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_example(I_e):
simTime = 1000. # ms
my_nest.ResetKernel()
model_list, model_dict = models()
my_nest.MyLoadModels(model_list, NEURON_MODELS)
I_e0 = my_nest.GetDefaults(NEURON_MODELS[0])['I_e']
SNR = MyGroup(NEURON_MODELS[0],
1,
sd=True,
mm=True,
mm_dt=1.,
params={'I_e': I_e + I_e0})
my_nest.MySimulate(simTime)
SNR.get_signal('v', 'V_m', stop=simTime) # retrieve signal
SNR.get_signal('s') # retrieve signal
meanRate = round(SNR.signals['spikes'].mean_rate(0, 1000), 1)
print SNR.signals['spikes'].isi()
SNR.signals['V_m'].my_set_spike_peak(21, spkSignal=SNR.signals['spikes'])
s = '\n'
s = s + 'Example:\n'
s = s + ' %s %5s %3s %s %5s %3s \n' % ('Mean rate:', meanRate, 'Hz', 'I_e',
I_e, 'pA')
infoString = s
return SNR, infoString
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_basa_line_STN(ctx_rate,
gpe_rate,
n_ctx,
n_gpe,
neuron_model,
syn_models,
stn_current,
sim_time=1000,
threads=8,
w_GPE_STN=0):
Params_in = {}
if w_GPE_STN: Params_in['GPE_STN_gaba_s'] = w_GPE_STN
model_list, model_dict = models(Params_in)
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels(model_dict, neuron_model)
my_nest.MyLoadModels(model_dict, syn_models)
SNR_list = [] # List with SNR groups for synapse.
if n_ctx > 0: CTX = MyPoissonInput(n=n_ctx, sd=True)
if n_gpe > 0: GPE = MyPoissonInput(n=n_gpe, sd=False)
if n_ctx > 0:
CTX.set_spike_times(rates=[ctx_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_gpe > 0:
GPE.set_spike_times(rates=[gpe_rate],
times=[1],
t_stop=sim_time,
seed=0)
I_e = my_nest.GetDefaults(neuron_model[0])['I_e'] + stn_current
STN = MyGroup(neuron_model[0],
n=1,
sd=True,
params={'I_e': I_e},
mm_dt=.1,
mm=True)
if n_ctx > 0:
my_nest.Connect(CTX[:], STN[:] * len(CTX[:]), model=syn_models[0])
if n_gpe > 0:
my_nest.Connect(GPE[:], STN[:] * len(GPE[:]), model=syn_models[1])
my_nest.MySimulate(sim_time)
STN.get_signal('s', start=0, stop=sim_time)
STN.get_signal('v', recordable='V_m', start=0, stop=sim_time)
return STN
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_basa_line_SNr(msn_rate, gpe_rate, stn_rate, n_msn, n_gpe, n_stn, neuron_model, snr_current, sim_time=1000, threads=8, stn_syn='STN_SNR_ampa_s'):
SNR_INJECTED_CURRENT=snr_current
SYNAPSE_MODEL_BACKROUND_STN=[stn_syn]
model_list, model_dict=models()
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels( model_dict, neuron_model )
my_nest.MyLoadModels( model_dict, SYNAPSE_MODEL_BACKROUND_MSN)
my_nest.MyLoadModels( model_dict, SYNAPSE_MODEL_BACKROUND_GPE)
my_nest.MyLoadModels( model_dict, SYNAPSE_MODEL_BACKROUND_STN)
SNR_list=[] # List with SNR groups for synapse.
if n_msn>0: MSN_base=MyPoissonInput(n=n_msn, sd=True)
if n_gpe>0: GPE=MyPoissonInput(n=n_gpe, sd=False)
if n_stn>0: STN=MyPoissonInput(n=n_stn, sd=False)
if n_msn>0: MSN_base.set_spike_times(rates=[ msn_rate], times=[1], t_stop=sim_time, seed=0)
if n_gpe>0: GPE.set_spike_times(rates=[gpe_rate], times=[1], t_stop=sim_time, seed=0)
if n_stn>0: STN.set_spike_times(rates=[stn_rate], times=[1], t_stop=sim_time, seed=0)
I_e=my_nest.GetDefaults(neuron_model[0])['I_e']+SNR_INJECTED_CURRENT
SNR=MyGroup( neuron_model[0], n=1, sd=True, params={'I_e':I_e}, mm_dt=.1, mm=True)
if n_msn>0: my_nest.Connect(MSN_base[:], SNR[:]*len(MSN_base[:]), model=SYNAPSE_MODEL_BACKROUND_MSN[0])
if n_gpe>0: my_nest.Connect(GPE[:],SNR[:]*len(GPE[:]), model=SYNAPSE_MODEL_BACKROUND_GPE[0])
if n_stn>0: my_nest.Connect(STN[:],SNR[:]*len(STN[:]), model=SYNAPSE_MODEL_BACKROUND_STN[0])
my_nest.MySimulate(sim_time)
SNR.get_signal( 's', start=0, stop=sim_time )
meanRate=round(SNR.signals['spikes'].mean_rate(1000,sim_time),1)
spk=SNR.signals['spikes'].time_slice(1000,sim_time).raw_data()
CV=numpy.std(numpy.diff(spk[:,0],axis=0))/numpy.mean(numpy.diff(spk[:,0],axis=0))
SNR.get_signal( 'v',recordable='V_m', start=0, stop=sim_time )
SNR.signals['V_m'].my_set_spike_peak( 15, spkSignal= SNR.signals['spikes'] )
pylab.rcParams.update( {'path.simplify':False} )
SNR.signals['V_m'].plot()
pylab.title(str(meanRate)+ 'Hz, CV='+str(CV))
pylab.show()
return
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_basa_line_GPe(msn_rate, stn_rate, gpe_rate, n_msn, n_stn, n_gpe, neuron_model, syn_models, gpe_current, sim_time=1000, threads=8):
model_list, model_dict=models()
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels( model_dict, neuron_model )
my_nest.MyLoadModels( model_dict, syn_models)
SNR_list=[] # List with SNR groups for synapse.
