def simulate_response_example_clamped_spiking(I_e): ''' Response when SNR is clamped to a voltage for strong and weak synapse ''' spike_at = 500. # ms simTime = 700. # ms my_nest.ResetKernel() model_list = models() my_nest.MyLoadModels(model_list, NEURON_MODELS) my_nest.MyLoadModels(model_list, SYNAPES_MODELS) SNR = MyGroup(NEURON_MODELS[0], len(SYNAPES_MODELS) + 1, mm=True, mm_dt=0.1, params={'I_e': I_e}) SG = my_nest.Create('spike_generator', params={'spike_times': [spike_at]}) for i in range(len(SYNAPES_MODELS)): my_nest.Connect(SG, [SNR[i]], model=SYNAPES_MODELS[i]) my_nest.MySimulate(simTime) SNR.get_signal('v', 'V_m', stop=simTime) # retrieve signal SNR.signals['V_m'] = SNR.signals['V_m'].my_time_slice(500, 560) return SNR
def simulate_steady_state_freq(frequencies, flag='ss'): global sname_nb relativeFacilitation=[] model_list=models() data={} n=len(frequencies) for syn in synapseModels: my_nest.ResetKernel() my_nest.MyLoadModels( model_list, neuronModels ) my_nest.MyLoadModels( model_list, [syn]) ss=my_nest.GetDefaults(syn) synapticEficacy = ss['weight']*ss['U'] SNR = MyGroup( neuronModels[0], n, mm_dt = .1, params={'I_e':-150.}, record_from=['g_GABAA_2'], spath=spath, sname_nb=sname_nb ) sname_nb+=1 tSim=3*1000/frequencies[0] spikeTimes=[] for f in frequencies : isi = 1000./f spikeTimes.append(numpy.arange(1,tSim,isi)) if not LOAD: for target, st in zip(SNR, spikeTimes ) : source = my_nest.Create('spike_generator', params={'spike_times':st} ) my_nest.SetDefaults(syn, params={'delay':1.}) my_nest.Connect(source, [target], model=syn) my_nest.MySimulate(tSim) SNR.get_signal( 'g','g_GABAA_2', stop=tSim ) # retrieve signal SNR.save_signal( 'g','g_GABAA_2', stop=tSim ) elif LOAD: SNR.load_signal( 'g','g_GABAA_2') signal=SNR.signals['g_GABAA_2'] tmpSteadyState=[] for i, st in enumerate(spikeTimes, start=1): if SNR.mm_dt==0.1: indecies=numpy.int64(numpy.ceil(st*10))+9 elif SNR.mm_dt==1.: indecies=numpy.int64(numpy.ceil(st)) values=signal[i].signal[indecies]-signal[i].signal[indecies-1] if flag=='ss': tmpSteadyState.append(values[-1]/synapticEficacy) if flag=='max': tmpSteadyState.append(max(values)/synapticEficacy) relativeFacilitation.append(tmpSteadyState) relativeFacilitation=numpy.array(relativeFacilitation) return frequencies, relativeFacilitation
def simulate_recovery(revoceryTimes): global sname_nb relativeRecovery=[] model_list=models() data={} n=len(revoceryTimes) for syn in synapseModels: my_nest.ResetKernel() my_nest.MyLoadModels( model_list, neuronModels ) my_nest.MyLoadModels( model_list, [syn]) ss=my_nest.GetDefaults(syn) synapticEficacy = ss['weight']*ss['U'] SNR = MyGroup( neuronModels[0], n, mm_dt = .1, params={'I_e':-150.}, record_from=['g_GABAA_2'], spath=spath, sname_nb=sname_nb) sname_nb+=1 tSim=5000 spikeTimes=[] for rt in revoceryTimes: spikeTimes.append(numpy.array([1.,11.,21.,31.,41.,41+rt])) if not LOAD: for target, st in zip(SNR, spikeTimes ) : source = my_nest.Create('spike_generator', params={'spike_times':st} ) my_nest.SetDefaults(syn, params={'delay':1.}) my_nest.Connect(source, [target], model=syn) my_nest.MySimulate(tSim) SNR.get_signal( 'g','g_GABAA_2', stop=tSim ) # retrieve signal SNR.save_signal( 'g','g_GABAA_2', stop=tSim ) elif LOAD: SNR.load_signal( 'g','g_GABAA_2') signal=SNR.signals['g_GABAA_2'] tmpSteadyState=[] for i, st in enumerate(spikeTimes, start=1): if SNR.mm_dt==0.1: indecies=numpy.int64(numpy.ceil(st*10))+9 elif SNR.mm_dt==1.: indecies=numpy.int64(numpy.ceil(st)) values=signal[i].signal[indecies]-signal[i].signal[indecies-1] tmpSteadyState.append(values[-1]/synapticEficacy) #tmpSteadyState.append(max(values)/synapticEficacy) relativeRecovery.append(tmpSteadyState) relativeRecovery=numpy.array(relativeRecovery) return revoceryTimes, relativeRecovery
def simulate_voltage_ipsp(I_vec): simTime = 700. # ms spikes_at = numpy.arange(500., len(I_vec) * simTime, simTime) # ms voltage = [] # mV ipsp_weak = [] # mV ipsp_strong = [] # mV my_nest.ResetKernel() model_list = models() my_nest.MyLoadModels(model_list, NEURON_MODELS) my_nest.MyLoadModels(model_list, SYNAPES_MODELS) SNR = MyGroup(NEURON_MODELS[0], len(SYNAPES_MODELS), mm=True, mm_dt=0.1) SG = my_nest.Create('spike_generator', params={'spike_times': spikes_at}) for i in range(len(SYNAPES_MODELS)): my_nest.Connect(SG, [SNR[i]], model=SYNAPES_MODELS[i]) simTimeTot = 0 for I_e in I_vec: my_nest.SetStatus(SNR[:], params={'I_e': float(I_e)}) my_nest.MySimulate(simTime) simTimeTot += simTime SNR.get_signal('v', 'V_m', stop=simTimeTot) # retrieve signal simTimeAcum = 0 for I_e in I_vec: signal = SNR.signals['V_m'].my_time_slice(400 + simTimeAcum, 700 + simTimeAcum) simTimeAcum += simTime clamped_at = signal[1].signal[-1] minV = min(signal[1].signal) maxV = max(signal[1].signal) if abs(minV - clamped_at) < abs(maxV - clamped_at): size_weak = max(signal[1].signal) - clamped_at size_strong = max(signal[2].signal) - clamped_at else: size_weak = min(signal[1].signal) - clamped_at size_strong = min(signal[2].signal) - clamped_at voltage.append(clamped_at) ipsp_weak.append(size_weak) ipsp_strong.append(size_strong) ipsp = numpy.array([ipsp_weak, ipsp_strong]) return voltage, ipsp
