariable':getattr(
self.BrianedNeurongroupVariable,
__RecordKeyStr
),
'RecordKeyStr':__RecordKeyStr
}
),
map(
lambda __BrianedRecordKeyStr:
Recorder.RecordPrefixStr+__BrianedRecordKeyStr,
self.BrianedRecordKeyStrsList
),
self.BrianedRecordKeyStrsList
)
#/##################/#
# add in the net
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedNeurongroupVariable
)
#/####################/#
# maybe pyplot a draw plot
#
#debug
'''
self.debug(
[
'We complete a view so first fill the draw'
]
)
'''
#Check
if 'Charts' not in self.TeamDict:
BrianedChartsDeriveTeamer=self.team(
'Charts'
).TeamedValueVariable
else:
BrianedChartsDeriveTeamer=self.TeamDict['Charts']
"""
#Check
if 'Traces' in self.TeamDict:
#debug
'''
self.debug(
[
'First we add the traces'
]
)
'''
#set
map(
lambda __KeyStr:
BrianedChartsDeriveTeamer.manage(
__KeyStr,
{
'-Draws':
{
}
}
),
self.TeamDict['Traces'].ManagementDict.keys()
)
#debug
'''
self.debug(
[
'self.TeamDict["Traces"] is ',
str(self.TeamDict["Traces"])
]
)
'''
#Check
if 'Events' in self.TeamDict:
#debug
'''
self.debug(
[
'Then we add the events'
]
)
'''
#set
BrianedChartsDeriveTeamer.mapSet(
map(
lambda __KeyStr:
(
'|'+__KeyStr,
{
'-Draws':
{
}
}
),
self.TeamDict['Events'].ManagementDict.keys()
)
)
#debug
'''
self.debug(
[
'self.TeamDict["Events"] is ',
str(self.TeamDict["Events"])
]
)
'''
"""
#/################/#
# Make maybe brian the new traces
#
#debug
self.debug(
[
'We make parent brian the new tracers'
]
)
#map
map(
lambda __BrianedTraceDeriveBrianer:
__BrianedTraceDeriveBrianer.parent(
).brian(
),
self.BrianedTraceDeriveBrianersList
)
#debug
'''
self.debug(
[
'Ok we know the structure ',
('self.',self,['BrianedNetworkVariable'])
]
)
'''
#debug
'''
self.debug(
[
'End of brianPopulation'
]
)
'''
def brianInteraction(self):
#/########################/#
# Postlet level
#
#debug
self.debug(
[
'It is a Synapser level, we set the Synapser',
('self.',self,[
#'BrianingSynapsesDict'
]
)
]
)
#/####################/#
# Set the parent
#
#Check
if self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable.BrianedParentSingularStr=='Interactome':
#debug
'''
self.debug(
[
'We are in a projectome structure'
]
)
'''
#set
self.BrianedParentInteractomeDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.BrianedParentInteractomeDeriveBrianerVariable.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
else:
#debug
'''
self.debug(
[
'There is no projectome structure'
]
)
'''
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the ConnectedTo Variable
#
#debug
self.debug(
[
'Check if we have to get the connected to variable',
('self.',self,['ConnectedToVariable'])
]
)
#Check
if self.ConnectedToVariable==None:
#debug
self.debug(
[
'We setConnection here'
]
)
#setConnection
self.setConnection(
self.ManagementTagStr,
self,
self.BrianedParentNeurongroupDeriveBrianerVariable
)
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#debug
self.debug(
[
'Do we have to make parent-brian the connected variable ?',
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable)
]
)
#Check
if self.ConnectedToVariable.BrianedNeurongroupVariable==None:
#parent brian
self.ConnectedToVariable.parent(
).brian(
)
#set
BrianedNameStr=self.BrianedParentNeurongroupDeriveBrianerVariable.StructureTagStr+'_To_'+self.ConnectedToVariable.StructureTagStr
#debug
self.debug(
[
'We set the synapses',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable),
'self.ConnectedToVariable.BrianedNeurongroupVariable is ',
str(self.ConnectedToVariable.BrianedNeurongroupVariable),
'Maybe we have to make brian the post',
'BrianedNameStr is '+BrianedNameStr
]
)
#import
from brian2 import Synapses
#init
self.BrianedSynapsesVariable=Synapses(