if n_msn>0: MSN=MyPoissonInput(n=n_msn, sd=False)
if n_stn>0: STN=MyPoissonInput(n=n_stn, sd=False)
if n_gpe>0: GPE=MyPoissonInput(n=n_gpe, sd=False)
if n_msn>0: MSN.set_spike_times(rates=[msn_rate], times=[1], t_stop=sim_time, seed=0)
if n_stn>0: STN.set_spike_times(rates=[stn_rate], times=[1], t_stop=sim_time, seed=0)
if n_gpe>0: GPE.set_spike_times(rates=[gpe_rate], times=[1], t_stop=sim_time, seed=0)
I_e=my_nest.GetDefaults(neuron_model[0])['I_e']+gpe_current
GPE_target=MyGroup( neuron_model[0], n=1, sd=True, params={'I_e':I_e},
mm_dt=.1, mm=True)
if n_msn>0: my_nest.Connect(MSN[:], GPE_target[:]*len(MSN[:]), model=syn_models[0])
if n_stn>0: my_nest.Connect(STN[:],GPE_target[:]*len(STN[:]), model=syn_models[1])
if n_gpe>0: my_nest.Connect(GPE[:],GPE_target[:]*len(GPE[:]), model=syn_models[2])
my_nest.MySimulate(sim_time)
GPE_target.get_signal( 's', start=0, stop=sim_time )
meanRate=round(GPE_target.signals['spikes'].mean_rate(1000,sim_time),1)
spk=GPE_target.signals['spikes'].time_slice(1000,sim_time).raw_data()
CV=numpy.std(numpy.diff(spk[:,0],axis=0))/numpy.mean(numpy.diff(spk[:,0],axis=0))
GPE_target.get_signal( 'v',recordable='V_m', start=0, stop=sim_time )
GPE_target.signals['V_m'].my_set_spike_peak( 15, spkSignal= GPE_target.signals['spikes'] )
pylab.rcParams.update( {'path.simplify':False} )
GPE_target.signals['V_m'].plot()
pylab.title(str(meanRate)+ 'Hz, CV='+str(CV))
pylab.show()
return
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(I_e):
simTime = 3000. # ms
my_nest.ResetKernel()
model_list, model_dict = models()
my_nest.MyLoadModels(model_list, NEURON_MODELS)
I_e0 = my_nest.GetDefaults(NEURON_MODELS[0])['I_e']
STN = MyGroup(NEURON_MODELS[0],
1,
sd=True,
mm=True,
mm_dt=1.,
params={'I_e': I_e + I_e0})
'''
scg = my_nest.Create( 'step_current_generator',n=1 )
rec=my_nest.GetStatus(STN[:])[0]['receptor_types']
my_nest.SetStatus(scg, {'amplitude_times':[280.,1700.],
'amplitude_values':[100.0,0.]})
my_nest.Connect( scg, STN.ids,
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
meanRate = round(STN.signals['spikes'].mean_rate(0, 1000), 1)
print STN.signals['spikes'].isi()
STN.signals['V_m'].my_set_spike_peak(21, spkSignal=STN.signals['spikes'])
s = '\n'
s = s + 'Example:\n'
s = s + ' %s %5s %3s %s %5s %3s \n' % ('Mean rate:', meanRate, 'Hz', 'I_e',
I_e, 'pA')
infoString = s
return STN, 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
def simulate_example_GPE_snr():
nFun = 0 # Function number
nSim = 0 # Simulation number within function
rates = numpy.array([20, 30])
times = numpy.array([0., 5000.])
nGPE = 10
simTime = 10000.
I_e = 0.
