def set_group_spike_monitor(self, ch=0):
"""
!!!
this would not work with random assemblies
to be removed in the future
"""
self.mon_spike_sngl = [
] # measure spike times from a few single neurons
for nrn in self.nrn_meas_e:
self.mon_spike_sngl.append(bb.SpikeMonitor(self.Pe[nrn]))
self.network.add(self.mon_spike_sngl)
self.mon_spike_gr = [
] # measure spike times from groups (for CV and FF)
for gr in self.nrngrp_meas:
self.mon_spike_gr.append(
bb.SpikeMonitor(self.p_ass[ch][gr][0:self.n_spikeM_gr]))
# also control group of neurons which is not included in the ps
self.mon_spike_gr.append(bb.SpikeMonitor(\
self.Pe[self.n_ass*self.s_ass:(self.n_ass+1)*self.s_ass]
[0:self.n_spikeM_gr]))
self.network.add(self.mon_spike_gr)
# default spike easure is off
for sp in self.mon_spike_gr:
sp.record = False
def SpikeMonitor(neuron_groups, index_str):
index_a, index_b, index_aux = _neuron_group_index(index_str)
if index_b == None and index_aux == None:
S = br.SpikeMonitor(neuron_groups[index_a], record=0)
elif index_a == 2:
S = br.SpikeMonitor(neuron_groups[index_a][index_aux], record=0)
elif index_b != None:
S = br.SpikeMonitor(neuron_groups[index_a][index_b], record=0)
return S
def main4(plot=True):
# what if we want to keep the input spike exactly the same throughout different taus?
# Solution: We run PoissonGroup once, store all the spikes, and use the stored spikes across the multiple runs
bs.start_scope()
num_inputs = 100
input_rate = 10 * bs.Hz
w = 0.1
tau_range = bs.linspace(1, 10, 30) * bs.ms
output_rates = []
P = bs.PoissonGroup(num_inputs, rates=input_rate)
p_monitor = bs.SpikeMonitor(P)
one_second = 1 * bs.second
"""
Note that in the code above, we created Network objects.
The reason is that in the loop, if we just called run it would try to simulate all the objects,
including the Poisson neurons P, and we only want to run that once.
We use Network to specify explicitly which objects we want to include.
"""
net = bs.Network(P, p_monitor)
net.run(one_second)
# keeps a copy of the spikes that are generated by the PoissonGroup during that explicit run earlier
spikes_i = p_monitor.i
spikes_t = p_monitor.t
# Construct network that we run each time
sgg = bs.SpikeGeneratorGroup(num_inputs, spikes_i, spikes_t)
eqs = '''
dv/dt = -v/tau : 1
'''
G = bs.NeuronGroup(1, eqs, threshold='v>1', reset='v=0', method='exact')
S = bs.Synapses(sgg, G, on_pre='v += w')
S.connect() # fully connected network
g_monitor = bs.SpikeMonitor(G)
# store the current state of the network
net = bs.Network(sgg, G, S, g_monitor)
net.store()
for tau in tau_range:
net.restore()
net.run(one_second)
output_rates.append(g_monitor.num_spikes / bs.second)
if plot:
plt.clf()
plt.plot(tau_range / bs.ms, output_rates)
plt.xlabel(r'$\tau$ (ms)')
plt.ylabel('Firing Rate (spikes/s)')
# there is much less noise compared to before where we used different PoissonGroup everytime
plt.show()
def make_classification_network(self, number_of_stimuli, network_name):
if network_name not in self.networks:
network_size = number_of_stimuli * self.number_of_neurons
count_mat = np.zeros((int(self.stimulus_duration / ms * 10), network_size), int)
target = b2.NeuronGroup(N=number_of_stimuli, model=self.eqs, threshold='v>threshold', reset='v=0',
namespace={'tau': self.tau, 'threshold': self.threshold})
driving = b2.SpikeGeneratorGroup(N=network_size,
indices=[0], times=[0 * ms])
# counts = b2.TimedArray(values=_count_mat, dt=b2.defaultclock.dt)
synapses = b2.Synapses(source=driving, target=target,
model='w: 1', on_pre='v+=w*counts(t, i)')
i = np.arange(network_size)
j = np.repeat(range(number_of_stimuli), self.number_of_neurons)
synapses.connect(j=j, i=i)
synapses.w = np.tile(self.weights, reps=number_of_stimuli)
spikes = b2.SpikeMonitor(target, record=True)
voltage = b2.StateMonitor(target, 'v', record=True)
net = b2.Network([target, driving, synapses, spikes, voltage])
net.store()
self.networks[network_name] = dict(net=net,
count_mat=count_mat,
synapses=synapses,
v_mon=voltage,
spike_mon=spikes,
number_of_stimuli=number_of_stimuli,
driving=driving)
else:
self.networks[network_name]['synapses'].w = np.tile(self.weights, reps=number_of_stimuli)
def simulate_LIF_neuron(input_current,
simulation_time=5. * b2.ms,
dt=0.01,
v_rest=-70 * b2.mV,
v_reset=-65 * b2.mV,
firing_threshold=-50 * b2.mV,
membrane_resistance=10. * b2.Mohm,
membrane_time_scale=8. * b2.ms,
abs_refractory_period=2.0 * b2.ms):
b2.defaultclock.dt = dt * b2.ms
# differential equation of Leaky Integrate-and-Fire model
# eqs = """
# dv/dt =
# ( -(v-v_rest) + membrane_resistance * input_current(t,i) ) / membrane_time_scale : volt (unless refractory)
# """
eqs = """
dv/dt =
( -(v-v_rest) + membrane_resistance * input_current ) / membrane_time_scale : volt (unless refractory)
"""
neuron = b2.NeuronGroup(1,
model=eqs,
reset="v=v_reset",
threshold="v>firing_threshold",
refractory=abs_refractory_period,
method="exact") # "euler" / "exact"
neuron.v = v_rest # set initial value
network = b2.core.network.Network(neuron)
# run before for compiling (JIT compile time out of timing)
#network.run(simulation_time, profile=True)
spike_monitor = b2.SpikeMonitor(neuron)
network.add(spike_monitor)
neuron.v = v_rest
#start_wallclock = time.time()
#start_cpu = time.clock() # timer()
network.run(simulation_time, profile=True)
#end_cpu = time.clock() # timer()
#end_wallclock = time.time()
#time_elapsed_wallclock = end_wallclock - start_wallclock
#time_elapsed_cpu = end_cpu - start_cpu
b2.device.build(directory='output',
clean=True,
compile=True,
run=True,
debug=False)
print("\n")
print("brian2 profiling summary (listed by time consumption):\n")
print(b2.profiling_summary())
return spike_monitor, network.get_profiling_info(
) # time_elapsed_wallclock, time_elapsed_cpu,
def calculate_input_seqs(self):
"""
Calculating input sequence based on the video input.