def simulate_example_inh_current(I_vec): simTime = 1000. # ms my_nest.ResetKernel() model_list, model_dict = models() my_nest.MyLoadModels(model_list, NEURON_MODELS) df = my_nest.GetDefaults(NEURON_MODELS[0]) n = len(I_vec) STN = MyGroup(NEURON_MODELS[0], n, sd=True, mm=True, mm_dt=1.0, record_from=['V_m', 'u']) I_e0 = my_nest.GetStatus(STN[:])[0]['I_e'] my_nest.SetStatus(STN[:], params={'I_e': I_e0 + I_E + 50}) # Set I_e I_e = my_nest.GetStatus(STN.ids, 'I_e')[0] scg = my_nest.Create('step_current_generator', n=n) rec = my_nest.GetStatus(STN[:])[0]['receptor_types'] for source, target, I in zip(scg, STN[:], I_vec): my_nest.SetStatus([source], { 'amplitude_times': [280., 700.], 'amplitude_values': [float(I), 0.] }) my_nest.Connect([source], [target], params={'receptor_type': rec['CURR']}) my_nest.MySimulate(simTime) STN.get_signal('v', 'V_m', stop=simTime) # retrieve signal STN.get_signal('s') # retrieve signal STN.signals['V_m'].my_set_spike_peak(21, spkSignal=STN.signals['spikes']) e = my_nest.GetStatus(STN.mm)[0]['events'] # get events #pylab.plot(e['u']) #pylab.show() meanRate = round(STN.signals['spikes'].mean_rate(0, 500), 1) s = '\n' s = s + 'Example inhibitory current:\n' s = s + ' %s %5s %3s %s %5s %3s \n' % ('Mean rate:', meanRate, 'Hz', 'I_e', I_e, 'pA') s = s + 'Steps:\n' s = s + ' %5s %3s \n' % (I_vec, 'pA') infoString = s return STN, infoString
def simulate_ahp(I_vec): simTime = 3000. # ms my_nest.ResetKernel() model_list, model_dict = models() my_nest.MyLoadModels(model_list, NEURON_MODELS) n = len(I_vec) STN = MyGroup(NEURON_MODELS[0], n, sd=True, mm=True, mm_dt=1.0) I_e0 = my_nest.GetStatus(STN[:])[0]['I_e'] #my_nest.SetStatus(STN[:], params={'I_e':-10.}) # Set I_e my_nest.SetStatus(STN[:], params={'I_e': 1.0}) # Set I_e I_e = my_nest.GetStatus(STN.ids, 'I_e')[0] scg = my_nest.Create('step_current_generator', n=n) rec = my_nest.GetStatus(STN[:])[0]['receptor_types'] for source, target, I in zip(scg, STN[:], I_vec): my_nest.SetStatus([source], { 'amplitude_times': [500., 1000.], 'amplitude_values': [float(I), 0.] }) my_nest.Connect([source], [target], params={'receptor_type': rec['CURR']}) my_nest.MySimulate(simTime) STN.get_signal('s') # retrieve signal STN.signals['spikes'] = STN.signals['spikes'].time_slice(700, 2000) delays = [] for i, curr in enumerate(I_vec): delays.append( max( numpy.diff( STN.signals['spikes'].spiketrains[i + 1.0].spike_times))) meanRate = round(STN.signals['spikes'].mean_rate(0, 500), 1) s = '\n' s = s + 'Example inhibitory current:\n' s = s + ' %s %5s %3s %s %5s %3s \n' % ('Mean rate:', meanRate, 'Hz', 'I_e', I_e, 'pA') s = s + 'Steps:\n' s = s + ' %5s %3s \n' % (I_vec, 'pA') infoString = s return I_vec, delays
def simulate_response_example_clamped_silent(I_e): ''' Response when SNR is clamped to a voltage for Ref 2 and Ref 1 synapse ''' spike_at = 500. # ms simTime = 700. # ms my_nest.ResetKernel() model_list, model_dict = models() my_nest.MyLoadModels(model_list, NEURON_MODELS) my_nest.MyLoadModels(model_list, SYNAPSE_MODELS) SNR = MyGroup(NEURON_MODELS[0], len(SYNAPSE_MODELS), mm=True, mm_dt=0.1, params={'I_e': I_e}) SG = my_nest.Create('spike_generator', params={'spike_times': [spike_at]}) for i in range(len(SYNAPSE_MODELS)): my_nest.Connect(SG, [SNR[i]], model=SYNAPSE_MODELS[i]) my_nest.MySimulate(simTime) SNR.get_signal('v', 'V_m', stop=simTime) # retrieve signal SNR.signals['V_m'] = SNR.signals['V_m'].my_time_slice(400, 700) clamped_at = SNR.signals['V_m'][1].signal[-1] size_MSN_weak = min(SNR.signals['V_m'][1].signal) - clamped_at size_MSN_strong = min(SNR.signals['V_m'][2].signal) - clamped_at size_GPE_ref = min(SNR.signals['V_m'][3].signal) - clamped_at s = '' s = s + ' %s %5s %3s \n' % ('Clamped at:', str(round(clamped_at, 1)), 'mV') s = s + ' %s %5s %3s \n' % (r'$\delta_w^{MSN}$', str(round(size_MSN_weak, 1)), 'mV') s = s + ' %s %5s %3s \n' % (r'$\delta_s^{MSN}$', str(round(size_MSN_strong, 1)), 'mV') s = s + ' %s %5s %3s \n' % (r'$\delta_s^{MSN}$', str(round( size_GPE_ref, 1)), 'mV') infoString = s return SNR, infoString
def simulate_example_rebound_spike(I_vec): simTime = 5000. # ms my_nest.ResetKernel() model_list, model_dict=models() my_nest.MyLoadModels( model_list, NEURON_MODELS ) n=len(I_vec) GPE = MyGroup( NEURON_MODELS[0], n, sd=True, mm=True, mm_dt = 1.0 ) I_e0=my_nest.GetStatus(GPE[:])[0]['I_e'] #my_nest.SetStatus(GPE[:], params={'I_e':-10.}) # Set I_e my_nest.SetStatus(GPE[:], params={'I_e':-10.}) # Set I_e I_e = my_nest.GetStatus(GPE.ids,'I_e')[0] scg = my_nest.Create( 'step_current_generator',n=n ) rec=my_nest.GetStatus(GPE[:])[0]['receptor_types'] for source, target, I in zip(scg, GPE[:], I_vec): my_nest.SetStatus([source], {'amplitude_times':[500.,700.], 'amplitude_values':[float(I),0.]}) my_nest.Connect( [source], [target], params = { 'receptor_type' : rec['CURR'] } ) my_nest.MySimulate(simTime) GPE.get_signal( 'v','V_m', stop=simTime ) # retrieve signal GPE.get_signal( 's') # retrieve signal GPE.signals['V_m'].my_set_spike_peak( 21, spkSignal= GPE.signals['spikes'] ) meanRate=round(GPE.signals['spikes'].mean_rate(0,500),1) s='\n' s =s + 'Example inhibitory current:\n' s = s + ' %s %5s %3s %s %5s %3s \n' % ( 'Mean rate:', meanRate, 'Hz', 'I_e', I_e,'pA' ) s = s + 'Steps:\n' s = s + ' %5s %3s \n' % ( I_vec, 'pA' ) infoString=s return GPE, infoString
