def __init__(self, filterbank, targetvar, *args, **kwds):
self.targetvar = targetvar
self.filterbank = filterbank
filterbank.buffer_init()
# update level keyword
kwds['level'] = kwds.get('level', 0) + 1
# Sanitize the clock - does it have the right dt value?
if 'clock' in kwds:
if int(1 / kwds['clock'].dt) != int(filterbank.samplerate):
raise ValueError('Clock should have 1/dt=samplerate')
else:
kwds['clock'] = Clock(dt=1 / filterbank.samplerate)
buffersize = kwds.pop('buffersize', 32)
if not isinstance(buffersize, int):
buffersize = int(buffersize * self.samplerate)
self.buffersize = buffersize
self.buffer_pointer = buffersize
self.buffer_start = -buffersize
NeuronGroup.__init__(self, filterbank.nchannels, *args, **kwds)
@network_operation(when='start', clock=self.clock)
def apply_filterbank_output():
if self.buffer_pointer >= self.buffersize:
self.buffer_pointer = 0
self.buffer_start += self.buffersize
self.buffer = self.filterbank.buffer_fetch(
self.buffer_start, self.buffer_start + self.buffersize)
setattr(self, targetvar, self.buffer[self.buffer_pointer, :])
self.buffer_pointer += 1
self.contained_objects.append(apply_filterbank_output)
def __init__(self, params=zheng_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params = params
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.cb = self.cb_0
self.g = self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = 'refractory'
self.model.add_param('refractory', second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0 * ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
self.group = NeuronGroup(self.neurons,
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory=self.max_refractory,
method=self.method,
clock=Clock(dt=self.dt))
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def initialize(self,tau_metric):
self.delay_range =max(self.delays)- min(self.delays)#delay range
self.min_delay = abs(min(self.delays))#minimum of possible delay
self.corr_vector=zeros(self.N)
self.norm_pop = zeros(self.N)
self.norm_target = zeros(self.N)
self.nbr_neurons_group = self.N/self.K
eqs="""
tau:second
dv/dt=(-v)/tau: volt
"""
# network to convolve target spikes with the kernel
self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock)
self.kernel_target=NeuronGroup(self.N,model=eqs,clock=self.group.clock)
self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='sparse', max_delay=self.min_delay)
self.kernel_target.tau=tau_metric
for igroup in xrange(self.K):
self.C_target.W[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = ones(self.nbr_neurons_group)
self.C_target.delay[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = self.min_delay * ones(self.nbr_neurons_group)
# network to convolve population spikes with the kernel
self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock)
self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range)
for iN in xrange(self.N):
self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN])
self.C_population.connect_one_to_one(self.group,self.kernel_population)
self.kernel_population.tau=tau_metric
self.spikecount = SpikeCounter(self.group)
self.contained_objects = [self.kernel_population,self.C_population,self.spikecount,self.input_target,self.C_target,self.kernel_target]
def ousim(mu_amp, mu_offs, sigma_amp, sigma_offs, freq, V_th):
# mu_amp, mu_offs, sigma_amp, sigma_offs, freq, V_th = config
if sigma_amp > sigma_offs:
sigma_amp = sigma_offs
# print("Setting up OU LIF simulation...")
ounet = Network()
clock.reinit_default_clock()
eqs =Equations('dV/dt = mu-(V+V0)/tau + sigma*I/sqrt(dt) : volt')
eqs+=Equations('dI/dt = -I/dt + xi/sqrt(dt) : 1')
eqs+=Equations('mu = mu_amp*sin(t*freq*2*pi) + mu_offs : volt/second')
eqs+=Equations('sigma = sigma_amp*sin(t*freq*2*pi) + sigma_offs :'
' volt/sqrt(second)')
eqs.prepare()
ounrn = NeuronGroup(1, eqs, threshold=V_th, refractory=t_refr,
reset=V_reset)
ounet.add(ounrn)
ounrn.V = V0
V_mon = StateMonitor(ounrn, 'V', record=True)
st_mon = SpikeMonitor(ounrn)
ounet.add(V_mon, st_mon)
ounet.run(duration)
V_mon.insert_spikes(st_mon, value=V_th*2)
times = V_mon.times
membrane = V_mon[0]
return times, st_mon.spiketimes[0], membrane
def pif_reset():
defaultclock.reinit()
sim = Network()
I = 0.2*nA
R = 1*Mohm
lifeq = """
dV/dt = I*R/ms : volt
Vth : volt
"""
thstep = 15*mV
nrn = NeuronGroup(1, lifeq, threshold="V>=Vth", reset="V=0*mV")
nrn.V = 0*mV
nrn.Vth = thstep
sim.add(nrn)
#connection = Connection(inputgrp, nrn, state="V", weight=0.5*mV)
#sim.add(inputgrp, connection)
vmon = StateMonitor(nrn, "V", record=True)
thmon = StateMonitor(nrn, "Vth", record=True)
spikemon = SpikeMonitor(nrn, record=True)
sim.add(vmon, thmon, spikemon)
sim.run(duration)
return vmon, thmon, spikemon
def __init__(self, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
cmr_o : 1
cb : 1
g : 1
c_ab : 1
cb_0 : 1
g_0 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True)
self.params=params
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.cb=self.cb_0
self.g=self.g_0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, filterbank, targetvar, *args, **kwds):
self.targetvar = targetvar
self.filterbank = filterbank
filterbank.buffer_init()
# update level keyword
kwds['level'] = kwds.get('level', 0)+1
# Sanitize the clock - does it have the right dt value?
if 'clock' in kwds:
if int(1/kwds['clock'].dt)!=int(filterbank.samplerate):
raise ValueError('Clock should have 1/dt=samplerate')
else:
kwds['clock'] = Clock(dt=1/filterbank.samplerate)
buffersize = kwds.pop('buffersize', 32)
if not isinstance(buffersize, int):
buffersize = int(buffersize*self.samplerate)
self.buffersize = buffersize
self.buffer_pointer = buffersize
self.buffer_start = -buffersize
NeuronGroup.__init__(self, filterbank.nchannels, *args, **kwds)
@network_operation(when='start', clock=self.clock)
def apply_filterbank_output():
if self.buffer_pointer>=self.buffersize:
self.buffer_pointer = 0
self.buffer_start += self.buffersize
self.buffer = self.filterbank.buffer_fetch(self.buffer_start, self.buffer_start+self.buffersize)
setattr(self, targetvar, self.buffer[self.buffer_pointer, :])
self.buffer_pointer += 1
self.contained_objects.append(apply_filterbank_output)
def generate_data():
g = NeuronGroup(1, model=equations, reset=0, threshold=1)
g.I = TimedArray(input, dt=.1*ms)
g.tau = 25*ms
g.R = 3e9
SpM = SpikeMonitor(g)
StM = StateMonitor(g, 'V', record=True)
net = Network(g, SpM, StM)
net.run(1*second)
return StM.values[0], SpM.spikes
def build(self, traj, brian_list, network_dict):
if not self.pre_built:
eqs = Equations(traj.model, tau=traj.tau)
ng = NeuronGroup(traj.N, eqs,
threshold=traj.threshold,
reset=traj.reset,
refractory=traj.refr)
ng.v0 = traj.v00
brian_list.append(ng)
network_dict['group'] = ng
def generate_data():
g = NeuronGroup(1, model=equations, reset=0, threshold=1)
g.I = TimedArray(input, dt=.1 * ms)
g.tau = 25 * ms
g.R = 3e9
SpM = SpikeMonitor(g)
StM = StateMonitor(g, 'V', record=True)
net = Network(g, SpM, StM)
net.run(1 * second)
return StM.values[0], SpM.spikes
def test_stim_pyramidal_impact():
simulation_clock=Clock(dt=.5*ms)
trial_duration=1*second
dcs_start_time=.5*second
stim_levels=[-8,-6,-4,-2,-1,-.5,-.25,0,.25,.5,1,2,4,6,8]
voltages = np.zeros(len(stim_levels))
for idx,stim_level in enumerate(stim_levels):
print('testing stim_level %.3fpA' % stim_level)
eqs = exp_IF(default_params.C, default_params.gL, default_params.EL, default_params.VT, default_params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=default_params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=default_params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=default_params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', default_params.tau1_nmda, default_params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=default_params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=default_params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', default_params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=default_params.E_gaba_a)
eqs +=InjectedCurrent('I_dcs: amp')
group=NeuronGroup(1, model=eqs, threshold=-20*mV, refractory=pyr_params.refractory, reset=default_params.Vr,
compile=True, freeze=True, clock=simulation_clock)
group.C=pyr_params.C
group.gL=pyr_params.gL
@network_operation(clock=simulation_clock)
def inject_current(c):
if simulation_clock.t>dcs_start_time:
group.I_dcs=stim_level*pA
monitor=StateMonitor(group, 'vm', simulation_clock, record=True)
net=Network(group, monitor, inject_current)
net.run(trial_duration, report='text')
voltages[idx]=monitor.values[0,-1]*1000
voltages=voltages-voltages[7]
plt.figure()
plt.plot(stim_levels,voltages)
plt.xlabel('Stimulation level (pA)')
plt.ylabel('Voltage Change (mV)')
plt.show()
def __init__(self, pyramidal_group, clock=defaultclock):
eqs = Equations('''
LFP : amp
''')
NeuronGroup.__init__(self,
1,
model=eqs,
compile=True,
freeze=True,
clock=clock)
self.LFP = linked_var(pyramidal_group, 'I_abs', func=sum)
def build(self, traj, brian_list, network_dict):
if not self.pre_built:
eqs = Equations(traj.model, tau=traj.tau)
ng = NeuronGroup(traj.N,
eqs,
threshold=traj.threshold,
reset=traj.reset,
refractory=traj.refr)
ng.v0 = traj.v00
brian_list.append(ng)
network_dict['group'] = ng
def __init__(self, clock, params=default_params, network=None):
eqs = Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_base = params.v_base
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1
self.f_in = 1
self.s = 0
self.v = 1
self.q = 1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
class BretteCriterion(Criterion):
def initialize(self,tau_metric):
self.delay_range =max(self.delays)- min(self.delays)#delay range
self.min_delay = abs(min(self.delays))#minimum of possible delay
self.corr_vector=zeros(self.N)
self.norm_pop = zeros(self.N)
self.norm_target = zeros(self.N)
self.nbr_neurons_group = self.N/self.K
eqs="""
tau:second
dv/dt=(-v)/tau: volt
"""
# network to convolve target spikes with the kernel
self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock)
self.kernel_target=NeuronGroup(self.N,model=eqs,clock=self.group.clock)
self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='sparse', max_delay=self.min_delay)
self.kernel_target.tau=tau_metric
for igroup in xrange(self.K):
self.C_target.W[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = ones(self.nbr_neurons_group)
self.C_target.delay[igroup,igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] = self.min_delay * ones(self.nbr_neurons_group)
# network to convolve population spikes with the kernel
self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock)
self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range)
for iN in xrange(self.N):
self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN])
self.C_population.connect_one_to_one(self.group,self.kernel_population)
self.kernel_population.tau=tau_metric
self.spikecount = SpikeCounter(self.group)
self.contained_objects = [self.kernel_population,self.C_population,self.spikecount,self.input_target,self.C_target,self.kernel_target]
def __call__(self):
trace_population = self.kernel_population.state_('v')
trace_target = self.kernel_target.state_('v')
self.corr_vector += trace_population*trace_target
self.norm_pop += trace_population**2
self.norm_target += trace_target**2
def get_values(self):
return (self.corr_vector,self.norm_pop,self.norm_target)
def normalize(self, values):
corr_vector=values[0]
norm_pop=values[1]
norm_target=values[2]
corr_vector[nonzero(self.spikecount.count==0)] = -inf
#print self.corr_vector/sqrt(norm_pop)/sqrt(norm_target)
return self.corr_vector/sqrt(norm_pop)/sqrt(norm_target)
def __init__(self, clock, params=default_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
o_e=1-(1-e_base)**(1/f_in) : 1
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_base*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
e_base : 1
v_base : 1
k1 : 1
k2 : 1
k3 : 1
''')
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_base=params.v_base
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1
self.f_in=1
self.s=0
self.v=1
self.q=1
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = 'refractory'
self.model.add_param('refractory', second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0*ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
# print refractory, self.max_refractory
if type(refractory) is double:
refractory=refractory*second
# if self.give_neuron_group == False:
self.group = NeuronGroup(self.neurons, # TODO: * slices?