my_nest.ResetKernel()
model_list = models()
my_nest.MyLoadModels(model_list, neuronModels)
my_nest.MyLoadModels(model_list, synapseModels)
GPE = MyGroup('spike_generator',
nGPE,
mm_dt=1.0,
mm=False,
sd=False,
spath=spath,
siter=str(nFun) + str(nSim))
SNR = MyGroup(neuronModels[0],
n=len(synapseModels),
params={'I_e': I_e},
mm_dt=.1,
mm=False,
spath=spath,
siter=str(nFun) + str(nSim))
nSim += 1
if not LOAD:
spikeTimes = []
for i in range(nGPE):
spikes = misc.inh_poisson_spikes(rates,
times,
t_stop=simTime,
n_rep=1,
seed=i)
my_nest.SetStatus([GPE[i]], params={'spike_times': spikes})
for spk in spikes:
spikeTimes.append((i, spk))
# add spike list for GPE to GPE spike list
GPE.signals['spikes'] = my_signals.MySpikeList(spikeTimes, GPE.ids)
GPE.save_signal('s')
for i, syn in enumerate(synapseModels):
my_nest.ConvergentConnect(GPE[:], [SNR[i]], model=syn)
my_nest.MySimulate(simTime)
SNR.save_signal('s')
SNR.get_signal('s') # retrieve signal
elif LOAD:
GPE.load_signal('s')
SNR.load_signal('s')
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 GPE and SNr:\n'
s = s + 'Synapse models:\n'
for syn in synapseModels:
s = s + ' %s\n' % (syn)
s = s + ' %s %5s %3s \n' % ('N GPE:', str(nGPE), '#')
s = s + ' %s %5s %3s \n' % ('GPE 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 GPE, SNR, infoString
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
#! 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')
ax_list = []
ax_list.append(MyAxes(fig, [.1, .37, .2, .26])) # text box
ax_list.append(MyAxes(fig, [.50, .15, .40, .65])) # voltage trace
ds = my_nest.GetDefaults(syn_model)
sn = my_nest.GetStatus(MSN[:])[0]
def simulate_GPE_vs_SNR_rate():
nFun = 1 # Function number
nSim = 0 # Simulation number within function
GPEmeanRates = numpy.arange(1, 50, 2)
SNRmeanRates = []
nGPE = 10
simTime = 10000.
I_e = 0.
for r in GPEmeanRates:
my_nest.ResetKernel()
model_list = models()
my_nest.MyLoadModels(model_list, neuronModels)
my_nest.MyLoadModels(model_list, synapseModels)
GPE = MyGroup('spike_generator',
nGPE,
mm_dt=1.0,
mm=False,
sd=False,
spath=spath,
siter=str(nFun) + str(nSim))
SNR = MyGroup(neuronModels[0],
n=len(synapseModels),
params={'I_e': I_e},
mm_dt=.1,
mm=False,
spath=spath,
siter=str(nFun) + str(nSim))
nSim += 1
if not LOAD:
spikeTimes = []
for i in range(nGPE):
spikes = misc.inh_poisson_spikes(numpy.array([r]),
numpy.array([0]),
t_stop=simTime,
n_rep=1,
seed=i)
my_nest.SetStatus([GPE[i]], params={'spike_times': spikes})
for spk in spikes:
spikeTimes.append((i, spk))
# add spike list for GPE to GPE spike list
GPE.signals['spikes'] = my_signals.MySpikeList(spikeTimes, GPE.ids)
GPE.save_signal('s')
for i, syn in enumerate(synapseModels):
my_nest.ConvergentConnect(GPE[:], [SNR[i]], model=syn)
my_nest.MySimulate(simTime)
SNR.save_signal('s')
SNR.get_signal('s') # retrieve signal
elif LOAD:
SNR.load_signal('s')
SNRmeanRates.append(SNR.signals['spikes'].mean_rates(0, simTime))
SNRmeanRates = numpy.array(SNRmeanRates).transpose()
GPEmeanRates = numpy.array(GPEmeanRates)
THR = 2.
rateAtThr = ''
for SNRr in SNRmeanRates:
tmp = str(GPEmeanRates[SNRr >= THR][-1])
rateAtThr += ' ' + tmp[0:4]
s = '\n'
s = s + 'GPE vs SNr rate:\n'
s = s + ' %s %5s %3s \n' % ('N GPEs:', str(nGPE), '#')
s = s + ' \n%s \n%5s %3s \n' % ('GPE rates:', str(GPEmeanRates[0]) + '-' +
str(GPEmeanRates[-1]), 'Hz')
s = s + ' %s %5s %3s \n' % ('Threshold SNr:', str(THR), 'Hz')
s = s + ' \n%s \n%5s %3s \n' % ('GPE rate at threshold SNr:',
str(rateAtThr), 'Hz')
s = s + ' \n%s %5s %3s \n' % ('Simulation time:', str(simTime), 'ms')
s = s + ' %s %5s %3s \n' % ('I_e:', str(I_e), 'pA')
infoString = s
return GPEmeanRates, SNRmeanRates, infoString
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
def simulate_example(hz=0, load=True):
global NEURON_MODELS
global SNAME
global SPATH
global SYNAPSE_MODELS
global SEL_ONSET
global I_E
N_EXP = 200
N_GPE = 50
N_SEL = 30 # Number of selected GPE
N_INH = 0 # Number of inhibited GPE
RATE_BASE = 15 # Base rate
RATE_SELE = hz # Selection rate
RATE_INHI = 0
SAVE_AT = SPATH + '/' + NEURON_MODELS[0] + '-example.pkl'
SEL_TIME = 20.
sim_time = SEL_TIME + SEL_ONSET + 800.