"""
b2.set_device('cpp_standalone',
directory=os.path.join(
self.output_folder,
'Input_cpp_run' + self.output_file_suffix))
# inputdt = b2.defaultclock.dt
spikemons = []
n0 = len(self.i_patterns[0].T)
frames = self.frames
factor = self.factor
# tmp_group = b2.NeuronGroup(n0, 'rate = frames(t,i)*factor : Hz', threshold='b2.rand()<rate*dt')
tmp_group = b2.NeuronGroup(n0,
'rate = frames(t,i)*factor : Hz',
threshold='rand()<rate*dt')
tmp_network = b2.Network()
tmp_network.add(tmp_group)
tmp_mon = b2.SpikeMonitor(tmp_group)
tmp_network.add(tmp_mon)
spikemons.append(tmp_mon)
if self.BaseLine == 0 * second:
tmp_network.run(self.duration, report='text')
else:
tmp_network.run(self.BaseLine)
tmp_network.run(self.duration - self.BaseLine)
self.save_input_sequence(
spikemons,
os.path.join(self.output_folder,
'input' + self.output_file_suffix))
shutil.rmtree(
os.path.join(self.output_folder,
'Input_cpp_run' + self.output_file_suffix))
def brian_poisson(rate, duration_ms, dt=1 * ms, n=1):
"""
:param rate:
:param duration_ms:
:param dt:
:param n:
:return:
"""
q = b2.units.fundamentalunits.Quantity
if not isinstance(rate, q):
if np.isscalar(rate):
rate = rate * Hz
else:
rate = np.array(rate) * Hz
if not isinstance(duration_ms, q):
if np.isscalar(duration_ms):
duration_ms = duration_ms * ms
else:
duration_ms = np.array(duration_ms) * ms
neuron = b2.NeuronGroup(n, "rate : Hz", threshold='rand()<rate*dt', dt=dt)
neuron.rate = rate
spikes = b2.SpikeMonitor(neuron, record=True)
b2.run(duration_ms)
if n == 1:
trains = spikes.spike_trains()[0] / dt
else:
trains = [train / dt for train in spikes.spike_trains().values()]
return trains
def main3():
# Adding Spikes
bs.start_scope()
tau = 10 * bs.ms
eqs = '''
dv/dt = (1-v)/tau : 1
'''
# conditions for spiking models
threshold = 'v>0.8'
reset = 'v = -0.8'
G = bs.NeuronGroup(1, eqs, threshold=threshold, reset=reset, method='exact')
M = bs.StateMonitor(G, 'v', record=0)
bs.run(50 * bs.ms)
plt.plot(M.t / bs.ms, M.v[0])
plt.xlabel('Time (ms)')
plt.ylabel('v')
plt.show()
# you can also add spike monitor
spike_monitor = bs.SpikeMonitor(G)
bs.run(50 * bs.ms)
print(f"Spike Times: {spike_monitor.t[:]}")
def simulate(tau):
b2.start_scope()
if standalone_mode:
b2.get_device().reinit()
b2.get_device().activate(build_on_run=False, directory=directory_name)
net = b2.Network()
P = b2.PoissonGroup(num_inputs, rates=input_rate)
G = b2.NeuronGroup(1, eqs, threshold='v>1', reset='v=0', method='euler')
S = b2.Synapses(P, G, on_pre='v += weight')
S.connect()
M = b2.SpikeMonitor(G)
net.add(P)
net.add(G)
net.add(S)
net.add(M)
net.run(1000 * b2.ms)
if standalone_mode:
b2.get_device().build(directory=directory_name,
compile=True,
run=True,
debug=False)
return M
def main4():
# Incorporation of refractory period
bs.start_scope()
tau = 10 * bs.ms
# the (unless refractory) is necessary
# refer to the documentation for more detail
eqs = '''
dv/dt = (1-v)/tau : 1 (unless refractory)
'''
equation = bs.Equations(eqs)
# conditions for spiking models
threshold = 'v>0.8'
reset = 'v = -0.8'
refractory = 5 * bs.ms
G = bs.NeuronGroup(1, eqs, threshold=threshold, reset=reset, method='exact', refractory=refractory)
state_monitor = bs.StateMonitor(G, 'v', record=0)
spike_monitor = bs.SpikeMonitor(G)
bs.run(50 * bs.ms)
plt.plot(state_monitor.t / bs.ms, state_monitor.v[0])
plt.xlabel('Time (ms)')
plt.ylabel('v')
plt.show()
def AllSpikeMonitors(neuron_groups, spike_monitor_names):
N = len(neuron_groups)
spike_monitors = []
spike_monitors.append(
br.SpikeMonitor(neuron_groups[0], name=spike_monitor_names[0]))
spike_monitors.append(
br.SpikeMonitor(neuron_groups[1][0], name=spike_monitor_names[1]))
#spike_monitors.append([])
#for i in range(len(neuron_groups[2])):
# spike_monitors[2].append(br.SpikeMonitor(neuron_groups[2][i], \
# record=0, name=spike_monitor_names[2][i]))
spike_monitors.append(