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory = self.max_refractory,
method = self.method,
clock=Clock(dt=self.dt))
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = "refractory"
self.model.add_param("refractory", second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0 * ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
self.group = NeuronGroup(
self.neurons,
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory=self.max_refractory,
method=self.method,
clock=Clock(dt=self.dt),
)
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def _build_model(self, traj, brian_list, network_dict):
"""Builds the neuron groups from `traj`.
Adds the neuron groups to `brian_list` and `network_dict`.
"""
model = traj.parameters.model
# Create the equations for both models
eqs_dict = self._build_model_eqs(traj)
# Create inhibitory neurons
eqs_i = eqs_dict['i']
neurons_i = NeuronGroup(N=model.N_i,
model=eqs_i,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Create excitatory neurons
eqs_e = eqs_dict['e']
neurons_e = NeuronGroup(N=model.N_e,
model=eqs_e,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Set the bias terms
neurons_e.mu = rand(
model.N_e) * (model.mu_e_max - model.mu_e_min) + model.mu_e_min
neurons_i.mu = rand(
model.N_i) * (model.mu_i_max - model.mu_i_min) + model.mu_i_min
# Set initial membrane potentials
neurons_e.V = rand(model.N_e)
neurons_i.V = rand(model.N_i)
# Add both groups to the `brian_list` and the `network_dict`
brian_list.append(neurons_i)
brian_list.append(neurons_e)
network_dict['neurons_e'] = neurons_e
network_dict['neurons_i'] = neurons_i
class VanRossumCriterion(Criterion):
def initialize(self, tau):
self.delay_range =max(self.delays)- min(self.delays)#delay range
self.min_delay = abs(min(self.delays))#minimum of possible delay
self.distance_vector=zeros(self.N)
self.nbr_neurons_group = self.N/self.K
eqs="""
dv/dt=(-v)/tau: volt
"""
# network to convolve target spikes with the kernel
self.input_target=SpikeGeneratorGroup(self.K,self.spikes,clock=self.group.clock)
self.kernel_target=NeuronGroup(self.K,model=eqs,clock=self.group.clock)
self.C_target = DelayConnection(self.input_target, self.kernel_target, 'v', structure='dense', max_delay=self.min_delay)
self.C_target.connect_one_to_one(self.input_target,self.kernel_target)
self.C_target.delay = self.min_delay*ones_like(self.C_target.delay)
# network to convolve population spikes with the kernel
self.kernel_population=NeuronGroup(self.N,model=eqs,clock=self.group.clock)
self.C_population = DelayConnection(self.group, self.kernel_population, 'v', structure='sparse', max_delay=self.delay_range)
for iN in xrange(self.N):
self.C_population.delay[iN,iN] = diagflat(self.min_delay + self.delays[iN])
self.C_population.connect_one_to_one(self.group,self.kernel_population)
self.spikecount = SpikeCounter(self.group)
self.contained_objects = [self.kernel_population,self.C_population,self.spikecount,self.input_target,self.C_target,self.kernel_target]
def __call__(self):
trace_population = self.kernel_population.state_('v')
trace_target = self.kernel_target.state_('v')
for igroup in xrange(self.K):
self.distance_vector[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group] += (trace_population[igroup*self.nbr_neurons_group:(1+igroup)*self.nbr_neurons_group]-trace_target[igroup])**2
def get_values(self):
return (self.distance_vector)
def normalize(self, distance_vector):
distance_vector[nonzero(self.spikecount.count==0)] = inf
return -self.distance_vector*self.group.clock.dt
def get_spikes(model=None, reset=None, threshold=None,
input=None, input_var='I', dt=None,
**params):
"""
Retrieves the spike times corresponding to the best parameters found by
the modelfitting function.
**Arguments**
``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt``
Same parameters as for the ``modelfitting`` function.
``**params``
The best parameters returned by the ``modelfitting`` function.
**Returns**
``spiketimes``
The spike times of the model with the given input and parameters.
"""
duration = len(input) * dt
ngroups = len(params[params.keys()[0]])
group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold,
clock=Clock(dt=dt))
group.set_var_by_array(input_var, TimedArray(input, clock=group.clock))
for param, values in params.iteritems():
if (param == 'delays') | (param == 'fitness'):
continue
group.state(param)[:] = values
M = SpikeMonitor(group)
net = Network(group, M)
net.run(duration)
reinit_default_clock()
return M.spikes
def fun(sigma, args):
"""
This function computes the mean firing rate of a LIF neuron with
white noise input current (OU process with threshold).
"""
if not isscalar(sigma):
raise Exception('sigma must be a scalar')
N = args['N']
tau = args['tau']
model = args['model']
reset = args['reset']
threshold = args['threshold']
duration = args['duration']
G = NeuronGroup(N, model=model, reset=reset, threshold=threshold)
M = SpikeCounter(G)
net = Network(G, M)
net.run(duration)
r = M.nspikes * 1.0 / N
return r
def _build_model(self, traj, brian_list, network_dict):
"""Builds the neuron groups from `traj`.
Adds the neuron groups to `brian_list` and `network_dict`.
"""
assert(isinstance(traj,SingleRun))
model = traj.parameters.model
# Create the equations for both models
eqs_dict = self._build_model_eqs(traj)
# Create inhibitory neurons
eqs_i = eqs_dict['i']
neurons_i = NeuronGroup(N=model.N_i,
model = eqs_i,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Create excitatory neurons
eqs_e = eqs_dict['e']
neurons_e = NeuronGroup(N=model.N_e,
model = eqs_e,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Set the bias terms
neurons_e.mu =rand(model.N_e) * (model.mu_e_max - model.mu_e_min) + model.mu_e_min
neurons_i.mu =rand(model.N_i) * (model.mu_i_max - model.mu_i_min) + model.mu_i_min
# Set initial membrane potentials
neurons_e.V = rand(model.N_e)
neurons_i.V = rand(model.N_i)
# Add both groups to the `brian_list` and the `network_dict`
brian_list.append(neurons_i)
brian_list.append(neurons_e)
network_dict['neurons_e']=neurons_e
network_dict['neurons_i']=neurons_i
def __init__(self, clock, params=zheng_params, network=None):
eqs = Equations(
'''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' %
(params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self,
1,
model=eqs,
clock=clock,
compile=True,
freeze=True)
self.params = params
self.G_base = params.G_base
self.eta = params.eta
self.tau_s = params.tau_s
self.tau_f = params.tau_f
self.alpha = params.alpha
self.tau_o = params.tau_o
self.e_base = params.e_base
self.v_0 = params.v_0
self.k1 = params.k1
self.params.s_e = params.s_e_0 * exp(-params.TE / params.T_2E)
self.params.s_i = params.s_i_0 * exp(-params.TE / params.T_2I)
self.params.beta = self.params.s_e / self.params.s_i
self.k2 = self.params.beta * params.r_0 * self.e_base * params.TE
self.k3 = self.params.beta - 1.0
self.c_ab = self.params.c_ab
self.cb_0 = self.params.cb_0
self.g_0 = self.params.g_0
self.phi = self.params.phi
self.v_ratio = self.params.v_ratio
self.j = self.params.j
self.transitTime = self.params.transitTime
self.k = self.params.k
self.r = self.params.r
self.f_in = 1.0
self.s = 0.0
self.v = 1.0
self.o_e = self.e_base
self.cb = self.cb_0
self.g = self.g_0
self.oe_log = np.log(1.0 - self.o_e / (1.0 - self.g))
self.q = 1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
def __init__(self, pyramidal_group, clock=defaultclock):
eqs=Equations('''
LFP : amp
''')
NeuronGroup.__init__(self, 1, model=eqs, compile=True, freeze=True, clock=clock)
self.LFP=linked_var(pyramidal_group, 'I_abs', func=sum)
def __init__(self,
lip_size,
params,
background_inputs=None,
visual_cortex_input=None,
go_input=None):
self.lip_size = lip_size
self.N = 2 * self.lip_size
self.params = params
self.background_inputs = background_inputs
self.visual_cortex_input = visual_cortex_input
self.go_input = go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda,
siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b,
params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations(
'g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens'
)
eqs += Equations(
'g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens'
)
# Total synaptic current
eqs += Equations(
'I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp'
)
NeuronGroup.__init__(self,
self.N,
model=eqs,
threshold=-20 * mV,
reset=params.EL,
compile=True)
self.init_subpopulations()
self.connections = []
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa = init_connection(self.background_inputs[0],
self.left_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
background_right_ampa = init_connection(
self.background_inputs[1],
self.right_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa = init_connection(self.visual_cortex_input[0],
self.left_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
vc_right_lip_ampa = init_connection(self.visual_cortex_input[1],
self.right_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa = init_connection(self.go_input,
self.left_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_right_lip_i_ampa = init_connection(self.go_input,
self.right_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_left_lip_e_ampa = init_connection(self.go_input,
self.left_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
go_right_lip_e_ampa = init_connection(self.go_input,
self.right_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
def hodgkin_huxley(duration=100, num=10, percent_excited=0.7, sample=1):
"""
The hodgkin_huxley function takes the following parameters:
duration - is the timeperiod to model for measured in milliseconds.
