def run_pop_code(pop_class, N, network_params, stimuli, trial_duration, report=None):
simulation_clock=Clock(dt=1*ms)
pop=pop_class(N,simulation_clock,network_params)
pop_monitor=MultiStateMonitor(pop, vars=['x','r','e'], record=True, clock=simulation_clock)
@network_operation(when='start', clock=simulation_clock)
def get_pop_input():
pop.x=0.0
for stimulus in stimuli:
if stimulus.start_time<simulation_clock.t<stimulus.end_time:
pop.x+=pop.get_population_function(stimulus.x,stimulus.var)
net=Network(pop, pop_monitor, get_pop_input)
#reinit_default_clock()
net.run(trial_duration, report=report)
g_total=np.sum(np.clip(pop_monitor['e'].values,0,1) * pop_monitor['x'].values, axis=0)+0.1
voxel_monitor=get_bold_signal(g_total, voxel.default_params, range(int(stimuli[0].start_time/simulation_clock.dt)), trial_duration)
# There is only one peak with rapid design
if trial_duration>6*second:
y_max=np.max(voxel_monitor['y'][0][60000:])
else:
y_max=np.max(voxel_monitor['y'][0])
return pop_monitor, voxel_monitor, y_max
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 get_bold_signal(g_total, voxel_params, baseline_range, trial_duration):
simulation_clock = Clock(dt=1 * ms)
voxel = Voxel(simulation_clock, params=voxel_params)
voxel.G_base = g_total[baseline_range[0]:baseline_range[1]].mean()
voxel_monitor = MultiStateMonitor(
voxel,
vars=['G_total', 's', 'f_in', 'v', 'f_out', 'q', 'y'],
record=True,
clock=simulation_clock)
@network_operation(when='start', clock=simulation_clock)
def get_input():
idx = int(simulation_clock.t / simulation_clock.dt)
if idx < baseline_range[0]:
voxel.G_total = voxel.G_base
elif idx < len(g_total):
voxel.G_total = g_total[idx]
else:
voxel.G_total = voxel.G_base
net = Network(voxel, get_input, voxel_monitor)
#reinit_default_clock()
bold_trial_duration = 10 * second
if trial_duration + 6 * second > bold_trial_duration:
bold_trial_duration = trial_duration + 6 * second
net.run(bold_trial_duration)
return voxel_monitor
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 get_bold_signal(g_total, voxel_params, baseline_range, trial_duration):
simulation_clock=Clock(dt=1*ms)
voxel=Voxel(simulation_clock, params=voxel_params)
voxel.G_base=g_total[baseline_range[0]:baseline_range[1]].mean()
voxel_monitor = MultiStateMonitor(voxel, vars=['G_total','s','f_in','v','f_out','q','y'], record=True, clock=simulation_clock)
@network_operation(when='start', clock=simulation_clock)
def get_input():
idx=int(simulation_clock.t/simulation_clock.dt)
if idx<baseline_range[0]:
voxel.G_total=voxel.G_base
elif idx<len(g_total):
voxel.G_total=g_total[idx]
else:
voxel.G_total=voxel.G_base
net=Network(voxel, get_input, voxel_monitor)
#reinit_default_clock()
bold_trial_duration=10*second
if trial_duration+6*second>bold_trial_duration:
bold_trial_duration=trial_duration+6*second
net.run(bold_trial_duration)
return voxel_monitor
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 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 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 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 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 run_restricted_pop_code(pop_class, N, network_params, stimuli, trial_duration, report=None):
simulation_clock=Clock(dt=1*ms)
pop=pop_class(N, simulation_clock, network_params)
#pop_monitor=MultiStateMonitor(pop, vars=['x','r','e','total_e','total_r'], record=True)