source=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
target=self.ConnectedToVariable.BrianedNeurongroupVariable,
name=BrianedNameStr.replace('/','_'),
**self.BrianingSynapsesDict
)
#/####################/#
# Connect options
#
#connect
if type(self.BrianingConnectVariable)==float:
#debug
self.debug(
[
'we connect with a sparsity of ',
('self.',self,[
'BrianingConnectVariable'
])
]
)
#connect
self.BrianedSynapsesVariable.connect(
True,
p=self.BrianingConnectVariable
)
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedSynapsesVariable
)
def brianTrace(self):
#debug
'''
self.debug(
[
'It is a Trace level, we set the Samples',
('self.',self,[
#'RecordingKeyVariable',
'RecordKeyStr'
])
]
)
'''
#/####################/#
# we record
#
#record
self.record()
#/####################/#
# Set the parent
#
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/###################/#
# Build the samples and maybe one default moniter
#
#debug
self.debug(
[
'Look if we have samples here',
"'Samples' not in self.TeamDict is ",
str('Samples' not in self.TeamDict)
]
)
#Check
if 'Samples' not in self.TeamDict:
BrianedSamplesDeriveManager=self.team(
'Samples'
).TeamedValueVariable
else:
BrianedSamplesDeriveManager=self.TeamDict[
'Samples'
]
#debug
self.debug(
[
'Do we have to set a default moniter ?',
#'len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedRecordKeyStrsList) is ',
#str(len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedRecordKeyStrsList)),
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedRecordKeyStrsList) is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedRecordKeyStrsList),
]
)
#Check
if len(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedRecordKeyStrsList)==1:
#debug
self.debug(
[
'BrianedSamplesDeriveManager.ManagementDict.keys() is',
str(BrianedSamplesDeriveManager.ManagementDict.keys())
]
)
#Check
if len(BrianedSamplesDeriveManager.ManagementDict)==0:
#debug
self.debug(
[
'There is just one variable that we sample',
'we manage and make it brian'
]
)
#manage
BrianedDefaultMoniter=BrianedSamplesDeriveManager.manage(
'Default',
).ManagedValueVariable
#set the monitor
BrianedDefaultMoniter.MoniteringLabelIndexIntsArray=[0] if self.BrianedParentNeurongroupDeriveBrianerVariable.BrianingNeurongroupDict[
'N']>0 else []
#brian
BrianedDefaultMoniter.parent(
).brian(
)
else:
#debug
self.debug(
[
'Just be sure to parent brian everybody'
]
)
#map
map(
lambda __DeriveBrianer:
__DeriveBrianer.parent(
).brian(
),
BrianedSamplesDeriveManager.ManagementDict.values()
)
def brianSample(self):
#debug
self.debug(
[
'It is a Sample State Moniter level',
('self.',self,[
'MoniteringLabelIndexIntsArray',
])
]
)
#/####################/#
# Set the parent
#
#get
self.BrianedParentDeriveRecorderVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.BrianedParentDeriveRecorderVariable.BrianedParentNeurongroupDeriveBrianerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the brian monitor
#
#debug
'''
self.debug(
[
'We set the state monitor',
('self.',self,[
#'BrianedParentNeurongroupDeriveBrianerVariable'
]),
#'self.BrianedParentDeriveRecorderVariable.RecordKeyStr is ',
#str(self.BrianedParentDeriveRecorderVariable.RecordKeyStr)
'self.ParentedTotalManagementOrderedDict.keys() is ',
str(self.ParentedTotalManagementOrderedDict.keys())
]
)
'''
#import
from brian2 import StateMonitor
#init
self.BrianedStateMonitorVariable=StateMonitor(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
self.BrianedParentDeriveRecorderVariable.RecordKeyStr,
self.MoniteringLabelIndexIntsArray,
)
#debug
'''
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedStateMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable)
]
)
'''
#/####################/#