num - an integer value represeting the number of neurons you want to model.
percent_excited - a float between 0 and 1 representing the percentage of excited neurons.
sample - gives the number of random neurons you would like plotted (default is 1)
"""
# Assert that we are getting valid input
assert(percent_excited >= 0 and percent_excited <= 1.0)
assert(duration > 0)
assert(num > 0)
assert(sample > 0)
assert(num >= sample)
# Constants used in the modeling equation
area = 20000*umetre**2
Cm = (1*ufarad*cm**-2)*area
gl = (5e-5*siemens*cm**-2)*area
El = -60*mV
EK = -90*mV
ENa = 50*mV
g_na = (100*msiemens*cm**-2)*area
g_kd = (30*msiemens*cm**-2)*area
VT = -63*mV
# Time constants
taue = 5*ms # excitatory
taui = 10*ms # inhibitory
# Reversal potentials
Ee = 0*mV # excitatory
Ei = -80*mV # inhibitory
# Synaptic weights
we = 6*nS # excitatory
wi = 67*nS # inhibitory
# The model equations
eqs = Equations(
'''
dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-g_na*(m*m*m)*h*(v-ENa)-g_kd*(n*n*n*n)*(v-EK))/Cm : volt
dm/dt = alpham*(1-m)-betam*m : 1
dn/dt = alphan*(1-n)-betan*n : 1
dh/dt = alphah*(1-h)-betah*h : 1
dge/dt = -ge*(1./taue) : siemens
dgi/dt = -gi*(1./taui) : siemens
alpham = 0.32*(mV**-1)*(13*mV-v+VT)/(exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz
betam = 0.28*(mV**-1)*(v-VT-40*mV)/(exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz
alphah = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz
betah = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz
alphan = 0.032*(mV**-1)*(15*mV-v+VT)/(exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz
betan = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz
'''
)
# Build the neuron group
neurons = NeuronGroup(
num,
model=eqs,
threshold=EmpiricalThreshold(threshold=-20*mV,refractory=3*ms),
implicit=True,
freeze=True
)
num_excited = int(num * percent_excited)
num_inhibited = num - num_excited
excited = neurons.subgroup(num_excited)
inhibited = neurons.subgroup(num_inhibited)
excited_conn = Connection(excited, neurons, 'ge', weight=we, sparseness=0.02)
inhibited_conn = Connection(inhibited, neurons, 'gi', weight=wi, sparseness=0.02)
# Initialization
neurons.v = El+(randn(num)*5-5)*mV
neurons.ge = (randn(num)*1.5+4)*10.*nS
neurons.gi = (randn(num)*12+20)*10.*nS
# Record a few trace and run
recorded = choice(num, sample)
trace = StateMonitor(neurons, 'v', record=recorded)
run(duration * msecond)
for i in recorded:
plot(trace.times/ms, trace[i]/mV)
show()
from matplotlib.patches import Rectangle import spikerlib as sl import numpy as np import sys sim = Network() duration = 200*ms dt = 0.1*ms tau = 10*ms Vth = 15*mV Vreset = 0*mV Vreset = 13.65*mV lifeq = "dV/dt = -V/tau : volt" lifnrn = NeuronGroup(1, lifeq, threshold="V>=Vth", reset=Vreset) lifnrn.V = Vreset sim.add(lifnrn) Nin = 200 fin = 80*Hz Sin = 0.6 sigma = 0.0*ms weight = 0.1*mV inputs = sl.tools.fast_synchronous_input_gen(Nin, fin, Sin, sigma, duration) connection = Connection(inputs, lifnrn, "V", weight=weight) sim.add(inputs, connection) vmon = StateMonitor(lifnrn, "V", record=True) spikemon = SpikeMonitor(lifnrn) sim.add(vmon, spikemon)
def runsim(neuron_model,
# sim params
dt, simtime, prerun, monitors, recvars,
# stimulation params
fstim, r0_bg, r0_stim, stim_starts, stim_stops, stim_odors, stim_amps, stim_start_var,
# network params
beeid, N_glu, N_KC, ORNperGlu, PNperKC, PN_I0, LN_I0,
# network weights
wi, wORNLN, wORNPN, wPNKC,
# default params
V0min, inh_struct=None, Winh=None, timestep=500, report=None):
np.random.seed() #needed for numpy/brian when runing parallel sims
define_default_clock(dt=dt)
inh_on_off = 0 if (wi == 0) or (wi is None) or (wORNLN is None) else 1
######################### NEURONGROUPS #########################
NG = dict()
# ORN Input
# For each glumerolus, random temporal response jitter can be added.
# The jitter is added to the response onset. Maximum jitter is given by stim_start_var.
# stim_start_jittered is a vector containing the jittered stim start tims
# orn_activation returns a booolean vector of stim presence given time t
# Total ORN rate: Baseline componenent equal for all units,
# and individual activationa.
jitter = np.random.uniform(0,stim_start_var,N_glu)
stim_tun = lambda odorN: fstim(N_glu=N_glu, odorN=odorN) * r0_stim
orn_activation = lambda t: np.sum([
a*stim_tun(odorN=o)*np.logical_and(np.greater(t,prerun+stim_start+jitter), np.less(t,prerun+stim_stop))
for stim_start,stim_stop,o,a in zip(stim_starts, stim_stops, stim_odors, stim_amps)], 0)
orn_rates = lambda t: np.repeat(r0_bg + orn_activation(t),repeats = ORNperGlu)
NG['ORN'] = PoissonGroup(ORNperGlu*N_glu, rates=orn_rates)
NG['PN'] = NeuronGroup(N_glu, **neuron_model)
NG['LN'] = NeuronGroup(N_glu*inh_on_off, **neuron_model)
if 'KC' in monitors: NG['KC'] = NeuronGroup(N_KC, **neuron_model)
######################### CONNECTIONS #########################
c = dict()
c['ORNPN'] = Connection(NG['ORN'],NG['PN'],'ge')
for i in np.arange(N_glu): c['ORNPN'].connect_full(NG['ORN'].subgroup(ORNperGlu),NG['PN'][i],weight=wORNPN)
if inh_on_off:
print('-- inhibiting --',wi)
c['ORNLN'] = Connection(NG['ORN'],NG['LN'],'ge')
c['LNPN'] = Connection(NG['LN'],NG['PN'],'gi',weight=(wi*35)/N_glu)
for i in np.arange(N_glu):
c['ORNLN'].connect_full(NG['ORN'][ i*ORNperGlu : (i+1)*ORNperGlu ],
NG['LN'][i],
weight = wORNLN)
if inh_struct: c['LNPN'].connect(NG['LN'],NG['PN'],Winh)
if 'KC' in monitors:
c['KC'] = Connection(NG['PN'],NG['KC'],'ge')
c['KC'].connect_random(NG['PN'],NG['KC'],p=PNperKC/float(N_glu),weight=wPNKC,seed=beeid)
######################### INITIAL VALUES #########################
VT = neuron_model['threshold']
NG['PN'].vm = np.random.uniform(V0min,VT,size=len(NG['PN']))
if inh_on_off:
NG['LN'].vm= np.random.uniform(V0min,VT,size=len(NG['LN']))
if 'KC' in monitors:
NG['KC'].vm= np.random.uniform(V0min,VT,size=len(NG['KC']))
net = Network(NG.values(), c.values())
#### Compensation currents ###
NG['PN'].I0 = PN_I0
NG['LN'].I0 = LN_I0
##########################################################################
######################### PRE-RUN #########################
net.run(prerun)
######################### MONITORS #########################
spmons = [SpikeMonitor(NG[mon], record=True) for mon in monitors]
net.add(spmons)
if len(recvars) > 0:
mons = [MultiStateMonitor(NG[mon], vars=recvars, record=True, timestep=timestep) for mon in monitors]
net.add(mons)
else:
mons = None
######################### RUN #########################
net = run(simtime, report=report)
out_spikes = dict( (monitors[i],np.array(sm.spikes)) for i,sm in enumerate(spmons) )
if mons is not None:
out_mons = dict( (mon,dict((var,statemon.values) for var,statemon in m.iteritems())) for mon,m in zip(monitors,mons))
else:
out_mons = None
#subtract the prerun from spike times, if there are any
for spikes in out_spikes.itervalues():
if len(spikes) != 0:
spikes[:,1] -= prerun
return out_spikes, out_mons
from brian import (NeuronGroup, Network, StateMonitor,
second, ms, volt, mV)
import numpy as np
import matplotlib.pyplot as plt
network = Network()
XT = -50*mV
DeltaT = 0.05*mV/ms
eqs = "dX/dt = DeltaT*exp((X-XT)/DeltaT) : volt"
neuron = NeuronGroup(1, eqs, threshold="X>=XT", reset=-65*mV)
neuron.X = -65*mV
network.add(neuron)
vmon = StateMonitor(neuron, "X", record=True)
network.add(vmon)
network.run(1*second)
plt.figure("Voltage")
plt.plot(vmon.times, vmon[0])
plt.show()
betah=1./(exp(-0.1/mV*(V+28*mV))+1)/ms : Hz
dn/dt=5*(alphan*(1-n)-betan*n) : 1
alphan=-0.01/mV*(V+34*mV)/(exp(-0.1/mV*(V+34*mV))-1)/ms : Hz
betan=0.125*exp(-(V+44*mV)/(80*mV))/ms : Hz
dgExc/dt = -gExc*(1./taue) : siemens
dgInh/dt = -gInh*(1./taui) : siemens
Iapp : amp
'''
neuron = NeuronGroup(1, eqs, threshold=threshold, method='RK')
neuron.V = -70*mV
# delays
B1, A1, A2, A3 = 5*ms, 20*ms, 30*ms, 40*ms
target_delay = A2-B1 # delay to be learned by neuron
spikes_A = [(0, 10*ms), (0, 115*ms), (0, 300*ms), (0, 450*ms)]
spikes_B = [(1, 10*ms), (1, 130*ms), (1, 335*ms), (1, 475*ms)]
inputs = SpikeGeneratorGroup(2, spikes_A+spikes_B)
synapse_A = Synapses(inputs[0], neuron,
model="w : siemens", pre="gExc_post += w")
synapse_A[:,:] = 3
synapse_A.w = WExc
synapse_A.delay[0] = A1
from brian import log_level_debug
log_level_debug()
set_global_preferences(useweave=True,usecodegen=True,usecodegenweave=True,usenewpropagate=True,usecstdp=True)
from matplotlib.pyplot import plot, show, subplot
params = {}
params["t_Nr"] = 2*ms