br.SpikeMonitor(neuron_groups[2], name=spike_monitor_names[2]))
return spike_monitors
def plot_fi_curve(self,
min_current=0 * pA,
max_current=1 * nA,
step_size=10 * pA,
max_rate=None,
plot=True):
# Compute current steps
steps = np.arange(min_current, max_current, step_size) * amp
N_steps = len(steps)
# Prepare params and eqs
neuron_parameters = self.neuron_parameters
refractory_period = neuron_parameters['refractory_period']
eqs = self.get_membrane_equation(substitute_ad_hoc={
'EXT_CURRENTS': '+ I_ext',
'EXT_CURRENTS_EQS': 'I_ext : amp'
})
# Create a neuron group
neurons = b2.NeuronGroup(N_steps,
model=eqs,
namespace=neuron_parameters,
reset=self.reset_statements,
threshold=self.threshold_condition,
refractory=refractory_period,
method=self.integration_method)
# Set initial values
initial_values = self.get_initial_values()
neurons.set_states(initial_values)
neurons.I_ext = 0 * pA
# Set what to monitor
#state_monitor = b2.StateMonitor(neurons, self.get_states_to_monitor(), record=True)
spike_monitor = b2.SpikeMonitor(neurons)
# Run the simulation
net = b2.Network(neurons, spike_monitor)
net.run(500 * ms)
# Add step current
neurons.I_ext = steps
net.run(1000 * ms)
counts = spike_monitor.count
# Plot/return the f-I curve
if plot is True:
plt.plot(steps / pA, counts)
plt.title('f-I curve')
plt.ylabel('Firing rate [Hz]')
plt.xlabel('Current [pA]')
if max_rate is not None:
plt.ylim([0, max_rate])
plt.show()
else:
return counts
def make_model(self):
# Determine the simulation
if self.clamp_type == 'current':
eqs_input = '''I_inj = inj_input(t) : amp'''
elif self.clamp_type == 'dynamic':
eqs_input = '''I_exc = g_exc(t) * (Er_e - v) : amp
I_inh = g_inh(t) * (Er_i - v) : amp
I_inj = I_exc + I_inh : amp'''
tracking = ['v', 'I_inj']
# Model the neuron with differential equations
eqs = '''
# Activation gates Na channel
m = 1. / (1. + exp(-(v - Vh) / k)) : 1
Vh = 3.223725 * k - 62.615488*mV : volt
# Inactivation gates Na channel
dh/dt = 5. * (alpha_h * (1 - h)- beta_h * h) : 1
alpha_h = 0.07 * exp(-(v + 58.*mV) / (20.*mV))/ms : Hz
beta_h = 1. / (exp(-0.1/mV * (v + 28.*mV)) + 1.)/ms : Hz
# Activation gates K channel
dn/dt = 5. * (alpha_n * (1. - n) - beta_n * n) : 1
alpha_n = 0.01/mV * 10*mV / exprel(-(v + 34.*mV) / (10.*mV))/ms : Hz
beta_n = 0.125 * exp(-(v + 44.*mV) / (80.*mV))/ms : Hz
# Activation gates K3.1 channel
dn3/dt = alphan3 * (1. - n3) - betan3 * n3 : 1
alphan3 = (1. / exp(((param * ((-0.029 * v + (1.9*mV))/mV)))))/ms : Hz
betan3 = (1. / exp(((param * ((0.021 * v + (1.1*mV))/mV)))))/ms : Hz
# Currents
I_leak = -gL * (v - EL) : amp
I_Na = -gNa * m**3 * h * (v - ENa) : amp
I_K = -gK * n**4 * (v - EK) : amp
I_K3 = -gK3 * n3**4 * (v - EK) : amp
dv/dt = (I_leak + I_Na + I_K + I_K3 + I_inj) / Cm : volt
'''
# Neuron & parameter initialization
neuron = b2.NeuronGroup(1,
model=eqs + eqs_input,
method='exponential_euler',
threshold='m > 0.5',
refractory=2 * b2.ms,
reset=None,
dt=self.dt * b2.ms)
neuron.v = -65 * b2.mV
# Track the parameters during simulation
self.M = b2.StateMonitor(neuron, tracking, record=True)
self.S = b2.SpikeMonitor(neuron, record=True)
self.neuron = neuron
net = b2.Network(neuron)
net.add(self.M, self.S)
self.network = net
def run(TSTOP=250, group_size=100, g_time=150, neuron_n=1000, linear=True,
ext_w=0.2, inh_w=0.2):
"""Run a simulation and return the resulting spiketrain"""
# Basic equation of the model
eq = """dv/dt = -gamma*v + I0 : volt
Ie : volt
Ii : volt
"""
thetaU = 16 * mV
tauM = 8 * ms
gamma = 1 / tauM
I0 = 17.6 * mV / tauM
#Build the group of neuron to use
G = br2.NeuronGroup(neuron_n, threshold="v>thetaU", reset="v=0*mV",
method='euler',
model=eq)
#Record the spikes from this group
spikes = br2.SpikeMonitor(G)
#Build stimulation
stim = br2.SpikeGeneratorGroup(1, [0], [g_time]*ms - br2.defaultclock.dt)
stim_syn = br2.Synapses(stim, G, on_pre="v += 2*thetaU")