# add to the structure
#
#debug
'''
self.debug(
[
'We add to the structure'
]
)
'''
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedStateMonitorVariable
)
#/####################/#
# maybe pyplot a draw plot
#
#debug
self.debug(
[
'Maybe we pyplot'
]
)
#Check
if self.BrianingPyplotBool:
#debug
'''
self.debug(
[
'We complete a view so first fill the draw'
]
)
'''
#set
LabelStr='$'+self.BrianedParentDeriveRecorderVariable.RecordKeyStr+'_{'+str(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable.name
).replace('_','/')+'}'
#set
self.PyplotingDrawVariable=map(
lambda __IndexInt:
(
'plot',
{
'#liarg:#map@get':[
'#IdGetStr.BrianedStateMonitorVariable.t',
'>>SYS.IdDict[#IdStr].BrianedStateMonitorVariable.'+self.BrianedParentDeriveRecorderVariable.RecordKeyStr+'['+str(
__IndexInt)+',:]'
],
'#kwarg':dict(
{
'label':LabelStr+'^{'+str(__IndexInt)+'}$',
'linestyle':'-',
'color':'b'
},
**self.BrianingPyplotDict
)
}
),
self.MoniteringLabelIndexIntsArray
)
#/####################/#
# maybe set for the Chart
#
#init
self.PyplotingChartVariable=[]
#/####################/#
# maybe set the X Chart also
#
#Check
if self.BrianedParentNeurongroupDeriveBrianerVariable.BrianingTimeDimensionVariable==None:
from brian2 import ms
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianingTimeDimensionVariable=ms
#set
XLabelStr='$t\ ('+str(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianingTimeDimensionVariable
)+')$'
#set
SampleTagStr=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable.name+'t'
#join
XlimLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"LimFloatsArray',",
"[SYS.IdDict[#IdStr].BrianedStateMonitorVariable.t[:].min(),",
"SYS.IdDict[#IdStr].BrianedStateMonitorVariable.t[:].max()]",
').'+SampleTagStr+"LimFloatsArray"
])
#join
XticksLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"TickFloatsArray',",
"map(lambda __Float:float('%.2f'%__Float),",
"SYS.getTickFloatsArray(",
'SYS.'+SampleTagStr+"LimFloatsArray,3",
")))."+SampleTagStr+"TickFloatsArray"
])
XtickLabelLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"TickStrsArray',",
"map(lambda __Float:'$'+str(__Float)+'$',",
"SYS."+SampleTagStr+"TickFloatsArray))."+SampleTagStr+"TickStrsArray"
])
#debug
'''
self.debug(
[
'XLabelStr is ',
XLabelStr,
'XlimLiargStr is',
XlimLiargStr,
'XticksLiargStr is ',
XticksLiargStr,
'XtickLabelLiargStr is ',
XtickLabelLiargStr
]
)
'''
#set
self.PyplotingChartVariable+=[
(
'set_xlabel',XLabelStr
),
(
'set_xlim',{
'#liarg:#map@get':[XlimLiargStr]
}
),
(
'set_xticks',{
'#liarg:#map@get':[XticksLiargStr]
}
),
(
'set_xticklabels',{
'#liarg:#map@get':[XtickLabelLiargStr]
}
)
]
#/####################/#
# maybe set the Y Chart also
#
#set
YLabelStr='$'+self.BrianedParentDeriveRecorderVariable.RecordKeyStr+'_{'+str(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable.name
).replace('_','/')+'}(t)\ ('+str(
self.BrianedParentDeriveRecorderVariable.RecordedTraceFloatsArray.unit
)+')$'
#set
SampleTagStr=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable.name+self.BrianedParentDeriveRecorderVariable.RecordKeyStr
#join
YlimLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"LimFloatsArray',",
"[SYS.IdDict[#IdStr].BrianedStateMonitorVariable."+self.BrianedParentDeriveRecorderVariable.RecordKeyStr+".min(),",
"SYS.IdDict[#IdStr].BrianedStateMonitorVariable."+self.BrianedParentDeriveRecorderVariable.RecordKeyStr+".max()]",
').'+SampleTagStr+"LimFloatsArray"
])
#join
YticksLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"TickFloatsArray',",
"map(lambda __Float:float('%.2f'%__Float),",
"SYS.getTickFloatsArray(",
'SYS.'+SampleTagStr+"LimFloatsArray,3",
")))."+SampleTagStr+"TickFloatsArray"
])