pop_monitor=MultiStateMonitor(pop, vars=['x','r','e'], record=True, clock=simulation_clock)
@network_operation(when='start', clock=simulation_clock)
def get_pop_input():
pop.x=0.0
for stimulus in stimuli:
if stimulus.start_time<simulation_clock.t<stimulus.end_time:
pop.x+=pop.get_population_function(stimulus.x,stimulus.var)
net=Network(pop, pop_monitor, get_pop_input)
#reinit_default_clock()
net.run(trial_duration, report=report)
g_total=np.sum(np.clip(pop_monitor['e'].values,0,1) * pop_monitor['x'].values, axis=0)+0.1
voxel_monitor=get_bold_signal(g_total, voxel.default_params, range(int(stimuli[0].start_time/simulation_clock.dt)), trial_duration)
return voxel_monitor
def lifsim(mu_amp, mu_offs, simga_amp, sigma_offs, freq, V_th):
lifnet = Network()
clock.reinit_default_clock()
eqs = Equations('dV/dt = (-V+V0)/tau : volt')
eqs.prepare()
lifnrn = NeuronGroup(1, eqs, threshold=V_th, refractory=t_refr,
reset=V_reset)
lifnet.add(lifnrn)
pulse_times = (np.arange(1, duration*freq, 1)+0.25)/freq
pulse_spikes = []
Npoiss = 5000
Npulse = 5000
wpoiss = (mu_offs-mu_amp)/(Npoiss*freq)
wpulse = mu_amp/(Npulse*freq)
sigma = 1/(freq*5)
if (wpulse != 0):
for pt in pulse_times:
pp = PulsePacket(t=pt*second, n=Npulse, sigma=sigma)
pulse_spikes.extend(pp.spiketimes)
pulse_input = SpikeGeneratorGroup(Npulse, pulse_spikes)
pulse_conn = Connection(pulse_input, lifnrn, 'V', weight=wpulse)
lifnet.add(pulse_input, pulse_conn)
if (wpoiss != 0):
poiss_input = PoissonGroup(Npoiss, freq)
poiss_conn = Connection(poiss_input, lifnrn, 'V', weight=wpoiss)
lifnet.add(poiss_input, poiss_conn)
V_mon = StateMonitor(lifnrn, 'V', record=True)
st_mon = SpikeMonitor(lifnrn)
lifnet.add(V_mon, st_mon)
lifnet.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 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 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
network.add(*inpconns)
asympt_v = fin*weight*tau*Nin
print("Asymptotic threshold-free membrane potential: %s" % (asympt_v))
max_volley = max(ingroup_sync)*Nin*weight
print("Max spike volley potential: %s" % (max_volley))
print("Setting up monitors ...")
inpmons = [SpikeMonitor(ing) for ing in ingroups]
network.add(*inpmons)
vmon = StateMonitor(nrngroup, 'V', record=True)
network.add(vmon)
spikemon = SpikeMonitor(nrngroup)
network.add(spikemon)
print("Running simulation for %s ..." % (duration))
network.run(duration, report="stdout")
print("Done.")
###### THE OTHER STUFF ######
print("Computing results ...")
t = np.arange(0*second, duration, dt)
best_corr = -1
if spikemon.nspikes:
vmon.insert_spikes(spikemon, Vth*2)
print("Calculating pairwise, interval Kreuz distance for the inputs of each cell ...")
krinpdist = dist_inputs_interval_all(inputneurons, spikemon, *inpmons)
meankr = [np.mean(kr) for kr in krinpdist]
printstats(vmon, spikemon)
mslopes, allslopes = calcslopes(vmon, spikemon)
n = 0
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()
inputgroups.append(inp)
connections.append(conn)
print("\r{}/{}".format(idx + 1, Nsims), end="")
sys.stdout.flush()
print()
spikemon = SpikeMonitor(nrn)
sim.add(*inputgroups)
sim.add(*connections)
sim.add(nrn)
sim.add(spikemon)
duration = 1000 * ms
print("Running for {} s".format(duration))
sim.run(duration, report="text")
plt.figure()
inputvolts = np.array([c[0] * c[1] * c[2] * tau for c in configs])
spikerates = np.array([len(sp) for sp in spikemon.spiketimes.itervalues()])
for idx in range(Nsims):
iv = inputvolts[idx]
sr = spikerates[idx]
plt.plot(iv, sr, "b.")