my_nest.ResetKernel() model_list = models() # Get model list for model in model_list: my_nest.CopyModel(model[0], model[1], model[2]) # Create models neuron_model = 'MSN_izh' MSN = MyGroup(neuron_model, 3, mm_dt=0.1) #! Spike train experiment 1. Train of 8 spikes at 20 Hz and the recovery spike #! at 550 ms as in Planert 2009 spike_times = range(10, 430, 50) spike_times.extend([430 + 550]) # input sgs = my_nest.Create('spike_generator', params={'spike_times': [float(sp) for sp in spike_times]}) syn_model = 'MSN_MSN_gaba_s' my_nest.Connect(sgs, [MSN[0]], model=syn_model) # connect MSNs T = 2000 # simulation time my_nest.Simulate(T) # simulate MSN.get_signal('v', 'V_m') pylab.close('all') # display # Create figure where figsize(width,height) and figure dimenstions window # width = figsize(width) x dpi and window hight = figsize(hight) x dpi plot_settings.set_mode(mode='dynamic', w=700.0, h=400.0) font_size_text = 10 fig = pylab.figure(facecolor='w') pylab.suptitle('MSN to MSN')
def simulate_recovery(revoceryTimes, load=True): # Path were raw data is saved. For example the spike trains. save_result_at=OUTPUT_PATH+'/simulate_recovery.pkl' save_header_at=OUTPUT_PATH+'/simulate_recovery_header' relativeRecovery=[] n=len(revoceryTimes) if not load: for syn in SYNAPSE_MODELS: my_nest.ResetKernel() model_list, model_dict=models() my_nest.MyLoadModels( model_list, NEURON_MODELS ) my_nest.MyLoadModels( model_list, [syn]) ss=my_nest.GetDefaults(syn) synapticEficacy = ss['weight']*ss['U'] SNR = MyGroup( NEURON_MODELS[0], n, mm=True, mm_dt = .1, params={'I_e':-150.}, record_from=['g_AMPA']) tSim=10000 spikeTimes=[] for rt in revoceryTimes: #spikeTimes.append(numpy.array([1.,11.,21.,31.,41.,41+rt])) # Choosen so that it starts at a pairpulse ration of 0.2 spikeTimes.append(numpy.array([1.,11.,21.,31.,41., 51.,61.,71.,81.,91., 101.,111.,121.,131.,141., 151.,161.,171.,181.,191., 191+rt])) for target, st in zip(SNR, spikeTimes ) : source = my_nest.Create('spike_generator', params={'spike_times':st} ) my_nest.SetDefaults(syn, params={'delay':1.}) my_nest.Connect(source, [target], model=syn) my_nest.MySimulate(tSim) SNR.get_signal( 'g','g_AMPA', stop=tSim ) # retrieve signal signal=SNR.signals['g_AMPA'] tmpSteadyState=[] for i, st in enumerate(spikeTimes, start=1): if SNR.mm_dt==0.1: indecies=numpy.int64(numpy.ceil(st*10))+9 elif SNR.mm_dt==1.: indecies=numpy.int64(numpy.ceil(st)) values=signal[i].signal[indecies]-signal[i].signal[indecies-1] tmpSteadyState.append(values[-1]/synapticEficacy) #tmpSteadyState.append(max(values)/synapticEficacy) relativeRecovery.append(tmpSteadyState) relativeRecovery=numpy.array(relativeRecovery) header=HEADER_SIMULATION_SETUP misc.text_save(header, save_header_at) misc.pickle_save([revoceryTimes, relativeRecovery], save_result_at) elif load: revoceryTimes, relativeRecovery=misc.pickle_load(save_result_at) return revoceryTimes, relativeRecovery
def simulate_example_irregular_firing(I_vec=[0]): simTime = 2000. # ms my_nest.ResetKernel() model_list, model_dict=models() my_nest.MyLoadModels( model_list, NEURON_MODELS ) n=len(I_vec) GPE = MyGroup( NEURON_MODELS[0], n, sd=True, mm=True, mm_dt = 1.0 ) I_e=I_vec[0] my_nest.SetStatus(GPE[:], params={'I_e':I_e}) # Set I_e scg = my_nest.Create( 'step_current_generator',n=n ) noise=my_nest.Create('noise_generator', params={'mean':0.,'std':10.}) rec=my_nest.GetStatus(GPE[:])[0]['receptor_types'] for source, target, I in zip(scg, GPE[:], I_vec): #I=5. my_nest.SetStatus([source], {'amplitude_times':[1., simTime], 'amplitude_values':[-5.,float(I)]}) my_nest.Connect( [source], [target], params = { 'receptor_type' : rec['CURR'] } ) my_nest.Connect( noise, [target], params = { 'receptor_type' : rec['CURR'] } ) my_nest.MySimulate(simTime) GPE.get_signal( 'v','V_m', stop=simTime ) # retrieve signal GPE.get_signal( 's') # retrieve signal GPE.signals['V_m'].my_set_spike_peak( 15, spkSignal= GPE.signals['spikes'] ) #a=GPE.signals['V_m'].analog_signals[1].signal #pylab.plot(a) # #a=a[500:] # pylab.subplot(211).plot(a, 'r') #pylab.show() # # a=a # a=a-numpy.mean(a) # # numpy.savetxt("foo.csv", a, delimiter=",") # # ff=numpy.abs(numpy.fft.fft(a)) # #pylab.plot(ff) # c=numpy.correlate(a, a, mode='full') # pylab.subplot(212).plot(c) # pylab.show() meanRate=round(GPE.signals['spikes'].mean_rate(0,500),1) s='\n' s =s + 'Example inhibitory current:\n' s = s + ' %s %5s %3s %s %5s %3s \n' % ( 'Mean rate:', meanRate, 'Hz', 'I_e', I_e,'pA' ) s = s + 'Steps:\n' s = s + ' %5s %3s \n' % ( I_vec, 'pA' ) infoString=s return GPE, infoString