stim_syn.connect(i=0, j=np.arange(group_size))
br2.magic_network.schedule = ['start', 'groups', 'synapses', 'thresholds', 'resets', 'end']
connections = np.random.rand(1000, 1000) < 0.3
exc_or_inh = np.random.rand(1000, 1000) < 0.5
exc_i, exc_j = (connections & exc_or_inh).nonzero()
inh_i, inh_j = (connections & ~exc_or_inh).nonzero()
if linear:
G.run_regularly('''
v += Ie + Ii
Ie = 0*mV
Ii = 0*mV
''', when='after_synapses')
else:
G.run_regularly('''
v += clip(Ie, 0*mV, 2*mV) + clip(2*(Ie-2*mV), 0*mV, 4*mV) + Ii
Ie = 0*mV
Ii = 0*mV
''', when='after_synapses')
dt = br2.defaultclock.dt
exc_syn = br2.Synapses(G, G, on_pre='Ie += %s*mV' % (ext_w), delay=5*ms-dt)
inh_syn = br2.Synapses(G, G, on_pre='Ii -= %s*mV' % (inh_w), delay=5*ms-dt)
exc_syn.connect(i=exc_i, j=exc_j)
inh_syn.connect(i=inh_i, j=inh_j)
#Set random initial conditions
G.v = np.random.rand(neuron_n) * 16 * mV
br2.run(TSTOP * ms)
return spikes
def current_pulse_sim_with_opto(args, params=None):
if params is None:
params = get_neuron_params(args['NRN'])
params['Vclamp'] = -80
neurons, eqs = get_membrane_equation(params, [],\
return_equations=True)
if args['verbose']:
print(eqs)
fig, ax = brian2.subplots(figsize=(5,3))
# V value initialization
neurons.V = params['El']*brian2.mV
trace = brian2.StateMonitor(neurons, 'V', record=0)
spikes = brian2.SpikeMonitor(neurons)
# rest run
brian2.run(args['delay'] * brian2.ms)
# first pulse
neurons.I0 = args['amp']*brian2.pA
brian2.run(args['duration']/3. * brian2.ms)
neurons.Gclamp = 1e3*brian2.nS
brian2.run(args['duration']/3. * brian2.ms)
neurons.Gclamp = 0*brian2.nS
brian2.run(args['duration']/3. * brian2.ms)
# second pulse
neurons.I0 = 0
brian2.run(args['delay'] * brian2.ms)
# We draw nicer spikes
Vm = trace[0].V[:]
for t in spikes.t:
ax.plot(t/brian2.ms*np.ones(2),
[Vm[int(t/brian2.defaultclock.dt)]/brian2.mV,-10],
'--', color=args['color'])
ax.plot(trace.t / brian2.ms, Vm / brian2.mV, color=args['color'])
if 'NRN' in args.keys():
ax.set_title(args['NRN'])
ax.annotate(str(int(params['El']))+'mV', (-50,params['El']-5))
ax.plot([-20], [params['El']], 'k>')
ax.plot([0,50], [-50, -50], 'k-', lw=4)
ax.plot([0,0], [-50, -40], 'k-', lw=4)
ax.annotate('10mV', (-50,-38))
ax.annotate('50ms', (0,-55))
# set_plot(ax, [], xticks=[], yticks=[])
# show()
if 'save' in args.keys():
fig.savefig(args['save'])
return fig
def simulate(runtime=0.5*b2.second, N=1):
b2.start_scope()
namespace['sigma'] = 2 * b2.mV
namespace['tau_m_E'] = namespace['C_m_E'] / namespace['g_m_E']
# I_1 = -2*b2.namp
# I_2 = -20*b2.namp
I_1 = namespace['g_m_E'] * (namespace['V_L'] - (2.5*b2.mV + namespace['V_thr']))
I_2 = namespace['g_m_E'] * (namespace['V_L'] - (-2.5*b2.mV + namespace['V_thr']))
eqn = """
dV/dt = (- g_m_E * (V - V_L) - I) / C_m_E + sigma*xi*tau_m_E**-0.5: volt (unless refractory)
I : amp
"""
N1 = b2.NeuronGroup(
N, eqn, threshold='V>V_thr',
reset='V = V_reset',
refractory=namespace['tau_rp_E'],
method='euler')
N1.V = namespace['V_reset']
N1.I = I_1
N2 = b2.NeuronGroup(
N, eqn, threshold='V>V_thr',
reset='V = V_reset',
refractory=namespace['tau_rp_E'],
method='euler')
N2.V = namespace['V_reset']
N2.I = I_2
st1 = b2.StateMonitor(N1, variables='V', record=True, name='st1')
st2 = b2.StateMonitor(N2, variables='V', record=True, name='st2')
sp1 = b2.SpikeMonitor(N1, name='sp1')
sp2 = b2.SpikeMonitor(N2, name='sp2')
net = b2.Network(b2.collect())
net.run(runtime, namespace=namespace, report='stdout')
return net
def main1():
bs.start_scope()
# Parameters
area = 20000 * bs.umetre**2
Cm = 1 * bs.ufarad * bs.cm**-2 * area
gl = 5e-5 * bs.siemens * bs.cm**-2 * area
El = -65 * bs.mV
EK = -90 * bs.mV
ENa = 50 * bs.mV
g_na = 100 * bs.msiemens * bs.cm**-2 * area
g_kd = 30 * bs.msiemens * bs.cm**-2 * area
VT = -63 * bs.mV
# HH stands for Hudgkin-Huxley
eqs_HH = '''
dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
(exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
(exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
(exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
I : amp
'''
group = bs.NeuronGroup(1,
eqs_HH,
threshold='v > -40*mV',
refractory='v > -40*mV',
method='exponential_euler')
group.v = El
state_monitor = bs.StateMonitor(group, 'v', record=True)
spike_monitor = bs.SpikeMonitor(group, variables='v')
plt.figure(figsize=(9, 4))
for l in range(5):
group.I = bs.rand() * 50 * bs.nA
bs.run(10 * bs.ms)
bs.axvline(l * 10, ls='--', c='k')
bs.axhline(El / bs.mV, ls='-', c='lightgray', lw=3)
state_time = state_monitor.t
spike_time = spike_monitor.t
plt.plot(state_time / bs.ms, spike_monitor.v[0] / bs.mV, '-b')
plt.plot(spike_time / bs.ms, spike_monitor.v / bs.mV, 'ob')
plt.xlabel('Time (ms)')
plt.ylabel('v (mV)')
plt.show()
def _create_device(self, group, variable):
"""Create a Brian2 recording device."""
# Brian2 records in the 'start' scheduling slot by default
if variable == 'spikes':
self._devices[variable] = brian2.SpikeMonitor(group, record=self.recorded)
else:
varname = self.population.celltype.state_variable_translations[variable]['translated_name']
neurons_to_record = np.sort(np.fromiter(
self.recorded[variable], dtype=int)) - self.population.first_id
self._devices[variable] = brian2.StateMonitor(group, varname,
record=neurons_to_record,
when='end',
dt=self.sampling_interval * ms)
simulator.state.network.add(self._devices[variable])
def simulate_neuron(self,
I_stim=input_factory.get_zero_current(),
simulation_time=1000 * ms,
**kwargs):
"""
Simulate/stimulate the neuron
:param I_stim: input stimulus (use the input_factory to create the stimulus)
:param simulation_time: duration (usually in milliseconds, eg. 3000*ms)
:param kwargs: custom neuron parameters can be given as arguments
:return: b2.StateMonitor, b2.SpikeMonitor
"""
neuron_parameters = dict(
self.neuron_parameters
) # Make a copy of parameters; otherwise will change object params
neuron_parameters.update(kwargs)
refractory_period = neuron_parameters['refractory_period']
stim_string = '+ I_stim(t,i)'
old_model_defns = dict(self.full_model_defns)
self.add_model_definition('EXT_CURRENTS', stim_string)
eqs = self.get_membrane_equation()
self.full_model_defns = old_model_defns
# Create a neuron group
neuron = b2.NeuronGroup(1,
model=eqs,
namespace=neuron_parameters,
reset=self.reset_statements,
threshold=self.threshold_condition,
refractory=refractory_period,
method=self.integration_method)
# Set initial values
initial_values = self.get_initial_values()
neuron.set_states(initial_values)
# Set what to monitor
state_monitor = b2.StateMonitor(neuron,
self.get_states_to_monitor(),
record=True)
spike_monitor = b2.SpikeMonitor(neuron)
# Run the simulation
net = b2.Network(neuron, state_monitor, spike_monitor)
net.run(simulation_time)
return state_monitor, spike_monitor
def simulate(self,
current=input_factory.get_zero_current(),
time=10 * b2.ms,
rheo_threshold=None,
v_rest=None,
v_reset=None,
delta_t=None,
time_scale=None,
resistance=None,
threshold=None,
adapt_volt_c=None,
adapt_tau=None,
adapt_incr=None):
if (v_rest == None):
v_rest = self.rest_pot
if (v_reset == None):
v_reset = self.reset_pot
if (rheo_threshold == None):
rheo_threshold = self.rheo_threshold
if (delta_t == None):
delta_t = self.delta_t
if (time_scale == None):
time_scale = self.time_scale
if (resistance == None):
resistance = self.resistance
if (threshold == None):
threshold = self.threshold
if (adapt_volt_c == None):
adapt_volt_c = self.adapt_volt_c
if (adapt_tau == None):
adapt_tau = self.adapt_tau
if (adapt_incr == None):
adapt_incr = self.adapt_incr
v_spike_str = "v>{:f}*mvolt".format(threshold / b2.mvolt)
eqs = """
dv/dt = (-(v-v_rest) +delta_t*exp((v-rheo_threshold)/delta_t)+ resistance * current(t,i) - resistance * w)/(time_scale) : volt