params["t_Nf"] = 80*ms
params["t_AMPA"] = 5*ms
simclock = Clock(dt=0.01*ms)
input=NeuronGroup(2,model='dv/dt=1/(10*ms):1', threshold=1, reset=0,clock=simclock)
neurons = NeuronGroup(1, model="""dv/dt=(NMDAo+AMPAo-v)/(10*ms) : 1
NMDAo : 1
AMPAo : 1""", freeze = True,clock=simclock)
ampadyn = '''
dAMPAoS/dt = -AMPAoS/t_AMPA : 1
AMPAi = AMPAoS
AMPAo = AMPAoS / (t_AMPA /msecond) : 1
'''
nmdadyn = '''
dNMDAoS/dt = (1/t_Nr)*(Nnor*NMDAi-NMDAoS) : 1
dNMDAi/dt = -(1/t_Nf)*NMDAi : 1
Nnor = (t_Nf/t_Nr)**((t_Nr)/(t_Nf - t_Nr)) : 1
Nscal = (t_Nf/msecond)**(t_Nf/(t_Nf - t_Nr))/(t_Nr/msecond)**(t_Nr/(t_Nf - t_Nr)) : 1
def run_simulation(realizations=1, trials=1, t=3000 * ms, alpha=1, ree=1,
k=50, winlen = 50 * ms, verbose=True, t_stim = 0):
"""
Run the whole simulation with the specified parameters. All model parameter are set in the function.
Keyword arguments:
:param realizations: number of repititions of the whole simulation, number of network instances
:param trials: number of trials for network instance
:param t: simulation time
:param alpha: scaling factor for number of neurons in the network
:param ree: clustering coefficient
:param k: number of clusters
:param t_stim : duration of stimulation of a subset of clusters
:param winlen: length of window in ms
:param verbose: plotting flag
:return: numpy matrices with spike times
"""
# The equations defining our neuron model
eqs_string = '''
dV/dt = (mu - V)/tau + x: volt
dx/dt = -1.0/tau_2*(x - y/tau_1) : volt/second
dy/dt = -y/tau_1 : volt
mu : volt
tau: second
tau_2: second
tau_1: second
'''
# Model parameters
n_e = int(4000 * alpha) # number of exc neurons
n_i = int(1000 * alpha) # number of inh neurons
tau_e = 15 * ms # membrane time constant (for excitatory synapses)
tau_i = 10 * ms # membrane time constant (for inhibitory synapses)
tau_syn_2_e = 3 * ms # exc synaptic time constant tau2 in paper
tau_syn_2_i = 2 * ms # inh synaptic time constant tau2 in paper
tau_syn_1 = 1 * ms # exc/inh synaptic time constant tau1 in paper
vt = -50 * mV # firing threshold
vr = -65 * mV # reset potential
dv = vt - vr # delta v
refrac = 5 * ms # absolute refractory period
# scale the weights to ensure same variance in the inputs
wee = 0.024 * dv * np.sqrt(1. / alpha)
wie = 0.014 * dv * np.sqrt(1. / alpha)
wii = -0.057 * dv * np.sqrt(1. / alpha)
wei = -0.045 * dv * np.sqrt(1. / alpha)
# Connection probability
p_ee = 0.2
p_ii = 0.5
p_ie = 0.5
p_ei = 0.5
# determine probs for inside and outside of clusters
p_in, p_out = get_cluster_connection_probs(ree, k, p_ee)
mu_min_e, mu_max_e = 1.1, 1.2
mu_min_i, mu_max_i = 1.0, 1.05
# increase cluster weights if there are clusters
wee_cluster = wee if p_in == p_out else 1.9 * wee
# define numpy array for data storing
all_data = np.zeros((realizations, trials, n_e+n_i, int(t/winlen)//2))
for realization in range(realizations):
# clear workspace to make sure that is a new realization of the network
clear(True, True)
reinit()
# set up new random bias parameter for every type of neuron
mu_e = vr + np.random.uniform(mu_min_e, mu_max_e, n_e) * dv # bias for excitatory neurons
mu_i = vr + np.random.uniform(mu_min_i, mu_max_i, n_i) * dv # bias for excitatory neurons
# Let's create an equation object from our string and parameters
model_eqs = Equations(eqs_string)
# Let's create 5000 neurons
all_neurons = NeuronGroup(N=n_e + n_i,
model=model_eqs,
threshold=vt,
reset=vr,
refractory=refrac,
freeze=True,
method='Euler',
compile=True)
# Divide the neurons into excitatory and inhibitory ones
neurons_e = all_neurons[0:n_e]
neurons_i = all_neurons[n_e:n_e + n_i]
# set the bias
neurons_e.mu = mu_e
neurons_i.mu = mu_i
neurons_e.tau = tau_e
neurons_i.tau = tau_i
neurons_e.tau_2 = tau_syn_2_e
neurons_i.tau_2 = tau_syn_2_i
all_neurons.tau_1 = tau_syn_1
# set up connections
connections = Connection(all_neurons, all_neurons, 'y')
# do the cluster connection like cross validation: cluster neuron := test idx; other neurons := train idx
kf = KFold(n=n_e, n_folds=k)
for idx_out, idx_in in kf: # idx_out holds all other neurons; idx_in holds all cluster neurons
# connect current cluster to itself
connections.connect_random(all_neurons[idx_in[0]:idx_in[-1]], all_neurons[idx_in[0]:idx_in[-1]],
sparseness=p_in, weight=wee_cluster)
# connect current cluster to other neurons
connections.connect_random(all_neurons[idx_in[0]:idx_in[-1]], all_neurons[idx_out[0]:idx_out[-1]],
sparseness=p_out, weight=wee)
# connect all excitatory to all inhibitory, irrespective of clustering
connections.connect_random(all_neurons[0:n_e], all_neurons[n_e:(n_e + n_i)], sparseness=p_ie, weight=wie)
# connect all inhibitory to all excitatory
connections.connect_random(all_neurons[n_e:(n_e + n_i)], all_neurons[0:n_e], sparseness=p_ei, weight=wei)
# connect all inhibitory to all inhibitory
connections.connect_random(all_neurons[n_e:(n_e + n_i)], all_neurons[n_e:(n_e + n_i)], sparseness=p_ii,
weight=wii)
# set up spike monitors
spike_mon_e = SpikeMonitor(neurons_e)
spike_mon_i = SpikeMonitor(neurons_i)
# set up network with monitors
network = Network(all_neurons, connections, spike_mon_e, spike_mon_i)
# run this network for some number of trials, every time with
for trial in range(trials):
# different initial values
all_neurons.V = vr + (vt - vr) * np.random.rand(len(all_neurons)) * 1.4
# Calibration phase
# run for the first half of the time to let the neurons adapt
network.run(t/2)
# reset monitors to start recording phase
spike_mon_i.reinit()
spike_mon_e.reinit()
# stimulation if duration is given
# define index variable for the stimulation possibility (is 0 for stimulation time=0)
t_stim_idx = int(t_stim / (winlen/ms))
if not(t_stim==0):
# Stimulation phase, increase input to subset of clusters
all_neurons[:400].mu += 0.07 * dv
network.run(t_stim * ms, report='text')
# set back to normal
all_neurons[:400].mu -= 0.07 * dv
# save data
all_data[realization, trial, :n_e, :t_stim_idx] = spikes_counter(spike_mon_e, winlen)
all_data[realization, trial, n_e:, :t_stim_idx] = spikes_counter(spike_mon_i, winlen)
# reset monitors
spike_mon_e.reinit()
spike_mon_i.reinit()
# run the remaining time of the simulation
network.run((t/2) - t_stim*ms, report='text')
# save results
all_data[realization, trial, :n_e, t_stim_idx:] = spikes_counter(spike_mon_e, winlen)
all_data[realization, trial, n_e:, t_stim_idx:] = spikes_counter(spike_mon_i, winlen)
if verbose:
plt.ion()
plt.figure()
raster_plot(spike_mon_e)
plt.title('Excitatory neurons')
spike_mon_e.reinit()
spike_mon_i.reinit()
return all_data
def reinit(self):
NeuronGroup.reinit(self)
self.filterbank.buffer_init()
self.buffer_pointer = self.buffersize
self.buffer_start = -self.buffersize
def runsim(fin):
clear(True)
gc.collect()
defaultclock.reinit()
weight = 0.16*mV
sim = Network()
duration = 2.0*second
Vth = 15*mV
Vreset = 13.65*mV
trefr = 2*ms
lifeq = """
dV/dt = -V/(10*ms) : volt
Vth : volt
"""
nrndef = {"model": lifeq, "threshold": "V>=Vth", "reset": "V=Vreset",
"refractory": 0.1*ms}
inputgroups = []
connections = []
neurons = []
Nneurons = len(fin)
neurons = NeuronGroup(Nneurons, **nrndef)
neurons.V = 0*mV
neurons.Vth = 15*mV
for idx in range(Nneurons):
fin_i = fin[idx]*Hz
inputgrp = PoissonGroup(50, fin_i)
conn = Connection(inputgrp, neurons[idx], state="V", weight=weight)
inputgroups.append(inputgrp)
connections.append(conn)
voltagemon = StateMonitor(neurons, "V", record=True)
spikemon = SpikeMonitor(neurons, record=True)
sim.add(neurons, voltagemon, spikemon)
sim.add(*inputgroups)
sim.add(*connections)
@network_operation
def refractory_threshold(clock):
for idx in range(Nneurons):
if (len(spikemon.spiketimes[idx])
and clock.t < spikemon.spiketimes[idx][-1]*second+trefr):
neurons.Vth[idx] = 100*mV
else:
neurons.Vth[idx] = Vth
sim.add(refractory_threshold)
print("Running simulation of {} neurons for {} s".format(Nneurons, duration))
sim.run(duration, report="stdout")
mnpss = []
allnpss = []
outisi = []
for idx in range(Nneurons):
vmon = voltagemon[idx]
smon = spikemon[idx]
if not len(smon):
continue
outisi.append(duration*1000/len(smon))
if len(smon) > 0:
npss = sl.tools.npss(vmon, smon, 0*mV, 15*mV, 10*ms, 2*ms)
else:
npss = 0
mnpss.append(np.mean(npss))
allnpss.append(npss)
return outisi, mnpss
def run_simulation(realizations=1, trials=1, t=3000 * ms, alpha=1, ree=1, k=50, winlen=50 * ms, verbose=True, t_stim=0):
"""
Run the whole simulation with the specified parameters. All model parameter are set in the function.