def simulate_network_poisson(params_msn_d1, params_msn_d2, params_stn, synapse_models, sim_time, seed, I_e_add, threads=1, start_rec=0, model_params={}, params_in={}, p_weights=False, p_conn=False, p_I_e=False): ''' Assume that the background MSN are static weak, then can use poisson process for them, params_msn_d1 - dictionary with timing and burst freq setup for msn {'base_rates':0.1, 'base_times':[1], 'mod_rates': 20, 'mod_times':[1,200], 'mod_units':list() 'n_tot':500, n_mod=20} params_msn_d2 - dictionary with timing and burst freq setup for gpe params_stn - dictionary {'rate':50} same as params_msn neuron_model - string, the neuron model to use synapse_models - dict, {'MSN':'...', 'GPE':,'...', 'STN':'...'} sim_time - simulation time seed - seed for random generator I_e_add - diabled start_rec - start recording from model_params - general model paramters ''' params = { 'conns': { 'MSN_D1_SNR': { 'syn': synapse_models[0] }, 'GPE_SNR': { 'syn': synapse_models[1] } } } my_nest.ResetKernel(threads=8) numpy.random.seed(seed) params = misc.dict_merge(model_params, params) params = misc.dict_merge({'neurons': {'GPE': {'paused': 0}}}, params) model_list, model_dict = models({}, p_weights) layer_list, connect_list = network(model_dict, params, p_conn) dic_p_I_e = {'SNR': 1., 'GPE': 1., 'STN': 1.} if p_I_e is not False: dic_p_I_e['SNR'] *= p_I_e[0] dic_p_I_e['GPE'] *= p_I_e[1] dic_p_I_e['STN'] *= p_I_e[2] # Create neurons and synapses layer_dic = {} for name, model, props in layer_list: # Update input current my_nest.MyLoadModels(model_dict, [model[1]]) if name in I_IN_VIVO.keys(): I_in_vitro = my_nest.GetDefaults(model[1])['I_e'] I_e = I_in_vitro + I_IN_VIVO[name] my_nest.SetDefaults(model[1], {'I_e': I_e * dic_p_I_e[name]}) #! Create layer, retrieve neurons ids per elements and p if model[0] == 'spike_generator': layer = MyLayerPoissonInput(layer_props=props, sd=True, sd_params={ 'start': start_rec, 'stop': sim_time }) elif model[0] == 'poisson_generator': layer = MyPoissonInput(model[0], props['columns'], sd=True, sd_params={ 'start': start_rec, 'stop': sim_time }) else: layer = MyLayerGroup(layer_props=props, sd=True, mm=False, mm_dt=0.1, sd_params={ 'start': start_rec, 'stop': sim_time }) for iter, id in enumerate(layer[:]): if name == 'GPE' and params_msn_d2[ 'n_mod'] and iter < params['neurons']['GPE']['paused']: scg = my_nest.Create('step_current_generator', n=1) rec = my_nest.GetStatus([id])[0]['receptor_types'] my_nest.SetStatus( scg, { 'amplitude_times': params_msn_d2['mod_times'], 'amplitude_values': [0., -300., 0.] }) my_nest.Connect(scg, [id], params={'receptor_type': rec['CURR']}) I_e = my_nest.GetDefaults(model[1])['I_e'] if I_E_VARIATION[name]: I = numpy.random.normal( I_e, I_E_VARIATION[name]) #I_E_VARIATION[name]) else: I = I_e my_nest.SetStatus([id], {'I_e': I}) layer_dic[name] = layer # Connect populations for conn in connect_list: print[conn[2]['synapse_model']] if not conn[2]['synapse_model'] in nest.Models(): my_nest.MyLoadModels(model_dict, [conn[2]['synapse_model']]) if layer_dic[conn[0]].model == 'poisson_generator': my_nest.Connect(layer_dic[conn[0]].ids, layer_dic[conn[1]].ids, model=conn[2]['synapse_model']) else: name = conn[0] + '_' + conn[1] + '_' + conn[3] tp.ConnectLayers(layer_dic[conn[0]].layer_id, layer_dic[conn[1]].layer_id, conn[2]) layer_dic[conn[1]].add_connection(source=layer_dic[conn[0]], type=conn[3], props=conn[2]) # Sort MSN D2 such that the closest to center is first in ids list. # Do this to we can get focused inhibition in GPe if params_msn_d2['focus']: MSN_D2_idx = layer_dic['MSN_D2'].sort_ids() else: MSN_D2_idx = range(len(numpy.array(layer_dic['MSN_D2'].ids))) n_mod_msn_d1 = params_msn_d1['n_mod'] n_mod_msn_d2 = params_msn_d2['n_mod'] MSN_D1_ids = layer_dic['MSN_D1'].ids MSN_D2_ids = layer_dic['MSN_D2'].ids MSN_D1_mod, MSN_D2_mod = [], [] if params_msn_d1['n_mod']: MSN_D1_mod = MSN_D1_ids[0:n_mod_msn_d1] if params_msn_d2['n_mod']: MSN_D2_mod = MSN_D2_ids[0:n_mod_msn_d2 * params_msn_d2['skip']:params_msn_d2['skip']] MSN_D1_base = list(set(MSN_D1_ids).difference(MSN_D1_mod)) MSN_D2_base = list(set(MSN_D2_ids).difference(MSN_D2_mod)) layer_dic['MSN_D1'].set_spike_times(params_msn_d1['base_rates'], params_msn_d1['base_times'], sim_time, ids=MSN_D1_base) layer_dic['MSN_D2'].set_spike_times(params_msn_d2['base_rates'], params_msn_d2['base_times'], sim_time, ids=MSN_D2_base) if params_msn_d1['n_mod']: layer_dic['MSN_D1'].set_spike_times(params_msn_d1['mod_rates'], params_msn_d1['mod_times'], sim_time) if params_msn_d2['n_mod']: layer_dic['MSN_D2'].set_spike_times(params_msn_d2['mod_rates'], params_msn_d2['mod_times'], sim_time, ids=MSN_D2_mod) # If background poisson are use if params_msn_d1['bg_rate']: layer_dic['MSN_D1_bg'].set_spike_times(params_msn_d1['bg_rate'], [1.], sim_time) if params_msn_d2['bg_rate']: layer_dic['MSN_D2_bg'].set_spike_times(params_msn_d2['bg_rate'], [1.], sim_time) STN_CTX_input_base = my_nest.Create('poisson_generator', params={ 'rate': BASE_RATE_CTX_STN, 'start': 0., 'stop': sim_time }) my_nest.MyLoadModels(model_dict, ['CTX_STN_ampa_s']) if 'STN' in layer_dic.keys(): my_nest.DivergentConnect(STN_CTX_input_base, layer_dic['STN'].ids, model='CTX_STN_ampa_s') if params_stn['mod'] and 'STN' in layer_dic.keys(): STN_CTX_input_mod = my_nest.Create('poisson_generator', params={ 'rate': params_stn['mod_rate'], 'start': params_stn['mod_times'][0], 'stop': params_stn['mod_times'][1] }) my_nest.DivergentConnect(STN_CTX_input_mod, layer_dic['STN'].ids, model='CTX_STN_ampa_s') my_nest.MySimulate(sim_time) if params_msn_d1['n_mod']: layer_dic['MSN_D1'].id_mod = MSN_D1_mod if params_msn_d2['n_mod']: layer_dic['MSN_D2'].id_mod = MSN_D2_mod if 'MSN_D1' in layer_dic.keys(): layer_dic['MSN_D1'].get_signal('s', start=start_rec, stop=sim_time) if 'MSN_D2' in layer_dic.keys(): layer_dic['MSN_D2'].get_signal('s', start=start_rec, stop=sim_time) if 'GPE' in layer_dic.keys(): layer_dic['GPE'].get_signal('s', start=start_rec, stop=sim_time) if 'SNR' in layer_dic.keys(): layer_dic['SNR'].get_signal('s', start=start_rec, stop=sim_time) if 'STN' in layer_dic.keys(): layer_dic['STN'].get_signal('s', start=start_rec, stop=sim_time) return layer_dic
def simulate_selection_vs_neurons(selRateInterval=[0.0, 500.0], hz=20): sname_nb = hz nGPE = 500 nExp = 5 if hz > 7: nMaxSelected = 60 else: nMaxSelected = 100 baseRate = 0.1 selectionRate = hz I_e = -5. simTime = 3500. model_list = models() selectionTime = 3000. selectionOnset = 500. expParams = [] expIntervals = [] iSNR = 0 for syn in SYNAPSE_MODELS: for iSel in range(nMaxSelected): expIntervals.append([iSNR, iSNR + nExp]) for iExp in range(nExp): expParams.append((syn, iSel, iExp, iSNR)) iSNR += 1 synIntervals = [] iSNR = 0 for syn in SYNAPSE_MODELS: synIntervals.append([iSNR, iSNR + nMaxSelected]) iSNR += nMaxSelected my_nest.ResetKernel() my_nest.MyLoadModels(model_list, NEURONMODELS) my_nest.MyLoadModels(model_list, SYNAPSE_MODELS) SNR = MyGroup(NEURONMODELS[0], n=len(expParams), params={'I_e': I_e}, mm_dt=.1, record_from=[''], spath=SPATH, sname_nb=sname_nb) sourceBack = [] sourceSel = [] for iExp in range(nExp): # Background tmpSourceBack = [] for iGPE in range(nGPE - 1): spikeTimes = misc.inh_poisson_spikes([baseRate], [1], t_stop=simTime, n_rep=nExp, seed=iGPE + 10 * iExp) if any(spikeTimes): tmpSourceBack.extend( my_nest.Create('spike_generator', params={'spike_times': spikeTimes})) sourceBack.append(tmpSourceBack) if not LOAD: for syn, iSel, iExp, iSNR in expParams: print 'Connect SNR ' + str(SNR[iSNR]) + ' ' + syn target = SNR[iSNR] my_nest.ConvergentConnect(sourceBack[iExp][0:nGPE - iSel], [target], model=syn) my_nest.ConvergentConnect(sourceSel[iExp][0:iSel + 1], [target], model=syn) my_nest.MySimulate(simTime) SNR.save_signal('s') SNR.get_signal('s') # retrieve signal #SNR.get_signal( 'v','V_m' ) # retrieve signal #SNR.signals['V_m'].plot() #SNR.signals['spikes'].raster_plot() #pylab.show() if LOAD: SNR.load_signal('s') #SNR.get_signal( 'v','V_m', stop=simTime ) # retrieve signal #SNR.signals['V_m'].plot(id_list=[5]) #SNR.['spikes'].raster_plot() #pylab.show() t1 = selRateInterval[0] t2 = selRateInterval[1] tmpMeanRates1 = [] tmpMeanRates2 = [] tmpMeanRates3 = [] tmpMeanRates4 = [] tmpMeanRates1 = SNR.signals['spikes'].mean_rates(selectionOnset + t1, selectionOnset + t2) for interval in expIntervals: tmpMeanRates3.append( numpy.mean(tmpMeanRates1[interval[0]:interval[1]], axis=0)) for interval in synIntervals: tmpMeanRates4.append(tmpMeanRates3[interval[0]:interval[1]]) meanRates = numpy.array(tmpMeanRates4) nbNeurons = numpy.arange(1, nMaxSelected + 1, 1) s = '\n' s = s + ' %s %5s %3s \n' % ('N GPEs:', str(nGPE), '#') s = s + ' %s %5s %3s \n' % ('N experiments:', str(nExp), '#') s = s + ' %s %5s %3s \n' % ('Base rate:', str(baseRate), 'Hz') s = s + ' %s %5s %3s \n' % ('Selection rate:', str(selectionRate), 'Hz') s = s + ' %s %5s %3s \n' % ('Selection time:', str(selectionTime), 'ms') s = s + ' %s %5s %3s \n' % ('I_e:', str(I_e), 'pA') infoString = s return nbNeurons, meanRates, infoString
#! Train at 3, 10, 50 and 100 Hz and then a recovery spike at keys = ['10 Hz', '50 hz', '100 hz'] # stimulation frequencies nb_spikes = [50., 50., 50.] # number of spikes for each stimulation shift = 50. # first spike spikes = {} # store spike time lists sg = {} # store spike generators rec1, rec2 = 500, 1500 for key, nb in zip(keys, nb_spikes): hz = float(key.split()[0]) spikes[key] = numpy.linspace(shift, shift + nb * 1000 / hz, nb + 1) # create frequency train rec_spk = numpy.array([rec1, rec2]) + max(spikes[key]) spikes[key] = numpy.append(spikes[key], rec_spk) sg[key] = my_nest.Create('spike_generator', params={'spike_times': spikes[key]}) #! Connections #! =========== for i, key in enumerate(keys): my_nest.Connect(sg[key], [SNR.ids[i]], params={'weight': 1.}, model=syn) #! Simulate #! ======== T = int(spikes[keys[0]][-1] + 2000) # simulation time my_nest.Simulate(T) # simulate #! Plot #! ==== SNR.get_signal('v', 'g_GABAA_1', stop=T) # retrieve signal