print("{} mV -> {} Hz".format(iv * 1000, sr / duration))
ivsorted = np.sort(inputvolts)
theofout = 1.0 / (tau * np.log(ivsorted / (ivsorted - Vth)))
theovin = Vth / (1 - np.exp(-1.0 / (tau * spikerates)))
plt.plot(ivsorted, theofout, "r-")
sidx = np.argsort(theovin)
plt.plot(theovin[sidx], spikerates[sidx], "g-")
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()
def run_neglect(input_freq, delay_duration, net_params=default_params, output_file=None, record_lfp=True, record_voxel=True,
record_neuron_state=False, record_spikes=True, record_pop_firing_rate=True, record_neuron_firing_rate=False,
record_inputs=False, plot_output=False, mem_trial=False):
start_time=time()
# Init simulation parameters
background_input_size=1000
#background_rate=20*Hz
#background_rate=30*Hz
background_rate=25*Hz
visual_input_size=1000
#visual_background_rate=10*Hz
visual_background_rate=5*Hz
#visual_stim_min_rate=15*Hz
#visual_stim_min_rate=10*Hz
visual_stim_min_rate=8*Hz
visual_stim_tau=0.15
go_input_size=1000
go_rate=20*Hz
#go_background_rate=1*Hz
go_background_rate=0*Hz
lip_size=6250
#stim_start_time=1.8*second
#stim_end_time=2*second
stim_start_time=.5*second
stim_end_time=.7*second
#go_start_time=3*second
#go_end_time=3.1*second
#go_start_time=1.7*second
go_start_time=stim_end_time+delay_duration
#go_end_time=1.8*second
go_end_time=go_start_time+.2*second
trial_duration=go_end_time+.5*second
# Create network inputs
background_inputs=[PoissonGroup(background_input_size, rates=background_rate),
PoissonGroup(background_input_size, rates=background_rate)]
def make_mem_rate_function(rate):
return lambda t: ((stim_start_time<t<stim_end_time and np.max([visual_background_rate,rate*exp(-(t-stim_start_time)/visual_stim_tau)])) or visual_background_rate)
def make_delay_rate_function(rate):
return lambda t: ((stim_start_time<t and np.max([visual_stim_min_rate,rate*exp(-(t-stim_start_time)/visual_stim_tau)])) or visual_background_rate)
def make_go_rate_function():
return lambda t: ((go_start_time<t<go_end_time and go_rate) or go_background_rate)
lrate=input_freq[0]*Hz
rrate=input_freq[1]*Hz
if mem_trial:
visual_cortex_inputs=[PoissonGroup(visual_input_size, rates=make_mem_rate_function(lrate)),
PoissonGroup(visual_input_size, rates=make_mem_rate_function(rrate))]
else:
visual_cortex_inputs=[PoissonGroup(visual_input_size, rates=make_delay_rate_function(lrate)),
PoissonGroup(visual_input_size, rates=make_delay_rate_function(rrate))]
go_input=PoissonGroup(go_input_size, rates=make_go_rate_function())
# Create WTA network
brain_network=BrainNetworkGroup(lip_size, params=net_params, background_inputs=background_inputs,
visual_cortex_input=visual_cortex_inputs, go_input=go_input)
# LFP source
left_lip_lfp_source=LFPSource(brain_network.left_lip.e_group)
right_lip_lfp_source=LFPSource(brain_network.right_lip.e_group)
# Create voxel
left_lip_voxel=Voxel(network=brain_network.left_lip.neuron_group)
right_lip_voxel=Voxel(network=brain_network.right_lip.neuron_group)
# Create network monitor
brain_monitor=BrainMonitor(background_inputs, visual_cortex_inputs, go_input, brain_network, left_lip_lfp_source,
right_lip_lfp_source, left_lip_voxel, right_lip_voxel, record_lfp=record_lfp, record_voxel=record_voxel,
record_neuron_state=record_neuron_state, record_spikes=record_spikes, record_pop_firing_rate=record_pop_firing_rate,
record_neuron_firing_rates=record_neuron_firing_rate, record_inputs=record_inputs)
# Create Brian network and reset clock
net=Network(background_inputs, visual_cortex_inputs, go_input, brain_network, left_lip_lfp_source, right_lip_lfp_source,
left_lip_voxel, right_lip_voxel, brain_network.connections, brain_monitor.monitors)
reinit_default_clock()
print "Initialization time:", time() - start_time
