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 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 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 setup_sims(neuron_params, input_params, duration): fin = input_params.get("fin") fout = input_params.get("fout") weight = input_params.get("weight") num_inp = input_params.get("num_inp") sync_configs = input_params.get("sync") if fin is None: fin = sl.tools.calibrate_frequencies(neuron_params, N_in=num_inp, w_in=weight, f_out=fout, synchrony_conf=sync_configs) brian.clear(True) gc.collect() brian.defaultclock.reinit() neurons = NeuronGroup(N=len(sync_configs), **neuron_params) simulation = Network(neurons) input_groups = [] for idx, (inrate, (sync, jitter)) in enumerate(zip(fin, sync_configs)): inp_grp = sl.tools.fast_synchronous_input_gen(num_inp, inrate*Hz, sync, jitter, duration) simulation.add(inp_grp) inp_conn = Connection(inp_grp, neurons[idx], state='V', weight=weight) input_groups.append(inp_grp) simulation.add(inp_conn) tracemon = StateMonitor(neurons, 'V', record=True) spikemon = SpikeMonitor(neurons) inputmons = [SpikeMonitor(igrp) for igrp in input_groups] simulation.add(tracemon, spikemon, inputmons) monitors = {"inputs": inputmons, "outputs": spikemon, "traces": tracemon} return simulation, monitors
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 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
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
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) sim.run(duration)
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
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')
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') plt.figure("Voltage")
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")
inputgroups = [] connections = [] print("Setting up ...") for idx, c in enumerate(configs): n, f, w = c inp = PoissonGroup(n, f) conn = Connection(inp, nrn[idx], state="V", weight=w) 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.")
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()
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 Sin = [] for nrn in range(Nnrns):