def simulate_example_msn_snr(): nFun=0 # Function number nSim=0 # Simulation number within function rates=numpy.array([.1,.1]) times=numpy.array([0.,25000.]) nMSN =500 simTime=100000. I_e=0. my_nest.ResetKernel() model_list=models() my_nest.MyLoadModels( model_list, neuronModels ) my_nest.MyLoadModels( model_list, synapseModels ) MSN = MyGroup( 'spike_generator', nMSN, mm_dt=1.0, mm=False, sd=False, spath=spath, sname_nb=str(nFun)+str(nSim)) SNR = MyGroup( neuronModels[0], n=len(synapseModels), params={'I_e':I_e}, sd=True, mm_dt = .1, mm=False, spath=spath, sname_nb=str(nFun)+str(nSim) ) nSim+=1 spikeTimes=[] for i in range(nMSN): spikes=misc.inh_poisson_spikes( rates, times, t_stop=simTime, n_rep=1, seed=i ) my_nest.SetStatus([MSN[i]], params={ 'spike_times':spikes } ) for spk in spikes: spikeTimes.append((i,spk)) # add spike list for MSN to MSN spike list MSN.signals['spikes'] = my_signals.MySpikeList(spikeTimes, MSN.ids) MSN.save_signal( 's') noise=my_nest.Create('noise_generator', params={'std':100.}) my_nest.Connect(noise,[SNR[0]],params={'receptor_type':5}) my_nest.Connect(noise,[SNR[1]],params={'receptor_type':5}) my_nest.Connect(noise,[SNR[2]],params={'receptor_type':5}) for i, syn in enumerate(synapseModels): my_nest.ConvergentConnect(MSN[:],[SNR[i]], model=syn) my_nest.MySimulate( simTime ) SNR.get_signal( 's' ) # retrieve signal SNR_rates=[SNR.signals['spikes'].mean_rates(0,5000), SNR.signals['spikes'].mean_rates(5000, 10000)] for i in range(0, len(SNR_rates)): for j in range(0, len(SNR_rates[0])): SNR_rates[i][j]=int(SNR_rates[i][j]) s='\n' s =s + 'Example plot MSN and SNr:\n' s =s + 'Synapse models:\n' for syn in synapseModels: s = s + ' %s\n' % (syn ) s = s + ' %s %5s %3s \n' % ( 'N MSN:', str ( nMSN ), '#' ) s = s + ' %s %5s %3s \n' % ( 'MSN Rates:', str ( [str(round(r,1)) for r in rates]),'Hz' ) s = s + ' %s %5s %3s \n' % ( '\nSNR Rates 0-5000:\n', str ( SNR_rates [0]) ,'Hz' ) s = s + ' %s %5s %3s \n' % ( '\nSNR Rates 10000-5000:\n', str ( SNR_rates [1]) ,'Hz' ) s = s + ' %s %5s %3s \n' % ( '\nTimes:', str ( times), 'ms' ) s = s + ' %s %5s %3s \n' % ( 'I_e:', str ( I_e ), 'pA' ) infoString=s return MSN, SNR, infoString
def simulate_network_test(params_msn_d1, params_msn_d2, params_stn, synapse_models, sim_time, seed, I_e_add, threads=1, start_rec=0, model_params={}, params_in={}, dis_conn_GPE_STN=False): ''' params_msn_d1 - dictionary with timing and burst freq setup for msn {'base_rates':0.1, 'base_times':[1], 'mod_rates': 20, 'mod_times':[1,200], 'mod_units':list() 'n_tot':500, n_mod=20} params_msn_d2 - dictionary with timing and burst freq setup for gpe params_stn - dictionary {'rate':50} same as params_msn neuron_model - string, the neuron model to use synapse_models - dict, {'MSN':'...', 'GPE':,'...', 'STN':'...'} sim_time - simulation time seed - seed for random generator I_e_add - diabled start_rec - start recording from model_params - general model paramters ''' my_nest.ResetKernel(threads=8) numpy.random.seed(seed) params = { 'conns': { 'MSN_D1_SNR': { 'syn': synapse_models[0] }, 'GPE_SNR': { 'syn': synapse_models[1] } } } params = misc.dict_merge(model_params, params) params = misc.dict_merge({'neurons': {'GPE': {'paused': 0}}}, params) model_list, model_dict = models(params_in) layer_list, connect_list = network(model_dict, params) # Create neurons and synapses layer_dic = {} for name, model, props in layer_list: # Update input current my_nest.MyLoadModels(model_dict, [model[1]]) if name in I_IN_VIVO.keys(): I_e = my_nest.GetDefaults(model[1])['I_e'] + I_IN_VIVO[name] my_nest.SetDefaults(model[1], {'I_e': I_e}) #! Create layer, retrieve neurons ids per elements and p if model[0] == 'spike_generator': layer = MyLayerPoissonInput(layer_props=props, sd=True, sd_params={ 'start': start_rec, 'stop': sim_time }) else: layer = MyLayerGroup(layer_props=props, sd=True, mm=False, mm_dt=0.1, sd_params={ 'start': start_rec, 'stop': sim_time }) for iter, id in enumerate(layer[:]): if name == 'GPE' and params_msn_d2[ 'n_mod'] and iter < params['neurons']['GPE']['paused']: scg = my_nest.Create('step_current_generator', n=1) rec = my_nest.GetStatus([id])[0]['receptor_types'] my_nest.SetStatus( scg, { 'amplitude_times': params_msn_d2['mod_times'], 'amplitude_values': [0., -300., 0.] }) my_nest.Connect(scg, [id], params={'receptor_type': rec['CURR']}) I_e = my_nest.GetDefaults(model[1])['I_e'] if I_E_VARIATION[name]: I = numpy.random.normal(I_e, I_E_VARIATION[name]) else: I = I_e #I=I_e my_nest.SetStatus([id], {'I_e': I}) layer_dic[name] = layer mm = nest.Create('multimeter', 1) recodables = ['V_m', 'I', 'g_AMPA', 'g_NMDA', 'g_GABAA_1', 'g_GABAA_2'] my_nest.SetStatus(mm, {'interval': 0.1, 'record_from': recodables}) my_nest.Connect(mm, [layer_dic['STN'].ids[0]]) # Connect populations for conn in connect_list: name = conn[0] + '_' + conn[1] my_nest.MyLoadModels(model_dict, [conn[2]['synapse_model']]) if dis_conn_GPE_STN == 'GPE' and (name in ['GPE_SNR']): r, syn = 32 * 30.0, 'GPE_SNR_gaba_s_ref' if not syn in my_nest.Models(): my_nest.MyLoadModels(model_dict, [syn]) pg = my_nest.Create('poisson_generator', 1, { 'rate': r, 'start': 1. }) my_nest.DivergentConnect(pg, layer_dic[conn[1]].ids, model=syn) elif dis_conn_GPE_STN == 'STN' and (name in ['STN_SNR']): r, syn = 30 * 10.0, 'STN_SNR_ampa_s' if not syn in my_nest.Models(): my_nest.MyLoadModels(model_dict, [syn]) pg = my_nest.Create('poisson_generator', 1, { 'rate': r, 'start': 1. }) my_nest.DivergentConnect(pg, layer_dic[conn[1]].ids, model=syn) else: name = name + '_' + conn[3] tp.ConnectLayers(layer_dic[conn[0]].layer_id, layer_dic[conn[1]].layer_id, conn[2]) layer_dic[conn[1]].add_connection(source=layer_dic[conn[0]], type=conn[3], props=conn[2]) # Sort MSN D2 such that the closest to center is first in ids list. # Do this to we can get focused inhibition in GPe if params_msn_d2['focus']: MSN_D2_idx = layer_dic['MSN_D2'].sort_ids() else: MSN_D2_idx = range(len(numpy.array(layer_dic['MSN_D2'].ids))) n_mod_msn_d1 = params_msn_d1['n_mod'] n_mod_msn_d2 = params_msn_d2['n_mod'] MSN_D1_ids = layer_dic['MSN_D1'].ids MSN_D2_ids = layer_dic['MSN_D2'].ids MSN_D1_mod, MSN_D2_mod = [], [] if params_msn_d1['n_mod']: MSN_D1_mod = MSN_D1_ids[0:n_mod_msn_d1] if params_msn_d2['n_mod']: MSN_D2_mod = MSN_D2_ids[0:n_mod_msn_d2 * params_msn_d2['skip']:params_msn_d2['skip']] MSN_D1_base = list(set(MSN_D1_ids).difference(MSN_D1_mod)) MSN_D2_base = list(set(MSN_D2_ids).difference(MSN_D2_mod)) #layer_dic['MSN_D1'].ids[0:n_base_msn_d1] #MSN_D2_ids=numpy.array(layer_dic['MSN_D2'].ids) #MSN_D2_base=MSN_D2_ids#[MSN_D2_idx[0:n_base_msn_d1]] #set().difference(t) layer_dic['MSN_D1'].set_spike_times(params_msn_d1['base_rates'], params_msn_d1['base_times'], sim_time, ids=MSN_D1_base) layer_dic['MSN_D2'].set_spike_times(params_msn_d2['base_rates'], params_msn_d2['base_times'], sim_time, ids=MSN_D2_base) if params_msn_d1['n_mod']: layer_dic['MSN_D1'].set_spike_times(params_msn_d1['mod_rates'], params_msn_d1['mod_times'], sim_time, ids=MSN_D1_mod) if params_msn_d2['n_mod']: layer_dic['MSN_D2'].set_spike_times(params_msn_d2['mod_rates'], params_msn_d2['mod_times'], sim_time, ids=MSN_D2_mod) STN_CTX_input_base = my_nest.Create('poisson_generator', params={ 'rate': BASE_RATE_CTX_STN, 'start': 0., 'stop': sim_time }) my_nest.MyLoadModels(model_dict, ['CTX_STN_ampa_s']) my_nest.DivergentConnect(STN_CTX_input_base, layer_dic['STN'].ids, model='CTX_STN_ampa_s') if params_stn['mod']: STN_CTX_input_mod = my_nest.Create('poisson_generator', params={ 'rate': params_stn['mod_rate'], 'start': params_stn['mod_times'][0], 'stop': params_stn['mod_times'][1] }) my_nest.DivergentConnect(STN_CTX_input_mod, layer_dic['STN'].ids, model='CTX_STN_ampa_s') #tar=[] #for id in layer_dic['MSN_D1'].ids: # tar.extend(sorted(nest.GetStatus(my_nest.FindConnections([id]),'target'))[:-1]) #pylab.subplot(211).hist(tar, 1500) # # tar=[] # for id in layer_dic['MSN_D2'].ids: # tar.extend(sorted(nest.GetStatus(my_nest.FindConnections([id]),'target'))[1:]) # # pylab.subplot(212).hist(tar, 1500) #pylab.show() # # my_nest.MySimulate(sim_time) if params_msn_d1['n_mod']: layer_dic['MSN_D1'].id_mod = MSN_D1_mod if params_msn_d2['n_mod']: layer_dic['MSN_D2'].id_mod = MSN_D2_mod #layer_dic['MSN_D1'].get_signal( 's', start=start_rec, stop=sim_time ) #layer_dic['MSN_D2'].get_signal( 's', start=start_rec, stop=sim_time ) #layer_dic['GPE'].get_signal( 's', start=start_rec, stop=sim_time ) #layer_dic['SNR'].get_signal( 's', start=start_rec, stop=sim_time ) #layer_dic['STN'].get_signal( 's', start=start_rec, stop=sim_time ) st_mm = my_nest.GetStatus(mm)[0] pylab.plot(st_mm['events']['g_AMPA']) pylab.plot(st_mm['events']['g_GABAA_1']) pylab.plot(st_mm['events']['g_NMDA']) pylab.plot(st_mm['events']['g_GABAA_2']) m_ampa = numpy.mean(st_mm['events']['g_AMPA']) m_gaba = numpy.mean(st_mm['events']['g_GABAA_1']) pylab.title("{0} m_ampa:{1:2.1f} m_gaba:{2:2.1f}".format( my_nest.version(), m_ampa, m_gaba)) pylab.show() return layer_dic
def simulate_steady_state_freq(frequencies, flag='ss', load=True): # Path were raw data is saved. For example the spike trains. save_result_at=OUTPUT_PATH+'/simulate_steady_state_freq.pkl' save_header_at=OUTPUT_PATH+'/simulate_steady_state_freq_header' relativeFacilitation=[] n=len(frequencies) if not load: for syn in SYNAPSE_MODELS: my_nest.ResetKernel() model_list, model_dict=models() my_nest.MyLoadModels( model_list, NEURON_MODELS ) my_nest.MyLoadModels( model_list, [syn]) SNR = MyGroup( NEURON_MODELS[0], n, mm=True, mm_dt = .1, params={'I_e':-150.}, record_from=['g_AMPA'] ) tSim=5*1000/frequencies[0] spikeTimes=[] tmpSteadyState=[] for f in frequencies : isi = 1000./f spikeTimes.append(numpy.arange(1,tSim,isi)) for target, st in zip(SNR, spikeTimes ) : source = my_nest.Create('spike_generator', params={'spike_times':st} ) my_nest.SetDefaults(syn, params={'delay':1.}) my_nest.Connect(source, [target], model=syn) my_nest.MySimulate(tSim) SNR.get_signal( 'g','g_AMPA', stop=tSim ) # retrieve signal signal=SNR.signals['g_AMPA'] for i, st in enumerate(spikeTimes, start=1): if SNR.mm_dt==0.1: indecies=numpy.int64(numpy.ceil(st*10))+9 elif SNR.mm_dt==1.: indecies=numpy.int64(numpy.ceil(st)) values=signal[i].signal[indecies]-signal[i].signal[indecies-1] ss=my_nest.GetDefaults(syn) synapticEficacy = ss['weight']*ss['U'] if flag=='ss': tmpSteadyState.append(values[-1]/synapticEficacy) if flag=='max': tmpSteadyState.append(max(values)/synapticEficacy) relativeFacilitation.append(tmpSteadyState) relativeFacilitation=numpy.array(relativeFacilitation) header=HEADER_SIMULATION_SETUP misc.text_save(header, save_header_at) misc.pickle_save([frequencies, relativeFacilitation], save_result_at) elif load: frequencies, relativeFacilitation=misc.pickle_load(save_result_at) return frequencies, relativeFacilitation