# Run simulation
start_time = time()
net.run(trial_duration, report='text')
print "Simulation time:", time() - start_time
# Compute BOLD signal
if record_voxel:
start_time=time()
brain_monitor.left_voxel_exc_monitor=get_bold_signal(brain_monitor.left_voxel_monitor['G_total_exc'].values[0],
left_lip_voxel.params, [500, 1500], trial_duration)
brain_monitor.left_voxel_monitor=get_bold_signal(brain_monitor.left_voxel_monitor['G_total'].values[0],
left_lip_voxel.params, [500, 1500], trial_duration)
brain_monitor.right_voxel_exc_monitor=get_bold_signal(brain_monitor.right_voxel_monitor['G_total_exc'].values[0],
right_lip_voxel.params, [500, 1500], trial_duration)
brain_monitor.right_voxel_monitor=get_bold_signal(brain_monitor.right_voxel_monitor['G_total'].values[0],
right_lip_voxel.params, [500, 1500], trial_duration)
print 'Time to compute BOLD:', time() - start_time
# Plot outputs
if plot_output:
brain_monitor.plot(trial_duration)
if output_file is not None:
write_output(brain_network, background_input_size, background_rate, visual_input_size, input_freq,
trial_duration, stim_start_time, stim_end_time, go_start_time, go_end_time, record_pop_firing_rate,
record_neuron_state, record_spikes, record_voxel, record_lfp, record_inputs, output_file, left_lip_voxel,
right_lip_voxel, brain_monitor)
return brain_monitor
def runsim(Nin, weight, fout, sync):
sim = Network()
clear(True)
gc.collect()
defaultclock.reinit()
duration = 5*second
lifeq = "dV/dt = -V/(10*ms) : volt"
nrndef = {"model": lifeq, "threshold": "V>=15*mV", "reset": "V=0*mV",
"refractory": 2*ms}
fin = load_or_calibrate(nrndef, Nin, weight, sync, fout,
Vth=15*mV, tau=10*ms)
# print("Calibrated frequencies:")
# print(", ".join(str(f) for f in fin))
inputgroups = []
connections = []
neurons = []
Nneurons = len(fin)
neurons = NeuronGroup(Nneurons, **nrndef)
for idx in range(Nneurons):
fin_i = fin[idx]
sync_i, sigma_i = sync[idx]
inputgrp = sl.tools.fast_synchronous_input_gen(Nin, fin_i,
sync_i, sigma_i,
duration)
defaultclock.reinit()
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)
print("Running {} {} {}".format(Nin, weight, fout))
sim.run(duration, report="stdout")
mnpss = []
allnpss = []
for idx in range(Nneurons):
vmon = voltagemon[idx]
smon = spikemon[idx]
# print("Desired firing rate: {}".format(fout))
# print("Actual firing rate: {}".format(len(smon)/duration))
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)
nrndeftuple = tuple(nrndef.items())
key = (nrndeftuple, Nin, weight, tuple(sync), fout, 15*mV, 10*ms)
save_data(key, allnpss)
imshape = (len(sigma), len(Sin))
imextent = (0, 1, 0, 4.0)
mnpss = np.reshape(mnpss, imshape, order="F")
plt.figure()
plt.imshow(mnpss, aspect="auto", origin="lower", extent=imextent,
interpolation="none", vmin=0, vmax=1)
cbar = plt.colorbar()
cbar.set_label("$\overline{M}$")
plt.xlabel("$S_{in}$")
plt.ylabel("$\sigma_{in}$ (ms)")
filename = "npss_{}_{}_{}".format(Nin, weight, fout).replace(".", "")
plt.savefig(filename+".pdf")
plt.savefig(filename+".png")
print("{} saved".format(filename))
voltages = voltagemon.values
spiketrains = spikemon.spiketimes.values()
pickle.dump({"voltages": voltages, "spiketrains": spiketrains},
open(filename+".pkl", 'w'))
return voltagemon, spikemon
class VirtualSubject:
def __init__(self, subj_id, wta_params=default_params(), pyr_params=pyr_params(), inh_params=inh_params(),
plasticity_params=plasticity_params(), sim_params=simulation_params()):
self.subj_id = subj_id
self.wta_params = wta_params
self.pyr_params = pyr_params
self.inh_params = inh_params
self.plasticity_params = plasticity_params
self.sim_params = sim_params
self.simulation_clock = Clock(dt=self.sim_params.dt)
self.input_update_clock = Clock(dt=1 / (self.wta_params.refresh_rate / Hz) * second)