SNAME_NB = hz + 1000
EXPERIMENTS = range(N_EXP)
MODEL_LIST = models()
my_nest.ResetKernel()
my_nest.MyLoadModels(MODEL_LIST, NEURON_MODELS)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS)
GPE_list = [] # GPE input for each experiment
for i_exp in EXPERIMENTS:
GPE = MyPoissonInput(n=N_GPE,
sd=True,
spath=SPATH,
sname_nb=SNAME_NB + i_exp)
GPE_list.append(GPE)
SNR_list = [] # SNR groups for each synapse
for i_syn, syn in enumerate(SYNAPSE_MODELS):
SNR = MyGroup(NEURON_MODELS[0],
n=N_EXP,
params={'I_e': I_E},
sd=True,
mm=False,
mm_dt=.1,
record_from=[''],
spath=SPATH,
sname_nb=SNAME_NB + i_syn)
SNR_list.append(SNR)
if not load:
for i_exp in EXPERIMENTS:
GPE = GPE_list[i_exp]
# Set spike times
# Base rate
for id in GPE[:]:
GPE.set_spike_times(id=id,
rates=[RATE_BASE],
times=[1],
t_stop=sim_time)
# Selection
for id in GPE[N_GPE - N_SEL:N_GPE + 1]:
rates = [RATE_BASE, RATE_SELE, RATE_BASE]
times = [1, SEL_ONSET, SEL_TIME + SEL_ONSET]
t_stop = sim_time
GPE.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop)
# Inhibition
for id in GPE[N_GPE - N_SEL - N_INH:N_GPE + 1 - N_SEL]:
rates = [RATE_BASE, RATE_INHI, RATE_BASE]
times = [1, SEL_ONSET, SEL_TIME + SEL_ONSET]
t_stop = sim_time
GPE.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop)
# Connect
for i_syn, syn in enumerate(SYNAPSE_MODELS):
target = SNR_list[i_syn][i_exp]
my_nest.ConvergentConnect(GPE[:], [target], model=syn)
my_nest.MySimulate(sim_time)
for GPE in GPE_list:
GPE.get_signal('s')
for SNR in SNR_list:
SNR.get_signal('s')
misc.pickle_save([GPE_list, SNR_list], SAVE_AT)
if load:
GPE_list, SNR_list = misc.pickle_load(SAVE_AT)
pre_ref = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
pre_dyn = str(SNR_list[2].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
s = '\n'
s = s + 'Example:\n'
s = s + ' %s %5s %3s \n' % ('N experiments:', str(len(EXPERIMENTS)), '#')
s = s + ' %s %5s %3s \n' % ('Base rate:', str(RATE_BASE), 'Hz')
s = s + ' %s %5s %3s \n' % ('Selection rate:', str(RATE_SELE), 'Hz')
s = s + ' %s %5s %3s \n' % ('Selection time:', str(SEL_TIME), 'ms')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref[0:4], 'Hz')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Dyn:', pre_dyn[0:4], 'Hz')
return GPE_list, SNR_list, s
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_MSN_vs_SNR_rate(load=True):
global SNR_INJECTED_CURRENT
global N_MSN
global N_GPE
global N_STN
global GPE_BASE_RATE
# Path were raw data is saved. For example the spike trains.
save_result_at = OUTPUT_PATH + '/simulate_MSN_vs_SNR_rate.pkl'
save_header_at = OUTPUT_PATH + '/simulate_MSN_vs_SNR_rate_header'
MSNmeanRates = numpy.arange(0.1, 3.1, 0.1)
SNRmeanRates = []
sim_time = 100000.
if not load:
for r in MSNmeanRates:
my_nest.ResetKernel(threads=3)
model_list, model_dict = models()
my_nest.MyLoadModels(model_list, NEURON_MODELS)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_BACKGROUND)
MSN = MyPoissonInput(n=N_MSN)
GPE = MyPoissonInput(n=N_GPE)
STN = MyPoissonInput(n=N_STN)
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
SNR = MyGroup(NEURON_MODELS[0],
n=len(SYNAPSE_MODELS_TESTED),
params={'I_e': I_e},
sd=True)
for id in MSN[:]:
MSN.set_spike_times(id=id,
rates=numpy.array([r]),
times=numpy.array([1]),
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate GPE
for id in GPE[:]:
GPE.set_spike_times(id=id,
rates=[GPE_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate STN
for id in STN[:]:
STN.set_spike_times(id=id,
rates=[STN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
for i, syn in enumerate(SYNAPSE_MODELS_TESTED):
my_nest.ConvergentConnect(MSN[:], [SNR[i]], model=syn)
my_nest.ConvergentConnect(GPE[:], [SNR[i]],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.ConvergentConnect(STN[:], [SNR[i]],
model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
SNR.get_signal('s') # retrieve signal
SNRmeanRates.append(SNR.signals['spikes'].mean_rates(
1000.0, sim_time))
SNRmeanRates = numpy.array(SNRmeanRates).transpose()
MSNmeanRates = numpy.array(MSNmeanRates)
rateAtThr = ''
for SNRr in SNRmeanRates:
tmp = str(MSNmeanRates[SNRr >= SELECTION_THR][-1])
rateAtThr += ' ' + tmp[0:4]
s = '\n'
s = s + 'simulate_MSN_vs_SNR_rate:\n'
s = s + ' %s %5s %3s \n' % ('N MSNs:', str(N_MSN), '#')
s = s + ' \n%s \n%5s %3s \n' % ('MSN rates:', str(
MSNmeanRates[0]) + '-' + str(MSNmeanRates[-1]), 'spikes/s')
s = s + ' %s %5s %3s \n' % ('N GPes:', str(N_GPE), '#')
s = s + ' %s %5s %3s \n' % ('Threshold SNr:', str(SELECTION_THR),
'spikes/s')
s = s + ' \n%s \n%5s %3s \n' % ('MSN rate right before threshold SNr:',