dw/dt=(adapt_volt_c*(v-v_rest)-w)/adapt_tau : amp
"""
neuron = b2.NeuronGroup(1,
model=eqs,
threshold=v_spike_str,
reset="v=v_reset;w+=adapt_incr",
method="euler")
neuron.v = v_rest
neuron.w = 0.0 * b2.pA
state_monitor = b2.StateMonitor(neuron, ["v", "w"], record=True)
spike_monitor = b2.SpikeMonitor(neuron)
b2.run(time)
return state_monitor, spike_monitor
def __init__(self, xmax, ymax, rmax, dvsSignal):
self.xmax = xmax
self.ymax = ymax
self.rmax = rmax
#self.rmin = 2
self.dvsSignal = dvsSignal
self.v_update = 0.5 * brian2.mvolt
self.group = snn.snn(self.xmax, self.ymax, self.rmax)
self.network_op = brian2.NetworkOperation(self.update_func,
dt=100 * brian2.ms)
#self.synapses = snn.inhibition(self.group)
self.spikeM = brian2.SpikeMonitor(self.group)
self.network = brian2.Network(self.group, self.network_op,
self.spikeM) #, self.synapses)
def main6():
# neurons with parameters
n_neurons = 100
tau = 10 * bs.ms
v0_max = 3.0
duration = 1000 * bs.ms
# v0: 1 declares a new per-neuron parameter v0
eqs = """
dv/dt = (v0-v)/tau: 1 (unless refractory)
v0 : 1
"""
# conditions for spiking models
threshold = 'v>1'
reset = 'v = 0'
refractory = 5 * bs.ms
G = bs.NeuronGroup(N=n_neurons, model=eqs, threshold=threshold, reset=reset, method='exact', refractory=refractory)
state_monitor = bs.StateMonitor(G, 'v', record=True)
spike_monitor = bs.SpikeMonitor(G)
"""
The line G.v0 = 'i*v0_max/(N-1)' initialises the value of v0 for each neuron varying from 0 up to v0_max.
The symbol i when it appears in strings like this refers to the neuron index.
"""
G.v0 = 'i*v0_max/(N-1)'
bs.run(duration=duration)
plt.figure(figsize=(12, 4))
plt.subplot(121)
plt.plot(spike_monitor.t / bs.ms, spike_monitor.i, '.k')
plt.xlabel('Time (ms)')
plt.ylabel('Neuron index')
plt.subplot(122)
plt.plot(G.v0, spike_monitor.count / duration)
plt.xlabel('v0')
plt.ylabel('Firing rate (sp/s)')
plt.show()
plt.clf()
# plt.plot(state_monitor.t / bs.ms, state_monitor.v[0])
# plt.plot(state_monitor.t / bs.ms, state_monitor.v[10])
# plt.plot(state_monitor.t / bs.ms, state_monitor.v[50])
plt.plot(state_monitor.t / bs.ms, state_monitor.v[30])
plt.plot(state_monitor.t / bs.ms, state_monitor.v[40])
plt.plot(state_monitor.t / bs.ms, state_monitor.v[70])
plt.xlabel('Time (ms)')
plt.ylabel('v')
plt.show()
def simulate_exponential_IF_neuron(tau=MEMBRANE_TIME_SCALE_tau,
R=MEMBRANE_RESISTANCE_R,
v_rest=V_REST,
v_reset=V_RESET,
v_rheobase=RHEOBASE_THRESHOLD_v_rh,
v_spike=FIRING_THRESHOLD_v_spike,
delta_T=SHARPNESS_delta_T,
I_stim=input_factory.get_zero_current(),
simulation_time=200 * b2.ms):
"""
Implements the dynamics of the exponential Integrate-and-fire model
Args:
tau (Quantity): Membrane time constant
R (Quantity): Membrane resistance
v_rest (Quantity): Resting potential
v_reset (Quantity): Reset value (vm after spike)
v_rheobase (Quantity): Rheobase threshold
v_spike (Quantity) : voltage threshold for the spike condition
delta_T (Quantity): Sharpness of the exponential term
I_stim (TimedArray): Input current
simulation_time (Quantity): Duration for which the model is simulated
Returns:
(voltage_monitor, spike_monitor):
A b2.StateMonitor for the variable "v" and a b2.SpikeMonitor
"""
eqs = """
dv/dt = (-(v-v_rest) +delta_T*exp((v-v_rheobase)/delta_T)+ R * I_stim(t,i))/(tau) : volt
"""
neuron = b2.NeuronGroup(1,
model=eqs,
reset="v=v_reset",
threshold="v>v_spike",
method="euler")
neuron.v = v_rest
# monitoring membrane potential of neuron and injecting current
voltage_monitor = b2.StateMonitor(neuron, ["v"], record=True)
spike_monitor = b2.SpikeMonitor(neuron)
# run the simulation
net = b2.Network(neuron, voltage_monitor, spike_monitor)
net.run(simulation_time)
return voltage_monitor, spike_monitor
def brian_poisson(rate, duration_ms, dt=1 * ms, n=1):
"""
Generates a sample of poisson neurons with a specified average frequency.