self.background_input = PoissonGroup(self.wta_params.background_input_size,
rates=self.wta_params.background_freq, clock=self.simulation_clock)
self.task_inputs = []
for i in range(self.wta_params.num_groups):
self.task_inputs.append(PoissonGroup(self.wta_params.task_input_size,
rates=self.wta_params.task_input_resting_rate, clock=self.simulation_clock))
# Create WTA network
self.wta_network = WTANetworkGroup(params=self.wta_params, background_input=self.background_input,
task_inputs=self.task_inputs, pyr_params=self.pyr_params, inh_params=self.inh_params,
plasticity_params=self.plasticity_params, clock=self.simulation_clock)
# Create network monitor
self.wta_monitor = WTAMonitor(self.wta_network, None, None, self.sim_params, record_lfp=False,
record_voxel=False, record_neuron_state=False, record_spikes=False,
record_firing_rate=True, record_inputs=True, record_connections=None,
save_summary_only=False, clock=self.simulation_clock)
# Create Brian network and reset clock
self.net = Network(self.background_input, self.task_inputs, self.wta_network,
self.wta_network.connections.values(), self.wta_monitor.monitors.values())
def run_trial(self, sim_params, input_freq):
self.wta_monitor.sim_params=sim_params
self.net.reinit(states=False)
@network_operation(when='start', clock=self.input_update_clock)
def set_task_inputs():
for idx in range(len(self.task_inputs)):
rate = self.wta_params.task_input_resting_rate
if sim_params.stim_start_time <= self.simulation_clock.t < sim_params.stim_end_time:
rate = input_freq[idx] * Hz + np.random.randn() * self.wta_params.input_var
if rate < self.wta_params.task_input_resting_rate:
rate = self.wta_params.task_input_resting_rate
self.task_inputs[idx]._S[0, :] = rate
@network_operation(clock=self.simulation_clock)
def inject_current():
if sim_params.dcs_start_time < self.simulation_clock.t <= sim_params.dcs_end_time:
self.wta_network.group_e.I_dcs = sim_params.p_dcs
self.wta_network.group_i.I_dcs = sim_params.i_dcs
else:
self.wta_network.group_e.I_dcs = 0 * pA
self.wta_network.group_i.I_dcs = 0 * pA
@network_operation(when='start', clock=self.simulation_clock)
def inject_muscimol():
if sim_params.muscimol_amount > 0:
self.wta_network.groups_e[sim_params.injection_site].g_muscimol = sim_params.muscimol_amount
self.net.remove(set_task_inputs, inject_current, inject_muscimol, self.wta_network.stdp.values())
self.net.add(set_task_inputs, inject_current, inject_muscimol)
if sim_params.plasticity:
self.net.add(self.wta_network.stdp.values())
self.net.run(sim_params.trial_duration, report='text')
#self.wta_monitor.plot()
self.net.remove(set_task_inputs, inject_current, inject_muscimol, self.wta_network.stdp.values())
PoissonInput,
mV, ms, second, Hz)
import numpy as np
network = Network()
tau = 20*ms
eqs = "dV/dt = -V/tau : volt"
lifgroup = NeuronGroup(10, eqs, threshold="V>=(20*mV)", reset=0*mV)
weights = np.linspace(0.1, 1, 10)
rates = np.arange(10, 100, 10)
inputgroups = []
for idx, (w, r) in enumerate(zip(weights, rates)):
inpgrp = PoissonInput(lifgroup[idx], 20, r*Hz, w*mV, state="V")
inputgroups.append(inpgrp)
network.add(lifgroup)
network.add(*inputgroups)
spikemon = SpikeMonitor(lifgroup)
vmon = StateMonitor(lifgroup, "V", record=True)
network.add(spikemon, vmon)
network.run(10*second, report="stdout")
spikes = spikemon.spiketimes.values()
voltage = vmon.values
np.savez("results.npz",
spikes=spikes,
voltages=voltage)
print("DONE")
'''
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')
plt.figure("Voltage")
plt.plot(vmon.times, vmon[0])
plt.figure("Bifurcation diagram")
for idx, incurrent in enumerate(inputcurrents):
label = display_in_unit(incurrent, uA)
plt.clf()
plt.plot(vmon[idx], nmon[idx], label=label, linestyle="", marker=".")