str(rateAtThr), 'spikes/s')
s = s + ' \n%s %5s %3s \n' % ('Simulation time:', str(sim_time), 'ms')
s = s + ' %s %5s %3s \n' % ('Injected current:',
str(SNR_INJECTED_CURRENT), 'pA')
infoString = s
header = HEADER_SIMULATION_SETUP + s
misc.text_save(header, save_header_at)
misc.pickle_save([MSNmeanRates, SNRmeanRates, infoString],
save_result_at)
elif load:
MSNmeanRates, SNRmeanRates, infoString = misc.pickle_load(
save_result_at)
return MSNmeanRates, SNRmeanRates, infoString
def simulate_example(hz=0, load=True):
global SNR_INJECTED_CURRENT
global NEURON_MODELS
global N_GPE
global N_SEL
global N_MSN
global N_STN
global MSN_RATE_BASE
global STN_BASE_RATE
global SNAME
global SPATH
global SYNAPSE_MODELS
global SEL_ONSET
global GPE_BASE_RATE
#n_exp = 20
n_exp = 200
RATE_SELE = hz # Selection rate
save_at = SPATH + '/' + NEURON_MODELS[0] + '-example.pkl'
sim_time = SEL_TIME + SEL_ONSET + 500.
SNAME_NB = hz + 1000
experiments = range(n_exp)
MODEL_LIST = models()
my_nest.ResetKernel()
my_nest.MyLoadModels(MODEL_LIST, NEURON_MODELS)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_BACKGROUND)
GPE_list = [] # GPE input for each experiment
for i_exp in experiments:
GPE = MyPoissonInput(n=N_GPE,
sd=True,
spath=SPATH,
sname_nb=SNAME_NB + i_exp)
GPE_list.append(GPE)
MSN_list = [] # MSN input for each experiment
for i_exp in experiments:
MSN = MyPoissonInput(n=N_MSN, sd=False)
MSN_list.append(MSN)
STN_list = [] # MSN input for each experiment
for i_exp in experiments:
STN = MyPoissonInput(n=N_STN, sd=False)
STN_list.append(STN)
SNR_list = [] # SNR groups for each synapse
I_e = my_nest.GetDefaults(NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
for i_syn in range(len(SYNAPSE_MODELS_TESTED)):
SNR = MyGroup(NEURON_MODELS[0], n=n_exp, params={'I_e': I_e}, sd=True)
SNR_list.append(SNR)
if not load:
for i_exp in experiments:
GPE = GPE_list[i_exp]
MSN = MSN_list[i_exp]
STN = STN_list[i_exp]
# Set spike times
# Base rate MSN
for id in MSN[:]:
MSN.set_spike_times(id=id,
rates=[MSN_RATE_BASE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate STN
for id in STN[:]:
STN.set_spike_times(id=id,
rates=[STN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Set spike times
# Base rate
for id in GPE[0:N_GPE - N_SEL]:
GPE.set_spike_times(id=id,
rates=[GPE_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Selection
for id in GPE[N_GPE - N_SEL:N_GPE]:
rates = [GPE_BASE_RATE, RATE_SELE, GPE_BASE_RATE]
times = [1, SEL_ONSET, SEL_TIME + SEL_ONSET]
t_stop = sim_time
GPE.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop,
seed=int(numpy.random.random() * 10000.0))
# Connect
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
target = SNR_list[i_syn][i_exp]
my_nest.ConvergentConnect(GPE[:], [target], model=syn)
my_nest.ConvergentConnect(MSN[:], [target],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.ConvergentConnect(STN[:], [target],
model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
for GPE in GPE_list:
GPE.get_signal('s')
for SNR in SNR_list:
SNR.get_signal('s')
misc.pickle_save([GPE_list, SNR_list], save_at)
elif load:
GPE_list, SNR_list = misc.pickle_load(save_at)
pre_ref = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET - 5000, SEL_ONSET))
pre_dyn = str(SNR_list[1].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
statusSynapes = []
for syn in SYNAPSE_MODELS_TESTED:
statusSynapes.append(my_nest.GetDefaults(syn))
s = '\n'
s = s + 'Example:\n'
s = s + ' %s %5s %3s \n' % ('N experiments:', str(len(experiments)), '#')
s = s + ' %s %5s %3s \n' % ('N GPEs:', str(N_GPE), '#')
s = s + ' %s %5s %3s \n' % ('Base rate:', str(GPE_BASE_RATE), 'spikes/s')
s = s + ' %s %5s %3s \n' % ('Selection rate:', str(RATE_SELE), 'spikes/s')
s = s + ' %s %5s %3s \n' % ('Selection time:', str(SEL_TIME), 'ms')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref[0:4], 'spikes/s')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Dyn:', pre_dyn[0:4], 'spikes/s')
for ss in statusSynapes:
s = s + '\n'
s = s + ' %s %10s\n' % ('Synapse', ss['synapsemodel'])
s = s + ' %s %5s %3s\n' % ('Weight', str(round(ss['weight'], 1)), 'nS')
return GPE_list, SNR_list, s
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_example(hz_1=0., hz_2=100., load=True):
global I_E
global NEURON_MODELS
global N_GPE
global N_SEL
global N_MSN
global N_STN
global MSN_RATE_BASE
global STN_RATE_BASE
global SNAME
global SPATH
global SYNAPSE_MODELS
global SEL_ONSET
N_EXP = 200
RATE_BASE = 25 # Base rate
RATE_SELE_1 = hz_1
RATE_SELE_2 = hz_2 # Selection rate
SAVE_AT = SPATH + '/' + NEURON_MODELS[0] + '-example.pkl'
SEL_TIME_1 = 500.