Returns the sample as a list of neurons
:param rate: The average firing frequency of each neuron in the sample
:param duration_ms: Length of a single "trial" in ms
:param dt: The shortest time interval between spikes in the sample
:param n: Number of neurons in the sample
NOTE: the "rate" and "duration_ms" parameters can also vary between neurons,
if this is the desired behaviour, they should be given as a list of length "n"
where each entry corresponds to the parameters of a single neuron in the sample
:return: List of neurons in the sample stimulus, in each neuron, firing times are specified in milliseconds
"""
q = b2.units.fundamentalunits.Quantity # Type used to represent units in brian
# Check that all parameters comply with units expected by brian
if not isinstance(rate, q): # Rate is expected to be given in Hz
if np.isscalar(rate):
rate = rate * Hz
else:
rate = np.array(rate) * Hz
if not isinstance(duration_ms,
q): # Duration is expected to be specified in ms
if np.isscalar(duration_ms):
duration_ms = duration_ms * ms
else:
duration_ms = np.array(duration_ms) * ms
# Specify properties of brian neuron group
neuron = b2.NeuronGroup(n, "rate : Hz", threshold='rand()<rate*dt', dt=dt)
neuron.rate = rate
spikes = b2.SpikeMonitor(
neuron, record=True
) # Set up monitoring (required for gathering spike timing information)
b2.run(duration_ms) # Run simulation with the specified parameters
# Extract spike timings from brian objects
if n == 1:
trains = spikes.spike_trains()[0] / dt
else:
trains = [train / dt for train in spikes.spike_trains().values()]
return trains
def make_model(self):
# Determine the simulation
if self.clamp_type == 'current':
eqs_input = '''I_inj = inj_input(t) : amp'''
elif self.clamp_type == 'dynamic':
eqs_input = '''I_exc = g_exc(t) * (Er_e - v) : amp
I_inh = g_inh(t) * (Er_i - v) : amp
I_inj = I_exc + I_inh : amp'''
tracking = ['v', 'I_inj']
# Model the neuron with differential equations
eqs = '''
Vh_m = 3.583881 * k_m - 53.294454*mV : volt
m = 1 / (1 + exp(-(v - Vh_m) / k_m)) : 1
h = 1 / (1 + exp((v - Vh_h) / k_h)) : 1
alpha_n = (0.032 * 5. / exprel((15. -v/mV + VT/mV) / 5.))/ms : Hz
beta_n = (0.5 * exp((10. - v/mV + VT/mV) / 40.))/ms : Hz
dn/dt = alpha_n * (1 - n) - beta_n * n : 1
I_leak = -gL * (v - EL) : amp
I_Na = -gNa * m**3 * h * (v - ENa) : amp
I_K = -gK * n**4 * (v - EK) : amp
dv/dt = (I_leak + I_Na + I_K + I_inj) / Cm : volt
'''
# Neuron & parameter initialization
neuron = b2.NeuronGroup(1,
model=eqs + eqs_input,
method='exponential_euler',
threshold='m > 0.5',
refractory=2 * b2.ms,
reset=None,
dt=self.dt * b2.ms)
neuron.v = -65 * b2.mV
# Track the parameters during simulation
self.M = b2.StateMonitor(neuron, tracking, record=True)
self.S = b2.SpikeMonitor(neuron, record=True)
self.neuron = neuron
net = b2.Network(neuron)
net.add(self.M, self.S)
self.network = net
def simulate_LIF_neuron(input_current,
simulation_time=5 * b2.ms,
v_rest=V_REST,
v_reset=V_RESET,
firing_threshold=FIRING_THRESHOLD,
membrane_resistance=MEMBRANE_RESISTANCE,
membrane_time_scale=MEMBRANE_TIME_SCALE,
abs_refractory_period=ABSOLUTE_REFRACTORY_PERIOD):
"""Basic leaky integrate and fire neuron implementation.
Args:
input_current (TimedArray): TimedArray of current amplitudes. One column per current_injection_location.
simulation_time (Quantity): Time for which the dynamics are simulated: 5ms
v_rest (Quantity): Resting potential: -70mV
v_reset (Quantity): Reset voltage after spike - 65mV
firing_threshold (Quantity) Voltage threshold for spiking -50mV
membrane_resistance (Quantity): 10Mohm
membrane_time_scale (Quantity): 8ms
abs_refractory_period (Quantity): 2ms
Returns:
StateMonitor: Brian2 StateMonitor for the membrane voltage "v"
SpikeMonitor: Biran2 SpikeMonitor
"""
# differential equation of Leaky Integrate-and-Fire model
eqs = """
dv/dt =
( -(v-v_rest) + membrane_resistance * input_current(t,i) ) / membrane_time_scale : volt (unless refractory)"""
# LIF neuron using Brian2 library
neuron = b2.NeuronGroup(1,
model=eqs,
reset="v=v_reset",
threshold="v>firing_threshold",
refractory=abs_refractory_period,
method="linear")
neuron.v = v_rest # set initial value
# monitoring membrane potential of neuron and injecting current
state_monitor = b2.StateMonitor(neuron, ["v"], record=True)
spike_monitor = b2.SpikeMonitor(neuron)
# run the simulation
b2.run(simulation_time)
return state_monitor, spike_monitor
def init_monitors(neurons, connections, monitor_params):
"""
Initialise Brian objects monitoring state variables in the network.