Keyword arguments:
:param realizations: number of repititions of the whole simulation, number of network instances
:param trials: number of trials for network instance
:param t: simulation time
:param alpha: scaling factor for number of neurons in the network
:param ree: clustering coefficient
:param k: number of clusters
:param t_stim : duration of stimulation of a subset of clusters
:param winlen: length of window in ms
:param verbose: plotting flag
:return: numpy matrices with spike times
"""
# The equations defining our neuron model
eqs_string = """
dV/dt = (mu - V)/tau + x: volt
dx/dt = -1.0/tau_2*(x - y/tau_1) : volt/second
dy/dt = -y/tau_1 : volt
mu : volt
tau: second
tau_2: second
tau_1: second
"""
# Model parameters
n_e = int(4000 * alpha) # number of exc neurons
n_i = int(1000 * alpha) # number of inh neurons
tau_e = 15 * ms # membrane time constant (for excitatory synapses)
tau_i = 10 * ms # membrane time constant (for inhibitory synapses)
tau_syn_2_e = 3 * ms # exc synaptic time constant tau2 in paper
tau_syn_2_i = 2 * ms # inh synaptic time constant tau2 in paper
tau_syn_1 = 1 * ms # exc/inh synaptic time constant tau1 in paper
vt = -50 * mV # firing threshold
vr = -65 * mV # reset potential
dv = vt - vr # delta v
refrac = 5 * ms # absolute refractory period
# scale the weights to ensure same variance in the inputs
wee = 0.024 * dv * np.sqrt(1.0 / alpha)
wie = 0.014 * dv * np.sqrt(1.0 / alpha)
wii = -0.057 * dv * np.sqrt(1.0 / alpha)
wei = -0.045 * dv * np.sqrt(1.0 / alpha)
# Connection probability
p_ee = 0.2
p_ii = 0.5
p_ie = 0.5
p_ei = 0.5
# determine probs for inside and outside of clusters
p_in, p_out = get_cluster_connection_probs(ree, k, p_ee)
mu_min_e, mu_max_e = 1.1, 1.2
mu_min_i, mu_max_i = 1.0, 1.05
# increase cluster weights if there are clusters
wee_cluster = wee if p_in == p_out else 1.9 * wee
# define numpy array for data storing
all_data = np.zeros((realizations, trials, n_e + n_i, int(t / winlen) // 2))
for realization in range(realizations):
# clear workspace to make sure that is a new realization of the network
clear(True, True)
reinit()
# set up new random bias parameter for every type of neuron
mu_e = vr + np.random.uniform(mu_min_e, mu_max_e, n_e) * dv # bias for excitatory neurons
mu_i = vr + np.random.uniform(mu_min_i, mu_max_i, n_i) * dv # bias for excitatory neurons
# Let's create an equation object from our string and parameters
model_eqs = Equations(eqs_string)
# Let's create 5000 neurons
all_neurons = NeuronGroup(
N=n_e + n_i,
model=model_eqs,
threshold=vt,
reset=vr,
refractory=refrac,
freeze=True,
method="Euler",
compile=True,
)
# Divide the neurons into excitatory and inhibitory ones
neurons_e = all_neurons[0:n_e]
neurons_i = all_neurons[n_e : n_e + n_i]
# set the bias
neurons_e.mu = mu_e
neurons_i.mu = mu_i
neurons_e.tau = tau_e
neurons_i.tau = tau_i
neurons_e.tau_2 = tau_syn_2_e
neurons_i.tau_2 = tau_syn_2_i
all_neurons.tau_1 = tau_syn_1
# set up connections
connections = Connection(all_neurons, all_neurons, "y")
# do the cluster connection like cross validation: cluster neuron := test idx; other neurons := train idx
kf = KFold(n=n_e, n_folds=k)
for idx_out, idx_in in kf: # idx_out holds all other neurons; idx_in holds all cluster neurons
# connect current cluster to itself
connections.connect_random(
all_neurons[idx_in[0] : idx_in[-1]],
all_neurons[idx_in[0] : idx_in[-1]],
sparseness=p_in,
weight=wee_cluster,
)
# connect current cluster to other neurons
connections.connect_random(
all_neurons[idx_in[0] : idx_in[-1]], all_neurons[idx_out[0] : idx_out[-1]], sparseness=p_out, weight=wee
)
# connect all excitatory to all inhibitory, irrespective of clustering
connections.connect_random(all_neurons[0:n_e], all_neurons[n_e : (n_e + n_i)], sparseness=p_ie, weight=wie)
# connect all inhibitory to all excitatory
connections.connect_random(all_neurons[n_e : (n_e + n_i)], all_neurons[0:n_e], sparseness=p_ei, weight=wei)
# connect all inhibitory to all inhibitory
connections.connect_random(
all_neurons[n_e : (n_e + n_i)], all_neurons[n_e : (n_e + n_i)], sparseness=p_ii, weight=wii
)
# set up spike monitors
spike_mon_e = SpikeMonitor(neurons_e)
spike_mon_i = SpikeMonitor(neurons_i)
# set up network with monitors
network = Network(all_neurons, connections, spike_mon_e, spike_mon_i)
# run this network for some number of trials, every time with
for trial in range(trials):
# different initial values
all_neurons.V = vr + (vt - vr) * np.random.rand(len(all_neurons)) * 1.4
# Calibration phase
# run for the first half of the time to let the neurons adapt
network.run(t / 2)
# reset monitors to start recording phase
spike_mon_i.reinit()
spike_mon_e.reinit()
# stimulation if duration is given
# define index variable for the stimulation possibility (is 0 for stimulation time=0)
t_stim_idx = int(t_stim / (winlen / ms))
if not (t_stim == 0):
# Stimulation phase, increase input to subset of clusters
all_neurons[:400].mu += 0.07 * dv
network.run(t_stim * ms, report="text")
# set back to normal
all_neurons[:400].mu -= 0.07 * dv
# save data
all_data[realization, trial, :n_e, :t_stim_idx] = spikes_counter(spike_mon_e, winlen)
all_data[realization, trial, n_e:, :t_stim_idx] = spikes_counter(spike_mon_i, winlen)
# reset monitors
spike_mon_e.reinit()
spike_mon_i.reinit()
# run the remaining time of the simulation
network.run((t / 2) - t_stim * ms, report="text")
# save results
all_data[realization, trial, :n_e, t_stim_idx:] = spikes_counter(spike_mon_e, winlen)
all_data[realization, trial, n_e:, t_stim_idx:] = spikes_counter(spike_mon_i, winlen)
if verbose:
plt.ion()
plt.figure()
raster_plot(spike_mon_e)
plt.title("Excitatory neurons")
spike_mon_e.reinit()
spike_mon_i.reinit()
return all_data
class ModelFitting(Fitness):
def initialize(self, **kwds):
# Initialization of variables
self.use_gpu = self.unit_type=='GPU'
# Gets the key,value pairs in shared_data
for key, val in self.shared_data.iteritems():
setattr(self, key, val)
# Gets the key,value pairs in **kwds
for key, val in kwds.iteritems():
setattr(self, key, val)
self.neurons = self.nodesize
self.groups = self.groups
self.model = cPickle.loads(self.model)
if type(self.model) is str:
self.model = Equations(self.model)
self.initialize_neurongroup()
self.transform_data()
self.inject_input()
# if self.use_gpu:
# ########
# # TODO
# ########
# # Select integration scheme according to method
# if self.method == 'Euler': scheme = euler_scheme
# elif self.method == 'RK': scheme = rk2_scheme
# elif self.method == 'exponential_Euler': scheme = exp_euler_scheme
# else: raise Exception("The numerical integration method is not valid")
#
# self.mf = GPUModelFitting(self.group, self.model, self.input, self.I_offset,
# self.spiketimes, self.spiketimes_offset, zeros(self.neurons), 0*ms, self.delta,
# precision=self.precision, scheme=scheme)
# else:
# self.cc = CoincidenceCounter(self.group, self.spiketimes, self.spiketimes_offset,
# onset=self.onset, delta=self.delta)
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = 'refractory'
self.model.add_param('refractory', second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0*ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
self.group = NeuronGroup(self.neurons,
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory = self.max_refractory,
method = self.method,
clock=Clock(dt=self.dt))
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def transform_data(self):
self.transformer = DataTransformer(self.neurons,
self.inputs,
spikes = self.spikes,
traces = self.traces,
dt = self.dt,
slices = self.slices,
overlap = self.overlap,
groups = self.groups)
self.total_steps = self.transformer.total_steps
self.sliced_duration = self.transformer.sliced_duration
self.sliced_inputs = self.transformer.slice_traces(self.inputs)
self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(self.sliced_inputs)
if self.traces is not None:
self.sliced_traces = self.transformer.slice_traces(self.traces)
self.traces_inline, self.traces_offset = self.transformer.transform_traces(self.sliced_traces)
else:
self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None
if self.spikes is not None:
self.sliced_spikes = self.transformer.slice_spikes(self.spikes)
self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(self.sliced_spikes)
else:
self.sliced_spikes, self.spikes_inline, self.spikes_offset = None, None, None
def inject_input(self):
# Injects current in consecutive subgroups, where I_offset have the same value
# on successive intervals
I_offset = self.inputs_offset