YtickLabelLiargStr="".join([
">>SYS.set(SYS,'"+SampleTagStr+"TickStrsArray',",
"map(lambda __Float:'$'+str(__Float)+'$',",
"SYS."+SampleTagStr+"TickFloatsArray))."+SampleTagStr+"TickStrsArray"
])
#debug
'''
self.debug(
[
'YLabelStr is ',
YLabelStr,
'YlimLiargStr is',
YlimLiargStr,
'YticksLiargStr is ',
YticksLiargStr,
'YtickLabelLiargStr is ',
YtickLabelLiargStr
]
)
'''
#set
self.PyplotingChartVariable+=[
(
'set_ylabel',YLabelStr
),
(
'set_ylim',{
'#liarg:#map@get':[YlimLiargStr]
}
),
(
'set_yticks',{
'#liarg:#map@get':[YticksLiargStr]
}
),
(
'set_yticklabels',{
'#liarg:#map@get':[YtickLabelLiargStr]
}
)
]
#/####################/#
# maybe set global Chart also
#
self.PyplotingChartVariable+=[
(
'tick_params',{
'#kwarg':{
'length':10,
'width':5,
'which':'major'
}
}
),
(
'tick_params',{
'#kwarg':{
'length':5,
'width':2,
'which':'minor'
}
}
),
('xaxis.set_ticks_position',
{
'#liarg':['bottom']
}
),
('yaxis.set_ticks_position',
{
'#liarg':['left']
}
),
('legend',{
'#liarg':[],
'#kwarg':{
'fontsize':10,
'shadow':True,
'fancybox':True,
'ncol':max(1,len(
getattr(
self.BrianedStateMonitorVariable,
self.BrianedParentDeriveRecorderVariable.RecordKeyStr
)
)/2),
'loc':2,
'bbox_to_anchor':(1.05, 1)
}
})
]
#/####################/#
# maybe replace Chart also
#
#debug
'''
self.debug(
[
'Before replace',
('self.',self,[
'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
'''
#mapReplace
[
self.PyplotingDrawVariable,
self.PyplotingChartVariable
]=map(
lambda __Variable:
SYS.replace(
__Variable,
{
'#IdStr':str(self.PrintIdInt),
'#IdGetStr':"#id:"+str(self.PrintIdInt)
},
self
)
if __Variable!=None
else None,
map(
lambda __KeyStr:
getattr(
self,
__KeyStr
),
[
'PyplotingDrawVariable',
'PyplotingChartVariable'
]
)
)
#debug
'''
self.debug(
[
'After replace',
('self.',self,[
#'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
'''
#/####################/#
# Update maybe the
# parent neuron group
"""
#Check
if 'Charts' not in self.BrianedParentNeurongroupDeriveBrianerVariable.TeamDict:
BrianedChartsDeriveManager=self.BrianedParentNeurongroupDeriveBrianerVariable.team(
'Charts').TeamedValueVariable
else:
BrianedChartsDeriveManager=self.BrianedParentNeurongroupDeriveBrianerVariable.TeamDict['Charts']
"""
#get
BrianedChartsDeriveManager=self.BrianedParentNeurongroupDeriveBrianerVariable.TeamDict[
'Charts'
]
#get
#BrianedChartDerivePyploter=BrianedChartsDeriveManager.ManagementDict[
# self.BrianedParentDeriveRecorderVariable.ManagementTagStr
#]
BrianedChartDerivePyploter=BrianedChartsDeriveManager.manage(
self.BrianedParentDeriveRecorderVariable.ManagementTagStr
).ManagedValueVariable
#debug
'''
self.debug(
[
'We update in the parent neurongroup chart',
'BrianedChartDerivePyploter is ',
SYS._str(BrianedChartDerivePyploter),
('self.',self,[])
]
)
'''
#team
BrianedDrawDeriveManager=BrianedChartDerivePyploter.team(
'Draws'
).TeamedValueVariable
#manage
BrianedDrawDeriveManager.manage(
str(self.ManagementIndexInt),
{
'PyplotingDrawVariable':self.PyplotingDrawVariable
}
)
def brianEvent(self):
#debug
'''
self.debug(
[
'It is a Spike Moniter level',
('self.',self,[
])
]
)
'''
#/####################/#
# Set the BrianedParentNeurongroupDeriveBrianerVariable
#
#get
self.BrianedParentNeurongroupDeriveBrianerVariable=self.ParentDeriveTeamerVariable.ParentDeriveTeamerVariable
#get
self.BrianedParentNetworkDeriveBrianerVariable=self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedParentNetworkDeriveBrianerVariable
#/####################/#
# Set the brian monitor
#
#debug
'''
self.debug(
[
'We set the spike monitor'
]
)
'''
#import
from brian2 import SpikeMonitor
#init
self.BrianedSpikeMonitorVariable=SpikeMonitor(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable,