"""
monitors = {
'spikes': {},
'neurons': {},
'connections': {}
}
for layer in ['input', 'layer1e']:
monitors['spikes'][layer] = b2.SpikeMonitor(neurons[layer])
if 'monitors_dt' not in monitor_params:
timestep = None
else:
timestep = monitor_params['monitors_dt']
monitors['neurons']['layer1e'] = b2.StateMonitor(
neurons['layer1e'],
['v', 'ge', 'max_ge', 'theta'],
# record=True is currently broken for standalone simulations
record=range(len(neurons['layer1e'])),
dt=timestep
)
if 'layer1vis' in neurons:
monitors['neurons']['layer1vis'] = b2.StateMonitor(
neurons['layer1vis'],
['v'],
# record=True is currently broken for standalone simulations
record=range(len(neurons['layer1vis'])),
dt=b2.second/60
)
conn = connections['input-layer1e']
n_connections = len(conn.target) * len(conn.source)
monitors['connections']['input-layer1e'] = b2.StateMonitor(
connections['input-layer1e'],
['w', 'post', 'pre'],
record=range(n_connections),
dt=timestep
)
return monitors
def main7():
# stochastic neurons
# multiple neurons
n_neurons = 100
tau = 10 * bs.ms
v0_max = 3.0
duration = 1000 * bs.ms
sigma = 0.2
# v0: 1 declares a new per-neuron parameter v0
# n Brian, we can do this by using the symbol xi in differential equations.
# Strictly speaking, this symbol is a “stochastic differential” but you can sort of thinking of it as just a
# Gaussian random variable with mean 0 and standard deviation 1.
eqs = '''
dv/dt = (v0-v)/tau+sigma*xi*tau**-0.5 : 1 (unless refractory)
v0 : 1
'''
# conditions for spiking models
threshold = 'v>1'
reset = 'v = 0'
refractory = 5 * bs.ms
G = bs.NeuronGroup(N=n_neurons, model=eqs, threshold=threshold, reset=reset, method='euler', refractory=refractory)
state_monitor = bs.StateMonitor(G, 'v', record=True)
spike_monitor = bs.SpikeMonitor(G)
"""
The line G.v0 = 'i*v0_max/(N-1)' initialises the value of v0 for each neuron varying from 0 up to v0_max.
The symbol i when it appears in strings like this refers to the neuron index.
"""
G.v0 = 'i*v0_max/(N-1)'
bs.run(duration=duration)
plt.figure(figsize=(12, 4))
plt.subplot(121)
plt.plot(spike_monitor.t / bs.ms, spike_monitor.i, '.k')
plt.xlabel('Time (ms)')
plt.ylabel('Neuron index')
plt.subplot(122)
plt.plot(G.v0, spike_monitor.count / duration)
plt.xlabel('v0')
plt.ylabel('Firing rate (sp/s)')
plt.show()
def simulate_neuron(self,
I_stim=input_factory.get_zero_current(),
simulation_time=1000 * ms,
**kwargs):
neuron_parameters = dict(
self.neuron_parameters
) # Make a copy of parameters; otherwise will change object params
neuron_parameters.update(kwargs)
refractory_period = neuron_parameters['refractory_period']
#eqs = self.get_membrane_equation(substitute_ad_hoc={'EXT_CURRENTS': '+ I_stim(t,i)'})
stim_string = '+ I_stim(t,i)'
old_model_defns = dict(self.full_model_defns)
self.add_model_definition('EXT_CURRENTS', stim_string)
eqs = self.get_membrane_equation()
self.full_model_defns = old_model_defns
# Create a neuron group
neuron = b2.NeuronGroup(1,
model=eqs,
namespace=neuron_parameters,
reset=self.reset_statements,
threshold=self.threshold_condition,
refractory=refractory_period,
method=self.integration_method)
# Set initial values
initial_values = self.get_initial_values()
neuron.set_states(initial_values)
# Set what to monitor
state_monitor = b2.StateMonitor(neuron,
self.get_states_to_monitor(),
record=True)
spike_monitor = b2.SpikeMonitor(neuron)
# Run the simulation
net = b2.Network(neuron, state_monitor, spike_monitor)
net.run(simulation_time)
return state_monitor, spike_monitor
def simulate(tau):
# These two lines are needed to start a new standalone simulation:
b2.device.reinit()
b2.device.activate()
eqs = '''
dv/dt = -v/tau : 1
'''
net = b2.Network()
P = b2.PoissonGroup(num_inputs, rates=input_rate)
G = b2.NeuronGroup(1, eqs, threshold='v>1', reset='v=0', method='euler')
S = b2.Synapses(P, G, on_pre='v += weight')
S.connect()
M = b2.SpikeMonitor(G)
net.add([P, G, S, M])
net.run(1000 * b2.ms)
return M