k = -1
for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)):
I_offset_subgroup_value = I_offset[i]
I_offset_subgroup_length = i - k
sliced_subgroup = self.group.subgroup(I_offset_subgroup_length)
input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps]
sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock))
k = i
def initialize_criterion(self, delays,tau_metric = None):
# general criterion parameters
params = dict(group=self.group, traces=self.sliced_traces, spikes=self.sliced_spikes,
targets_count=self.groups*self.slices, duration=self.sliced_duration, onset=self.onset,
spikes_inline=self.spikes_inline, spikes_offset=self.spikes_offset,
traces_inline=self.traces_inline, traces_offset=self.traces_offset,
delays=delays, when='start')
criterion_name = self.criterion.__class__.__name__
# criterion-specific parameters
if criterion_name == 'GammaFactor':
params['delta'] = self.criterion.delta
params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm
self.criterion_object = GammaFactorCriterion(**params)
if criterion_name == 'LpError':
params['p'] = self.criterion.p
params['varname'] = self.criterion.varname
self.criterion_object = LpErrorCriterion(**params)
if criterion_name == 'VanRossum':
params['tau'] = self.criterion.tau
self.criterion_object = VanRossumCriterion(**params)
if criterion_name == 'Brette':
params['tau_metric'] = tau_metric
self.criterion_object = BretteCriterion(**params)
def update_neurongroup(self, **param_values):
"""
Inject fitting parameters into the NeuronGroup
"""
# Sets the parameter values in the NeuronGroup object
self.group.reinit()
for param, value in param_values.iteritems():
self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing
# Reinitializes the model variables
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def combine_sliced_values(self, values):
if type(values) is tuple:
combined_values = tuple([sum(reshape(v, (self.slices, -1)), axis=0) for v in values])
else:
combined_values = sum(reshape(values, (self.slices, -1)), axis=0)
return combined_values
def evaluate(self, **param_values):
"""
Use fitparams['delays'] to take delays into account
Use fitparams['refractory'] to take refractory into account
"""
delays = param_values.pop('delays', zeros(self.neurons))
refractory = param_values.pop('refractory', zeros(self.neurons))
tau_metric = param_values.pop('tau_metric', zeros(self.neurons))
# repeat spike delays and refractory to take slices into account
delays = kron(delays, ones(self.slices))
refractory = kron(refractory, ones(self.slices))
tau_metric = kron(tau_metric, ones(self.slices))
self.update_neurongroup(**param_values)
if self.criterion.__class__.__name__ == 'Brette':
self.initialize_criterion(delays,tau_metric)
else:
self.initialize_criterion(delays)
if self.use_gpu:
pass
#########
# TODO
#########
# # Reinitializes the simulation object
# self.mf.reinit_vars(self.input, self.I_offset, self.spiketimes, self.spiketimes_offset, delays, refractory)
# # LAUNCHES the simulation on the GPU
# self.mf.launch(self.duration, self.stepsize)
# coincidence_count = self.mf.coincidence_count
# spike_count = self.mf.spike_count
else:
# set the refractory period
if self.max_refractory is not None:
self.group.refractory = refractory
# Launch the simulation on the CPU
self.group.clock.reinit()
net = Network(self.group, self.criterion_object)
net.run(self.duration)
sliced_values = self.criterion_object.get_values()
combined_values = self.combine_sliced_values(sliced_values)
values = self.criterion_object.normalize(combined_values)
return values
def __init__(self, lip_size, params, background_inputs=None, visual_cortex_input=None,
go_input=None):
self.lip_size=lip_size
self.N=2*self.lip_size
self.params=params
self.background_inputs=background_inputs
self.visual_cortex_input=visual_cortex_input
self.go_input=go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b, params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations('g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens')
eqs += Equations('g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens')
# Total synaptic current
eqs += Equations('I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp')
NeuronGroup.__init__(self, self.N, model=eqs, threshold=-20*mV, reset=params.EL, compile=True)
self.init_subpopulations()
self.connections=[]
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa=init_connection(self.background_inputs[0], self.left_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
background_right_ampa=init_connection(self.background_inputs[1], self.right_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa=init_connection(self.visual_cortex_input[0], self.left_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
vc_right_lip_ampa=init_connection(self.visual_cortex_input[1], self.right_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa=init_connection(self.go_input, self.left_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_right_lip_i_ampa=init_connection(self.go_input, self.right_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_left_lip_e_ampa=init_connection(self.go_input, self.left_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
go_right_lip_e_ampa=init_connection(self.go_input, self.right_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
Vrest = 0*mV
Vth = 20*mV
tau = 20*ms
Nnrns = 4
Ningroups = 1
Nin_per_group = 50
fin = 20*Hz
ingroup_sync = [0.5]
sigma = 0*ms
weight = 2.0*mV
Nallin = Nin_per_group*Ningroups
Nin = 25 # total number of connections each cell receives
lifeq_exc = Equations("dV/dt = (Vrest-V)/tau : volt")
lifeq_exc.prepare()
nrngroup = NeuronGroup(Nnrns, lifeq_exc, threshold="V>Vth", reset=Vrest,
refractory=2*ms)
nrngroup.V = Vrest
network.add(nrngroup)
print("Setting up inputs and connections ...")
ingroups = []
inpconns = []
for ing in range(Ningroups):
ingroup = sl.tools.fast_synchronous_input_gen(Nin_per_group, fin,
ingroup_sync[ing], sigma, duration,
shuffle=False)
inpconn = Connection(ingroup, nrngroup, 'V')
ingroups.append(ingroup)
inpconns.append(inpconn)
inputneurons = []
# CONNECTIONS
class Simulator(object):
def __init__(self, model, reset, threshold,
inputs, input_var = 'I', dt = defaultclock.dt,
refractory = 0*ms, max_refractory = None,
spikes = None, traces = None,
groups = 1,
slices = 1, overlap = 0*second,
onset = 0*second,
neurons = 1000, # = nodesize = number of neurons on this node = (total number of neurons on this node)/(number of slices)
initial_values = None,
unit_type = 'CPU',
stepsize = 128*ms,
precision = 'double',
criterion = None,
statemonitor_var=None,
spikemonitor = False,
nbr_spikes = 200,
ntrials=1,
method = 'Euler',
# stand_alone=False,
# neuron_group=None,
# given_neuron_group=False
):
# print refractory, max_refractory
# self.neuron_group = neuron_group
# self.given_neuron_group = False
# self.stand_alone = given_neuron_group
self.model = model
self.reset = reset
self.threshold = threshold
self.inputs = inputs
self.input_var = input_var
self.dt = dt
self.refractory = refractory
self.max_refractory = max_refractory
self.spikes = spikes
self.traces = traces
self.initial_values = initial_values
self.groups = groups
self.slices = slices
self.overlap = overlap
self.ntrials=ntrials
self.onset = onset
self.neurons = neurons
self.unit_type = unit_type
if type(statemonitor_var) is not list and statemonitor_var is not None:
statemonitor_var = [statemonitor_var]
self.statemonitor_var = statemonitor_var
self.spikemonitor=spikemonitor
self.nbr_spikes = nbr_spikes
self.stepsize = stepsize
self.precision = precision
self.criterion = criterion
self.method = method
self.use_gpu = self.unit_type=='GPU'
if self.statemonitor_var is not None:
self.statemonitor_values = [zeros(self.neurons)]*len(statemonitor_var)
self.initialize_neurongroup()
self.transform_data()
self.inject_input()
if self.criterion.__class__.__name__ == 'Brette':
self.initialize_criterion(delays=zeros(self.neurons),tau_metric=zeros(self.neurons))
else:
self.initialize_criterion(delays=zeros(self.neurons))
if self.use_gpu:
self.initialize_gpu()
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = 'refractory'
self.model.add_param('refractory', second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0*ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
# print refractory, self.max_refractory
if type(refractory) is double:
refractory=refractory*second
# if self.give_neuron_group == False:
self.group = NeuronGroup(self.neurons, # TODO: * slices?