SEL_TIME_2 = 200.
sim_time = SEL_TIME_1 + SEL_TIME_2 + SEL_ONSET + 500.
SNAME_NB = hz_1 + hz_2 + 1000
EXPERIMENTS = range(N_EXP)
MODEL_LIST = models()
my_nest.ResetKernel()
my_nest.MyLoadModels(MODEL_LIST, NEURON_MODELS)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_BACKGROUND)
GPE_list = [] # GPE input for each experiment
for i_exp in EXPERIMENTS:
GPE = MyPoissonInput(n=N_GPE, sd=True)
GPE_list.append(GPE)
MSN_list = [] # MSN input for each experiment
for i_exp in EXPERIMENTS:
MSN = MyPoissonInput(n=N_MSN, sd=True)
MSN_list.append(MSN)
STN_list = [] # MSN input for each experiment
for i_exp in EXPERIMENTS:
STN = MyPoissonInput(n=N_STN, sd=True)
STN_list.append(STN)
SNR_list = [] # SNR groups for each synapse
for i_syn in SYNAPSE_MODELS_TESTED:
I_e = my_nest.GetDefaults(NEURON_MODELS[0])['I_e'] + I_E
SNR = MyGroup(NEURON_MODELS[0],
n=N_EXP,
params={'I_e': I_e},
sd=True,
mm=False,
mm_dt=.1,
record_from=[''])
SNR_list.append(SNR)
if not load:
for i_exp in EXPERIMENTS:
GPE = GPE_list[i_exp]
MSN = MSN_list[i_exp]
STN = STN_list[i_exp]
# Set spike times
# Base rate MSN
for id in MSN[:]:
MSN.set_spike_times(id=id,
rates=[MSN_RATE_BASE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate
for id in GPE[0:N_GPE - N_SEL]:
GPE.set_spike_times(id=id,
rates=[RATE_BASE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate STN
for id in STN[:]:
STN.set_spike_times(id=id,
rates=[STN_RATE_BASE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Selection
for id in GPE[N_GPE - N_SEL:N_GPE]:
rates = [RATE_BASE, RATE_SELE_1, RATE_SELE_2, RATE_BASE]
times = [
1, SEL_ONSET, SEL_ONSET + SEL_TIME_1,
SEL_ONSET + SEL_TIME_1 + SEL_TIME_2
]
t_stop = sim_time
GPE.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop,
seed=int(numpy.random.random() * 10000.0))
# Connect
for i, syn in enumerate(SYNAPSE_MODELS_TESTED):
target = SNR_list[i][i_exp]
my_nest.ConvergentConnect(GPE[:], [target], model=syn)
my_nest.ConvergentConnect(MSN[:], [target],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.ConvergentConnect(STN[:], [target],
model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
for GPE in GPE_list:
GPE.get_signal('s')
for SNR in SNR_list:
SNR.get_signal('s')
misc.pickle_save([GPE_list, SNR_list], SAVE_AT)
elif load:
GPE_list, SNR_list = misc.pickle_load(SAVE_AT)
pre_ref = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
pre_dyn = str(SNR_list[1].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
statusSynapes = []
for syn in SYNAPSE_MODELS_TESTED:
statusSynapes.append(my_nest.GetDefaults(syn))
s = '\n'
s = s + 'Example:\n'
s = s + ' %s %5s %3s \n' % ('N experiments:', str(len(EXPERIMENTS)), '#')
s = s + ' %s %5s %3s \n' % ('N GPEs:', str(N_GPE), '#')
s = s + ' %s %5s %3s \n' % ('Base rate:', str(RATE_BASE), 'spikes/s')
s = s + ' %s %5s %3s \n' % ('Selection rate:', str(RATE_SELE_1),
'spikes/s')
s = s + ' %s %5s %3s \n' % ('Selection time:', str(SEL_TIME_1), 'ms')
s = s + ' %s %5s %3s \n' % ('Selection rate:', str(RATE_SELE_2),
'spikes/s')
s = s + ' %s %5s %3s \n' % ('Selection time:', str(SEL_TIME_2), 'ms')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref[0:4], 'spikes/s')
s = s + ' %s %5s %3s \n' % ('Pre sel rate Dyn:', pre_dyn[0:4], 'spikes/s')
for ss in statusSynapes:
s = s + '\n'
s = s + ' %s %10s\n' % ('Synapse', ss['synapsemodel'])
s = s + ' %s %5s %3s\n' % ('Weight', str(round(ss['weight'], 1)), 'nS')
return GPE_list, SNR_list, s
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
delay = my_nest.GetDefaults(syn)['delay']
cond_peaks = {}
for id, analog_signal in SNR.signals['g_GABAA_1'].analog_signals.iteritems(
): # find conductance peaks
cond_peaks[keys[id - 1]] = []
for i in spikes[keys[id - 1]]:
i = int(10 * i) + delay * 10
valley = numpy.min(analog_signal.signal[i - 20:i + 10])
cond_peaks[keys[id - 1]].append(
numpy.max(analog_signal.signal[i - 20:i + 10] - valley))
# Create figure where figsize(width,height) and figure dimenstions window
# width = figsize(width) x dpi and window hight = figsize(hight) x dpi
def simulate_basa_line_GPe(msn_rate,
stn_rate,
gpe_rate,
n_msn,
n_stn,
n_gpe,
neuron_model,
syn_models,
gpe_current,
sim_time=1000,
threads=8):
model_list, model_dict = models()
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels(model_dict, neuron_model)
my_nest.MyLoadModels(model_dict, syn_models)
SNR_list = [] # List with SNR groups for synapse.