def simulate_network(params_msn_d1, params_msn_d2, params_stn, synapse_models, sim_time, seed, I_e_add, threads=1, start_rec=0, model_params={}): ''' params_msn_d1 - dictionary with timing and burst freq setup for msn {'base_rates':[0.1, 0.1, ..., 0.1], #Size number of actions 'mod_rates': [[20,0,...,0], [0,20,...,0],...[0,0,...,20]] #size number of actions times number of events 'mod_times':[[500,1000],[1500,2000],[9500,10000]] # size number of events 'n_neurons':500} params_msn_d2 - dictionary with timing and burst freq setup for gpe params_stn - dictionary {'rate':50} same as params_msn neuron_model - string, the neuron model to use synapse_models - dict, {'MSN':'...', 'GPE':,'...', 'STN':'...'} sim_time - simulation time seed - seed for random generator I_e_add - diabled start_rec - start recording from model_params - general model paramters ''' I_e_add = {'SNR': 300, 'STN': 0, 'GPE': 30} f = 0.01 #0.01#0.5 I_e_variation = {'GPE': 25 * f, 'SNR': 100 * f, 'STN': 10 * f} my_nest.ResetKernel(threads=8) numpy.random.seed(seed) params = { 'conns': { 'MSN_D1_SNR': { 'syn': synapse_models[0] }, 'GPE_SNR': { 'syn': synapse_models[1] } } } params = misc.dict_merge(model_params, params) model_list, model_dict = models() group_list, group_dict, connect_list, connect_params = network( model_dict, params) print connect_params groups = {} for name, model, setup in group_list: # Update input current my_nest.MyLoadModels(model_dict, [model]) if name in I_e_add.keys(): I_e = my_nest.GetDefaults(model)['I_e'] + I_e_add[name] my_nest.SetDefaults(model, {'I_e': I_e}) groups[name] = [] for action in range(connect_params['misc']['n_actions']): if model in ['MSN_D1_spk_gen', 'MSN_D2_spk_gen']: group = MyPoissonInput(params=setup, sd=True, sd_params={ 'start': start_rec, 'stop': sim_time }) else: group = MyGroup(params=setup, sd=True, mm=False, mm_dt=0.1, sd_params={ 'start': start_rec, 'stop': sim_time }) groups[name].append(group) for action in range(connect_params['misc']['n_actions']): groups['MSN_D1'][action].set_spike_times( list(params_msn_d1['mod_rates'][action]), list(params_msn_d1['mod_times']), sim_time, ids=groups['MSN_D1'][action].ids) groups['MSN_D2'][action].set_spike_times( params_msn_d2['mod_rates'][action], params_msn_d2['mod_times'], sim_time, ids=groups['MSN_D2'][action].ids) # Create neurons and synapses for source, target, props in connect_list: my_nest.MyLoadModels(model_dict, [props['model']]) for action in range(connect_params['misc']['n_actions']): pre = list(groups[source][action].ids) post = list(groups[target][action].ids) my_nest.MyRandomConvergentConnect(pre, post, params=props) STN_CTX_input_base = my_nest.Create('poisson_generator', params={ 'rate': params_stn['rate'], 'start': 0., 'stop': sim_time }) my_nest.MyLoadModels(model_dict, ['CTX_STN_ampa_s']) for action in range(connect_params['misc']['n_actions']): my_nest.DivergentConnect(STN_CTX_input_base, groups['STN'][action].ids, model='CTX_STN_ampa_s') my_nest.MySimulate(sim_time) for action in range(connect_params['misc']['n_actions']): groups['MSN_D1'][action].get_signal('s', start=start_rec, stop=sim_time) groups['MSN_D2'][action].get_signal('s', start=start_rec, stop=sim_time) groups['GPE'][action].get_signal('s', start=start_rec, stop=sim_time) groups['SNR'][action].get_signal('s', start=start_rec, stop=sim_time) groups['STN'][action].get_signal('s', start=start_rec, stop=sim_time) return groups
save_result_at = OUTPUT_PATH + '/simulate.plk' if 0: neuron_list = [] for i, model in enumerate(neuron_models): my_nest.MyLoadModels(model_dict, [model]) I_in_vitro = my_nest.GetDefaults(model)['I_e'] neuron = MyGroup(model, n=n, sd=True, mm_dt=.1, mm=False) for id in neuron.ids: I = numpy.random.normal(I_in_vitro, I_in_vitro * norm_std[i] * mrs[i]) my_nest.SetStatus([id], {'I_e': I}) neuron_list.append(neuron) noise = my_nest.Create('noise_generator', params={ 'mean': 0., 'std': 1. }) rec = my_nest.GetStatus(neuron[:])[0]['receptor_types'] for id in neuron.ids: my_nest.Connect(noise, [id], params={'receptor_type': rec['CURR']}) my_nest.MySimulate(sim_time) mr_list = [] for neuron in neuron_list: neuron.get_signal('s', start=0, stop=sim_time) signal = neuron.signals['spikes'] mr = numpy.mean(signal.spike_histogram(time_bin=1, normalized=True), axis=1)