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory = self.max_refractory,
method = self.method,
clock=Clock(dt=self.dt))
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
# else:
# self.group = self.neuron_group
def initialize_gpu(self):
# Select integration scheme according to method
if self.method == 'Euler': scheme = euler_scheme
elif self.method == 'RK': scheme = rk2_scheme
elif self.method == 'exponential_Euler': scheme = exp_euler_scheme
else: raise Exception("The numerical integration method is not valid")
self.mf = GPUModelFitting(self.group, self.model, self.criterion_object,
self.input_var, self.neurons/self.groups,
self.onset,
statemonitor_var = self.statemonitor_var,
spikemonitor = self.spikemonitor,
nbr_spikes = self.nbr_spikes,
duration = self.sliced_duration,
precision=self.precision, scheme=scheme)
def transform_data(self):
self.transformer = DataTransformer(self.neurons,
self.inputs,
spikes = self.spikes,
traces = self.traces,
dt = self.dt,
slices = self.slices,
overlap = self.overlap,
groups = self.groups,ntrials=self.ntrials)
self.total_steps = self.transformer.total_steps
self.sliced_duration = self.transformer.sliced_duration
if self.ntrials>1:
self.inputs_inline = self.inputs.flatten()
self.sliced_inputs = self.inputs
self.inputs_offset = zeros(self.neurons)
else:
self.sliced_inputs = self.transformer.slice_traces(self.inputs)
self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(self.sliced_inputs)
if self.traces is not None:
self.sliced_traces = self.transformer.slice_traces(self.traces)
self.traces_inline, self.traces_offset = self.transformer.transform_traces(self.sliced_traces)
else:
self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None
if self.spikes is not None:
if self.ntrials>1:
self.sliced_spikes = self.transformer.slice_spikes(self.spikes)
self.spikes_inline, self.trials_offset = self.transformer.transform_trials(self.spikes)
self.spikes_offset = zeros((self.neurons),dtype=int)
else:
self.sliced_spikes = self.transformer.slice_spikes(self.spikes)
self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(self.sliced_spikes)
self.trials_offset=[0]
else:
self.sliced_spikes, self.spikes_inline, self.spikes_offset,self.trials_offset = None, None, None, None
def inject_input(self):
# Injects current in consecutive subgroups, where I_offset have the same value
# on successive intervals
I_offset = self.inputs_offset
k = -1
for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)):
I_offset_subgroup_value = I_offset[i]
I_offset_subgroup_length = i - k
sliced_subgroup = self.group.subgroup(I_offset_subgroup_length)
input_sliced_values = self.inputs_inline[I_offset_subgroup_value:I_offset_subgroup_value + self.total_steps]
sliced_subgroup.set_var_by_array(self.input_var, TimedArray(input_sliced_values, clock=self.group.clock))
k = i
def initialize_criterion(self, **criterion_params):
# general criterion parameters
params = dict(group=self.group, traces=self.sliced_traces, spikes=self.sliced_spikes,
targets_count=self.groups*self.slices, duration=self.sliced_duration, onset=self.onset,
spikes_inline=self.spikes_inline, spikes_offset=self.spikes_offset,
traces_inline=self.traces_inline, traces_offset=self.traces_offset,trials_offset=self.trials_offset)
for key,val in criterion_params.iteritems():
params[key] = val
criterion_name = self.criterion.__class__.__name__
# criterion-specific parameters
if criterion_name == 'GammaFactor':
params['delta'] = self.criterion.delta
params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm
params['fr_weight'] = self.criterion.fr_weight
self.criterion_object = GammaFactorCriterion(**params)
if criterion_name == 'GammaFactor2':
params['delta'] = self.criterion.delta
params['coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm
params['fr_weight'] = self.criterion.fr_weight
params['nlevels'] = self.criterion.nlevels
params['level_duration'] = self.criterion.level_duration
self.criterion_object = GammaFactorCriterion2(**params)
if criterion_name == 'LpError':
params['p'] = self.criterion.p
params['varname'] = self.criterion.varname
params['method'] = self.criterion.method
params['insets'] = self.criterion.insets
params['outsets'] = self.criterion.outsets
params['points'] = self.criterion.points
self.criterion_object = LpErrorCriterion(**params)
if criterion_name == 'VanRossum':
params['tau'] = self.criterion.tau
self.criterion_object = VanRossumCriterion(**params)
if criterion_name == 'Brette':
self.criterion_object = BretteCriterion(**params)
def update_neurongroup(self, **param_values):
"""
Inject fitting parameters into the NeuronGroup
"""
# Sets the parameter values in the NeuronGroup object
self.group.reinit()
for param, value in param_values.iteritems():
self.group.state(param)[:] = kron(value, ones(self.slices)) # kron param_values if slicing
# Reinitializes the model variables
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def combine_sliced_values(self, values):
if type(values) is tuple:
combined_values = tuple([sum(reshape(v, (self.slices, -1)), axis=0) for v in values])
else:
combined_values = sum(reshape(values, (self.slices, -1)), axis=0)
return combined_values
def run(self, **param_values):
delays = param_values.pop('delays', zeros(self.neurons))
# print self.refractory,self.max_refractory
if self.max_refractory is not None:
refractory = param_values.pop('refractory', zeros(self.neurons))
else:
refractory = self.refractory*ones(self.neurons)
tau_metric = param_values.pop('tau_metric', zeros(self.neurons))
self.update_neurongroup(**param_values)
# repeat spike delays and refractory to take slices into account
delays = kron(delays, ones(self.slices))
refractory = kron(refractory, ones(self.slices))
tau_metric = kron(tau_metric, ones(self.slices))
# TODO: add here parameters to criterion_params if a criterion must use some parameters
criterion_params = dict(delays=delays)
if self.criterion.__class__.__name__ == 'Brette':
criterion_params['tau_metric'] = tau_metric
self.update_neurongroup(**param_values)
self.initialize_criterion(**criterion_params)
if self.use_gpu:
# Reinitializes the simulation object
self.mf.reinit_vars(self.criterion_object,
self.inputs_inline, self.inputs_offset,
self.spikes_inline, self.spikes_offset,
self.traces_inline, self.traces_offset,
delays, refractory
)
# LAUNCHES the simulation on the GPU
self.mf.launch(self.sliced_duration, self.stepsize)
# Synchronize the GPU values with a call to gpuarray.get()
self.criterion_object.update_gpu_values()
else:
# set the refractory period
if self.max_refractory is not None:
self.group.refractory = refractory
# Launch the simulation on the CPU
self.group.clock.reinit()
net = Network(self.group, self.criterion_object)
if self.statemonitor_var is not None:
self.statemonitors = []
for state in self.statemonitor_var:
monitor = StateMonitor(self.group, state, record=True)
self.statemonitors.append(monitor)
net.add(monitor)
net.run(self.sliced_duration)
sliced_values = self.criterion_object.get_values()
combined_values = self.combine_sliced_values(sliced_values)
values = self.criterion_object.normalize(combined_values)
return values
def get_statemonitor_values(self):
if not self.use_gpu:
return [monitor.values for monitor in self.statemonitors]
else:
return self.mf.get_statemonitor_values()
def get_spikemonitor_values(self):
if not self.use_gpu:
return [monitor.values for monitor in self.statemonitors]
else:
return self.mf.get_spikemonitor_values()
def test_stim_pyramidal_impact():
simulation_clock = Clock(dt=.5 * ms)
trial_duration = 1 * second
dcs_start_time = .5 * second
stim_levels = [-8, -6, -4, -2, -1, -.5, -.25, 0, .25, .5, 1, 2, 4, 6, 8]
voltages = np.zeros(len(stim_levels))
for idx, stim_level in enumerate(stim_levels):
print('testing stim_level %.3fpA' % stim_level)
eqs = exp_IF(default_params.C, default_params.gL, default_params.EL,
default_params.VT, default_params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp',
E=default_params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp',
E=default_params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp',
E=default_params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', default_params.tau1_nmda,
default_params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=default_params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp',
E=default_params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', default_params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp',
E=default_params.E_gaba_a)
eqs += InjectedCurrent('I_dcs: amp')
group = NeuronGroup(1,
model=eqs,
threshold=-20 * mV,
refractory=pyr_params.refractory,
reset=default_params.Vr,
compile=True,
freeze=True,
clock=simulation_clock)
group.C = pyr_params.C
group.gL = pyr_params.gL
@network_operation(clock=simulation_clock)
def inject_current(c):
if simulation_clock.t > dcs_start_time:
group.I_dcs = stim_level * pA
monitor = StateMonitor(group, 'vm', simulation_clock, record=True)
net = Network(group, monitor, inject_current)
net.run(trial_duration, report='text')
voltages[idx] = monitor.values[0, -1] * 1000
voltages = voltages - voltages[7]
plt.figure()
plt.plot(stim_levels, voltages)
plt.xlabel('Stimulation level (pA)')
plt.ylabel('Voltage Change (mV)')
plt.show()
def reinit(self):
NeuronGroup.reinit(self)
self.filterbank.buffer_init()
self.buffer_pointer = self.buffersize
self.buffer_start = -self.buffersize
def __init__(self, clock, params=zheng_params, network=None):
eqs=Equations('''
G_total : siemens
G_total_exc : siemens
ds/dt=eta*(G_total-G_base)/G_base-s/tau_s-(f_in-1.0)/tau_f : 1
df_in/dt=s/second : 1.0
dv/dt=1/tau_o*(f_in-f_out) : 1
f_out=v**(1.0/alpha) : 1
do_e/dt=1.0/(phi/f_in)*(-o_e+(1.0-g)*(1.0-(1.0-e_base/(1.0-g_0))**(1.0/f_in))) : %.4f
dcb/dt=1.0/(phi/f_in)*(-cb-(c_ab*o_e)/oe_log+c_ab*g) : 1
oe_log : 1
cmr_o=(cb-g*c_ab)/(cb_0-g_0*c_ab) : 1
dg/dt=1.0/(j*v_ratio*((r*transitTime)/e_base))*((cmr_o-1.0)-k*s) : %.4f
dq/dt=1/tau_o*((f_in*o_e/e_base)-f_out*q/v) : 1
y=v_0*((k1+k2)*(1-q)-(k2+k3)*(1-v)) : 1
