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 __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 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 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 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 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
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