plt.axis(xmin=-0.08, xmax=0.08, ymin=0, ymax=1)
plt.title(label)
filename = "/home/achilleas/tmp/hh_bf{:04}.png".format(idx)
plt.savefig(filename)
print("Saved {}".format(filename))
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
class VirtualSubject:
def __init__(self, subj_id, wta_params=default_params(), pyr_params=pyr_params(), inh_params=inh_params(),
sim_params=simulation_params(), network_class=WTANetworkGroup):
self.subj_id = subj_id
self.wta_params = wta_params
self.pyr_params = pyr_params
self.inh_params = inh_params
self.sim_params = sim_params
self.simulation_clock = Clock(dt=self.sim_params.dt)
self.input_update_clock = Clock(dt=1 / (self.wta_params.refresh_rate / Hz) * second)
self.background_input = PoissonGroup(self.wta_params.background_input_size,
rates=self.wta_params.background_freq, clock=self.simulation_clock)
self.task_inputs = []
for i in range(self.wta_params.num_groups):
self.task_inputs.append(PoissonGroup(self.wta_params.task_input_size,
rates=self.wta_params.task_input_resting_rate, clock=self.simulation_clock))
# Create WTA network
self.wta_network = network_class(params=self.wta_params, background_input=self.background_input,
task_inputs=self.task_inputs, pyr_params=self.pyr_params, inh_params=self.inh_params,
clock=self.simulation_clock)
# Create network monitor
self.wta_monitor = WTAMonitor(self.wta_network, self.sim_params, record_neuron_state=False, record_spikes=False,
record_firing_rate=True, record_inputs=True, save_summary_only=False,
clock=self.simulation_clock)
# Create Brian network and reset clock
self.net = Network(self.background_input, self.task_inputs, self.wta_network,
self.wta_network.connections.values(), self.wta_monitor.monitors.values())
def run_trial(self, sim_params, input_freq):
self.wta_monitor.sim_params=sim_params
self.net.reinit(states=False)
@network_operation(when='start', clock=self.input_update_clock)
def set_task_inputs():
for idx in range(len(self.task_inputs)):
rate = self.wta_params.task_input_resting_rate
if sim_params.stim_start_time <= self.simulation_clock.t < sim_params.stim_end_time:
rate = input_freq[idx] * Hz + np.random.randn() * self.wta_params.input_var
if rate < self.wta_params.task_input_resting_rate:
rate = self.wta_params.task_input_resting_rate
self.task_inputs[idx]._S[0, :] = rate
@network_operation(clock=self.simulation_clock)
def inject_current():
if sim_params.dcs_start_time < self.simulation_clock.t <= sim_params.dcs_end_time:
self.wta_network.group_e.I_dcs = sim_params.p_dcs
self.wta_network.group_i.I_dcs = sim_params.i_dcs
else:
self.wta_network.group_e.I_dcs = 0 * pA
self.wta_network.group_i.I_dcs = 0 * pA
self.net.remove(set_task_inputs, inject_current)
self.net.add(set_task_inputs, inject_current)
self.net.run(sim_params.trial_duration, report='text')
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
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)
sim.run(duration)
if spikemon.nspikes == 0:
print("No spikes fired")
sys.exit(1)
vmon.insert_spikes(spikemon, 40*mV)
high, low = sl.tools.get_slope_bounds(spikemon[0], 0*mV, Vreset, Vth, tau, dt)
high *= 1000
thigh = np.arange(len(high))*dt*1000
low *= 1000
tlow = np.arange(len(low))*dt*1000
Vth = float(Vth*1000)
plt.figure()
plt.plot(vmon.times*1000, vmon[0]*1000, label="$V(t)$")
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
spike_mon_e = SpikeMonitor(neurons_e)
spike_mon_i = SpikeMonitor(neurons_i)
#state_mon_v_e = StateMonitor(neurons_e, 'V', record=[0,1,2])
#state_mon_v_i = StateMonitor(neurons_i, 'V', record=[0,1])
#state_mon_isyn = StateMonitor(neurons_i, 'Isyn', record=[0,1])
# Put everything into the network conn_ii conn_ee_clusters, conn_ei, conn_ie,
network = Network(neurons_e, neurons_i, spike_mon_e, conn_ii, conn_ee_clusters, conn_ie, conn_ei, spike_mon_i)#, state_mon_v_i, state_mon_isyn, state_mon_v_e,)
# Let's run our simulation
network.run(duration, report='text')
#plt.figure()
#plt.plot(state_mon_isyn.times,state_mon_isyn.values[0,:])
# Plot spike raster plots, blue exc neurons, red inh neurons
plt.figure()
gs = gridspec.GridSpec(2, 1, height_ratios=[1, 3])
plt.subplot(gs[0])
raster_plot(spike_mon_i, color='r')
plt.title('Inhibitory neurons')
plt.subplot(gs[1])
raster_plot(spike_mon_e)
plt.title('Excitatory neurons')