G_base : siemens
eta : 1/second
tau_s : second
tau_f : second
alpha : 1
tau_o : second
v_0 : 1
k1 : 1
k2 : 1
k3 : 1
phi : %.4f*second
e_base : %.4f
g_0 : %.4f
c_ab : 1
cb_0 : 1
v_ratio : 1
j : 1
transitTime : second
k : 1
r : 1
''' % (params.e_base, params.g_0, params.phi, params.e_base, params.g_0))
NeuronGroup.__init__(self, 1, model=eqs, clock=clock, compile=True, freeze=True)
self.params=params
self.G_base=params.G_base
self.eta=params.eta
self.tau_s=params.tau_s
self.tau_f=params.tau_f
self.alpha=params.alpha
self.tau_o=params.tau_o
self.e_base=params.e_base
self.v_0=params.v_0
self.k1=params.k1
self.params.s_e=params.s_e_0*exp(-params.TE/params.T_2E)
self.params.s_i=params.s_i_0*exp(-params.TE/params.T_2I)
self.params.beta=self.params.s_e/self.params.s_i
self.k2=self.params.beta*params.r_0*self.e_base*params.TE
self.k3=self.params.beta-1.0
self.c_ab=self.params.c_ab
self.cb_0=self.params.cb_0
self.g_0=self.params.g_0
self.phi=self.params.phi
self.v_ratio=self.params.v_ratio
self.j=self.params.j
self.transitTime=self.params.transitTime
self.k=self.params.k
self.r=self.params.r
self.f_in=1.0
self.s=0.0
self.v=1.0
self.o_e=self.e_base
self.cb=self.cb_0
self.g=self.g_0
self.oe_log=np.log(1.0-self.o_e/(1.0-self.g))
self.q=1.0
if network is not None:
self.G_total = linked_var(network, 'g_syn', func=sum)
self.G_total_exc = linked_var(network, 'g_syn_exc', func=sum)
class Simulator(object):
def __init__(
self,
model,
reset,
threshold,
inputs,
input_var='I',
dt=defaultclock.dt,
refractory=0 * ms,
max_refractory=None,
spikes=None,
traces=None,
groups=1,
slices=1,
overlap=0 * second,
onset=0 * second,
neurons=1000, # = nodesize = number of neurons on this node = total number of neurons/slices
initial_values=None,
unit_type='CPU',
stepsize=100 * ms,
precision='double',
criterion=None,
statemonitor_var=None,
method='Euler'):
self.model = model
self.reset = reset
self.threshold = threshold
self.inputs = inputs
self.input_var = input_var
self.dt = dt
self.refractory = refractory
self.max_refractory = max_refractory
self.spikes = spikes
self.traces = traces
self.initial_values = initial_values
self.groups = groups
self.slices = slices
self.overlap = overlap
self.onset = onset
self.neurons = neurons
self.unit_type = unit_type
self.statemonitor_var = statemonitor_var
self.stepsize = stepsize
self.precision = precision
self.criterion = criterion
self.method = method
self.use_gpu = self.unit_type == 'GPU'
if self.statemonitor_var is not None:
self.statemonitor_values = zeros(self.neurons)
self.initialize_neurongroup()
self.transform_data()
self.inject_input()
self.initialize_criterion(delays=zeros(self.neurons))
if self.use_gpu:
self.initialize_gpu()
def initialize_neurongroup(self):
# Add 'refractory' parameter on the CPU only
if not self.use_gpu:
if self.max_refractory is not None:
refractory = 'refractory'
self.model.add_param('refractory', second)
else:
refractory = self.refractory
else:
if self.max_refractory is not None:
refractory = 0 * ms
else:
refractory = self.refractory
# Must recompile the Equations : the functions are not transfered after pickling/unpickling
self.model.compile_functions()
self.group = NeuronGroup(self.neurons,
model=self.model,
reset=self.reset,
threshold=self.threshold,
refractory=refractory,
max_refractory=self.max_refractory,
method=self.method,
clock=Clock(dt=self.dt))
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def initialize_gpu(self):
# Select integration scheme according to method
if self.method == 'Euler': scheme = euler_scheme
elif self.method == 'RK': scheme = rk2_scheme
elif self.method == 'exponential_Euler': scheme = exp_euler_scheme
else: raise Exception("The numerical integration method is not valid")
self.mf = GPUModelFitting(self.group,
self.model,
self.criterion_object,
self.input_var,
self.onset,
statemonitor_var=self.statemonitor_var,
duration=self.sliced_duration,
precision=self.precision,
scheme=scheme)
def transform_data(self):
self.transformer = DataTransformer(self.neurons,
self.inputs,
spikes=self.spikes,
traces=self.traces,
dt=self.dt,
slices=self.slices,
overlap=self.overlap,
groups=self.groups)
self.total_steps = self.transformer.total_steps
self.sliced_duration = self.transformer.sliced_duration
self.sliced_inputs = self.transformer.slice_traces(self.inputs)
self.inputs_inline, self.inputs_offset = self.transformer.transform_traces(
self.sliced_inputs)
if self.traces is not None:
self.sliced_traces = self.transformer.slice_traces(self.traces)
self.traces_inline, self.traces_offset = self.transformer.transform_traces(
self.sliced_traces)
else:
self.sliced_traces, self.traces_inline, self.traces_offset = None, None, None
if self.spikes is not None:
self.sliced_spikes = self.transformer.slice_spikes(self.spikes)
self.spikes_inline, self.spikes_offset = self.transformer.transform_spikes(
self.sliced_spikes)
else:
self.sliced_spikes, self.spikes_inline, self.spikes_offset = None, None, None
def inject_input(self):
# Injects current in consecutive subgroups, where I_offset have the same value
# on successive intervals
I_offset = self.inputs_offset
k = -1
for i in hstack((nonzero(diff(I_offset))[0], len(I_offset) - 1)):
I_offset_subgroup_value = I_offset[i]
I_offset_subgroup_length = i - k
sliced_subgroup = self.group.subgroup(I_offset_subgroup_length)
input_sliced_values = self.inputs_inline[
I_offset_subgroup_value:I_offset_subgroup_value +
self.total_steps]
sliced_subgroup.set_var_by_array(
self.input_var,
TimedArray(input_sliced_values, clock=self.group.clock))
k = i
def initialize_criterion(self, **criterion_params):
# general criterion parameters
params = dict(group=self.group,
traces=self.sliced_traces,
spikes=self.sliced_spikes,
targets_count=self.groups * self.slices,
duration=self.sliced_duration,
onset=self.onset,
spikes_inline=self.spikes_inline,
spikes_offset=self.spikes_offset,
traces_inline=self.traces_inline,
traces_offset=self.traces_offset)
for key, val in criterion_params.iteritems():
params[key] = val
criterion_name = self.criterion.__class__.__name__
# criterion-specific parameters
if criterion_name == 'GammaFactor':
params['delta'] = self.criterion.delta
params[
'coincidence_count_algorithm'] = self.criterion.coincidence_count_algorithm
self.criterion_object = GammaFactorCriterion(**params)
if criterion_name == 'LpError':
params['p'] = self.criterion.p
params['varname'] = self.criterion.varname
self.criterion_object = LpErrorCriterion(**params)
def update_neurongroup(self, **param_values):
"""
Inject fitting parameters into the NeuronGroup
"""
# Sets the parameter values in the NeuronGroup object
self.group.reinit()
for param, value in param_values.iteritems():
self.group.state(param)[:] = kron(value, ones(
self.slices)) # kron param_values if slicing
# Reinitializes the model variables
if self.initial_values is not None:
for param, value in self.initial_values.iteritems():
self.group.state(param)[:] = value
def combine_sliced_values(self, values):
if type(values) is tuple:
combined_values = tuple(
[sum(reshape(v, (self.slices, -1)), axis=0) for v in values])
else:
combined_values = sum(reshape(values, (self.slices, -1)), axis=0)
return combined_values
def run(self, **param_values):
delays = param_values.pop('delays', zeros(self.neurons))
refractory = param_values.pop('refractory', zeros(self.neurons))
self.update_neurongroup(**param_values)
# repeat spike delays and refractory to take slices into account
delays = kron(delays, ones(self.slices))
refractory = kron(refractory, ones(self.slices))
# TODO: add here parameters to criterion_params if a criterion must use some parameters
criterion_params = dict(delays=delays)
self.update_neurongroup(**param_values)
self.initialize_criterion(**criterion_params)
if self.use_gpu:
# Reinitializes the simulation object
self.mf.reinit_vars(self.criterion_object, self.inputs_inline,
self.inputs_offset, self.spikes_inline,
self.spikes_offset, self.traces_inline,
self.traces_offset, delays, refractory)
# LAUNCHES the simulation on the GPU
self.mf.launch(self.sliced_duration, self.stepsize)
# Synchronize the GPU values with a call to gpuarray.get()
self.criterion_object.update_gpu_values()
else:
# set the refractory period
if self.max_refractory is not None:
self.group.refractory = refractory
# Launch the simulation on the CPU
self.group.clock.reinit()
net = Network(self.group, self.criterion_object)
if self.statemonitor_var is not None:
self.statemonitor = StateMonitor(self.group,
self.statemonitor_var,
record=True)
net.add(self.statemonitor)
net.run(self.sliced_duration)
sliced_values = self.criterion_object.get_values()
combined_values = self.combine_sliced_values(sliced_values)
values = self.criterion_object.normalize(combined_values)
return values
def get_statemonitor_values(self):
if not self.use_gpu:
return self.statemonitor.values
else:
return self.mf.get_statemonitor_values()
tau1=tau1,
tau2=tau2e,
)
model_eqs_i = Equations(eqs_string,
tau=taui,
tau1=tau1,
tau2=tau2i,
)
# Divide the neurons into excitatory and inhibitory ones
neurons_e = NeuronGroup(N=N_e,
model=model_eqs_e,
threshold=V_th,
reset=V_reset,
refractory=refr_period,
freeze = True,
method='Euler',
compile=True)
neurons_i = NeuronGroup(N=N_i,
model=model_eqs_i,
threshold=V_th,
reset=V_reset,
refractory=refr_period,
freeze = True,
method='Euler',
compile=True)
neurons_e.myu = np.random.uniform(myueMin, myueMax, N_e)
betah=1./(exp(-0.1/mV*(v+28*mV))+1)/ms : Hz
dn/dt=5*(alphan*(1-n)-betan*n) : 1
alphan=-0.01/mV*(v+34*mV)/(exp(-0.1/mV*(v+34*mV))-1)/ms : Hz
betan=0.125*exp(-(v+44*mV)/(80*mV))/ms : Hz
dgExc/dt = -gExc*(1./taue) : siemens
dgInh/dt = -gInh*(1./taui) : siemens
Iapp : amp
'''
neuron = NeuronGroup(len(inputcurrents), eqs, threshold=threshold, method='RK')
sim.add(neuron)
# Init conditions
neuron.v = -65*mV
neuron.Iapp = inputcurrents
neuron.h = 1
# Monitors
vmon = StateMonitor(neuron, 'v', record=True)
nmon = StateMonitor(neuron, 'n', record=True)
sim.add(vmon, nmon)
# Run
sim.run(duration, report='text')