)
#debug
'''
self.debug(
[
'Ok we have setted the monitor',
('self.',self,['BrianedSpikeMonitorVariable']),
'Now we add to the structure',
'self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable is ',
str(self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNetworkVariable)
]
)
'''
#/####################/#
# add to the structure
#
#add
self.BrianedParentNetworkDeriveBrianerVariable.BrianedNetworkVariable.add(
self.BrianedSpikeMonitorVariable
)
#/####################/#
# maybe pyplot a draw plot
#
#Check
if self.BrianingPyplotBool:
#debug
'''
self.debug(
[
'We complete a view so first fill the draw'
]
)
'''
#set
LabelStr='$'+self.ManagementTagStr+'_{'+str(
self.BrianedParentNeurongroupDeriveBrianerVariable.BrianedNeurongroupVariable.name
).replace('_','/')+'}'
#set
self.PyplotingDrawVariable=[
(
'plot',
{
'#liarg:#map@get':[
'#IdGetStr.BrianedSpikeMonitorVariable.t',
'>>SYS.IdDict[#IdStr].BrianedSpikeMonitorVariable.i'
],
'#kwarg':dict(
{
'label':LabelStr,
'linestyle':'',
'marker':'.',
'color':'b'
},
**self.BrianingPyplotDict
)
}
)
]
#/####################/#
# maybe replace Chart also
#
#debug
'''
self.debug(
[
'Before replace',
('self.',self,[
'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
'''
#mapReplace
[
self.PyplotingDrawVariable,
self.PyplotingChartVariable
]=map(
lambda __Variable:
SYS.replace(
__Variable,
{
'#IdStr':str(self.PrintIdInt),
'#IdGetStr':"#id:"+str(self.PrintIdInt)
},
self
)
if __Variable!=None
else None,
map(
lambda __KeyStr:
getattr(
self,
__KeyStr
),
[
'PyplotingDrawVariable',
'PyplotingChartVariable'
]
)
)
#debug
'''
self.debug(
[
'After replace',
('self.',self,[
#'PyplotingDrawVariable',
'PyplotingChartVariable'
])
]
)
'''
#/####################/#
# Update maybe the
# parent neuron group
#get
BrianedChartDeriveManager=self.BrianedParentNeurongroupDeriveBrianerVariable.TeamDict[
'Charts'
]
#manage
BrianedChartDerivePyploter=BrianedChartDeriveManager.manage(
self.ManagementTagStr
).ManagedValueVariable
#debug
'''
self.debug(
[
'We update in the parent neurongroup chart',
'BrianedChartDerivePyploter is ',
SYS._str(BrianedChartDerivePyploter),
('self.',self,[])
]
)
'''
#team
BrianedDrawDeriveManager=BrianedChartDerivePyploter.team(
'Draws'
).TeamedValueVariable
#manage
BrianedDrawDeriveManager.manage(
str(self.ManagementIndexInt),
{
'PyplotingDrawVariable':self.PyplotingDrawVariable
}
)
def mimic_simulate(self):
#parent method
BaseClass.simulate(self)
#debug
'''
self.debug('We start simulate in brian')
'''
#run with the brian method
self.BrianedNetworkVariable.run(
self.SimulatingStopTimeFloat*self.BrianingTimeDimensionVariable
)
#debug
'''
self.debug('We stop running in brian')
'''
def mimic_record(self):
#base method
BaseClass.record(self)
#debug
'''
self.debug(
[
'We have traced, alias the init in the brian object',
('self.',self,[
'RecordedTraceFloatsArray',
'RecordedInitFloatsArray'
])
]
)
'''
#alias
self.RecordedTraceFloatsArray[:]=self.RecordedInitFloatsArray*self.RecordedTraceFloatsArray.unit
#debug
'''
self.debug(
[
('self.',self,['RecordedTraceFloatsArray'])
]
)
'''
def mimic__print(self,**_KwargVariablesDict):
#/##################/#
# Modify the printing Variable
#
#Check
if self.PrintingSelfBool:
#/##################/#
# Remove the brian objects that are non setted
#
#map
map(
lambda __KeyStr:
self.forcePrint(
[__KeyStr],
'BrianerClass'
)
if getattr(self.PrintingCopyVariable,__KeyStr)!=None
else None,
[
'BrianedNetworkVariable',
'BrianedNeurongroupVariable',
'BrianedSynapsesVariable',
'BrianedStateMonitorVariable',
'BrianedSpikeMonitorVariable',
'BrianedClockVariable'
]
)
#/##################/#
# Call the base method
#
#call
BaseClass._print(self,**_KwargVariablesDict)