if n_msn > 0: MSN = MyPoissonInput(n=n_msn, sd=False)
if n_stn > 0: STN = MyPoissonInput(n=n_stn, sd=False)
if n_gpe > 0: GPE = MyPoissonInput(n=n_gpe, sd=False)
if n_msn > 0:
MSN.set_spike_times(rates=[msn_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_stn > 0:
STN.set_spike_times(rates=[stn_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_gpe > 0:
GPE.set_spike_times(rates=[gpe_rate],
times=[1],
t_stop=sim_time,
seed=0)
I_e = my_nest.GetDefaults(neuron_model[0])['I_e'] + gpe_current
GPE_target = MyGroup(neuron_model[0],
n=1,
sd=True,
params={'I_e': I_e},
mm_dt=.1,
mm=True)
if n_msn > 0:
my_nest.Connect(MSN[:],
GPE_target[:] * len(MSN[:]),
model=syn_models[0])
if n_stn > 0:
my_nest.Connect(STN[:],
GPE_target[:] * len(STN[:]),
model=syn_models[1])
if n_gpe > 0:
my_nest.Connect(GPE[:],
GPE_target[:] * len(GPE[:]),
model=syn_models[2])
my_nest.MySimulate(sim_time)
GPE_target.get_signal('s', start=0, stop=sim_time)
meanRate = round(GPE_target.signals['spikes'].mean_rate(1000, sim_time), 1)
spk = GPE_target.signals['spikes'].time_slice(1000, sim_time).raw_data()
CV = numpy.std(numpy.diff(spk[:, 0], axis=0)) / numpy.mean(
numpy.diff(spk[:, 0], axis=0))
GPE_target.get_signal('v', recordable='V_m', start=0, stop=sim_time)
GPE_target.signals['V_m'].my_set_spike_peak(
15, spkSignal=GPE_target.signals['spikes'])
pylab.rcParams.update({'path.simplify': False})
GPE_target.signals['V_m'].plot()
pylab.title(str(meanRate) + 'Hz, CV=' + str(CV))
pylab.show()
return
def simulate_basa_line_GPe(msn_rate,
stn_rate,
gpe_rate,
n_msn,
n_stn,
n_gpe,
neuron_model,
syn_models,
gpe_current,
sim_time=1000,
threads=8,
w_GPE_GPE=False,
w_STN_GPE=False):
Params_in = {}
if w_GPE_GPE: Params_in['GPE_GPE_gaba_s'] = w_GPE_GPE
if w_STN_GPE: Params_in['STN_GPE_ampa_s'] = w_STN_GPE
model_list, model_dict = models(Params_in)
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels(model_dict, neuron_model)
my_nest.MyLoadModels(model_dict, syn_models)
SNR_list = [] # List with SNR groups for synapse.
if n_msn > 0: MSN = MyPoissonInput(n=n_msn, sd=False)
if n_stn > 0: STN = MyPoissonInput(n=n_stn, sd=False)
if n_gpe > 0: GPE = MyPoissonInput(n=n_gpe, sd=False)
if n_msn > 0:
MSN.set_spike_times(rates=[msn_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_stn > 0:
STN.set_spike_times(rates=[stn_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_gpe > 0:
GPE.set_spike_times(rates=[gpe_rate],
times=[1],
t_stop=sim_time,
seed=0)
I_e = my_nest.GetDefaults(neuron_model[0])['I_e'] + gpe_current
GPE_target = MyGroup(neuron_model[0],
n=1,
sd=True,
params={'I_e': I_e},
mm_dt=.1,
mm=True)
if n_msn > 0:
my_nest.Connect(MSN[:],
GPE_target[:] * len(MSN[:]),
model=syn_models[0])
if n_stn > 0:
my_nest.Connect(STN[:],
GPE_target[:] * len(STN[:]),
model=syn_models[1])
if n_gpe > 0:
my_nest.Connect(GPE[:],
GPE_target[:] * len(GPE[:]),
model=syn_models[2])
my_nest.MySimulate(sim_time)
GPE_target.get_signal('s', start=0, stop=sim_time)
GPE_target.get_signal('v', recordable='V_m', start=0, stop=sim_time)
return GPE_target
def simulate_example(load=True):
global GPE_BASE_RATE
global FILE_NAME
global N_GPE
global N_STN
global N_MSN_BURST
global N_MSN
global NEURON_MODELS
global OUTPUT_PATH
global SEL_ONSET
global SNR_INJECTED_CURRENT
global SYNAPSE_MODELS_TESTED
#n_exp =200 # number of experiments
n_exp = 200 # number of experiments
# Path were raw data is saved. For example the spike trains.
save_result_at = OUTPUT_PATH + '/simulate_example.pkl'
save_header_at = OUTPUT_PATH + '/simulate_example_header'
burst_time = 500.
sim_time = SEL_INTERVAL_2[1] + 500
model_list = models()
my_nest.ResetKernel(threads=8)
my_nest.MyLoadModels(model_list, NEURON_MODELS)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_BACKGROUND)
SNR_list = [] # List with SNR groups for synapse.
if not load:
MSN_base = MyPoissonInput(n=N_MSN_BASE * n_exp)
MSN_burst = MyPoissonInput(n=N_MSN_BURST * n_exp)
GPE = MyPoissonInput(n=N_GPE * n_exp, sd=True)
STN = MyPoissonInput(n=N_STN * n_exp, sd=True)
# Set spike times MSN and GPe
# Non bursting MSNs
for id in MSN_base[:]:
seed = numpy.random.random_integers(0, 1000000.0)
MSN_base.set_spike_times(id=id,
rates=[MSN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=seed)
# Background GPe
for id in GPE[:]:
seed = numpy.random.random_integers(0, 1000000.0)
GPE.set_spike_times(id=id,
rates=[GPE_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=seed)
# Background STN
for id in STN[:]:
seed = numpy.random.random_integers(0, 1000000.0)
STN.set_spike_times(id=id,
rates=[STN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=seed)
# Bursting MSNs
for id in MSN_burst[:]:
rates = [
MSN_BASE_RATE, MSN_BURST_RATE, MSN_BASE_RATE, MSN_BURST_RATE,
MSN_BASE_RATE
]
times = [
1, SEL_INTERVAL_1[0], SEL_INTERVAL_1[1], SEL_INTERVAL_2[0],
SEL_INTERVAL_2[1]
]
t_stop = sim_time
seed = numpy.random.random_integers(0, 1000000.0)
MSN_burst.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop,
seed=seed)
for i_syn in range(len(SYNAPSE_MODELS_TESTED)):
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
SNR = MyGroup(NEURON_MODELS[0],
n=n_exp,
sd=True,
params={'I_e': I_e},
mm_dt=.1,
record_from=[''])
SNR_list.append(SNR)
# Connect, experiment specific
sources_MSN_SNR_base = numpy.arange(0, n_exp * N_MSN_BASE)
sources_MSN_SNR_burst = numpy.arange(0, n_exp * N_MSN_BURST)
targets_MSN_SNR_base = numpy.mgrid[0:n_exp, 0:N_MSN_BASE][0].reshape(
1, N_MSN_BASE * n_exp)[0]
targets_MSN_SNR_burst = numpy.mgrid[0:n_exp, 0:N_MSN_BURST][0].reshape(
1, N_MSN_BURST * n_exp)[0]
sources_GPE_SNR = numpy.arange(0, n_exp * N_GPE)
targets_GPE_SNR = numpy.mgrid[0:n_exp,
0:N_GPE][0].reshape(1, N_GPE * n_exp)[0]
sources_STN_SNR = numpy.arange(0, n_exp * N_STN)
targets_STN_SNR = numpy.mgrid[0:n_exp,
0:N_STN][0].reshape(1, N_STN * n_exp)[0]
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
syn = SYNAPSE_MODELS_TESTED[i_syn]
SNR = SNR_list[i_syn]
my_nest.Connect(MSN_base[sources_MSN_SNR_base],
SNR[targets_MSN_SNR_base],
model=syn)
my_nest.Connect(MSN_burst[sources_MSN_SNR_burst],
SNR[targets_MSN_SNR_burst],
model=syn)
my_nest.Connect(GPE[sources_GPE_SNR],
SNR[targets_GPE_SNR],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.Connect(STN[sources_STN_SNR],
SNR[targets_STN_SNR],
model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
for SNR in SNR_list:
SNR.get_signal('s', start=0, stop=sim_time)
pre_ref_1 = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
burst_1 = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET, SEL_ONSET + 200))
burst_2 = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET + 1000, SEL_ONSET + 1200))
s = '\n'
s = s + 'Simulate example:\n'
s = s + '%s %5s %3s \n' % ('Simulation time', str(sim_time), '#')
s = s + '%s %5s %3s \n' % ('N experiments:', str(n_exp), '#')
s = s + '%s %5s %3s \n' % ('MSN base rate:', str(MSN_BASE_RATE),
'spikes/s')
s = s + '%s %5s %3s \n' % ('MSN burst rate:', str(MSN_BURST_RATE),
'spikes/s')
s = s + '%s %5s %3s \n' % ('GPe rate:', str(GPE_BASE_RATE), 'spikes/s')
s = s + '%s %5s %3s \n' % ('Burst time:', str(burst_time), 'ms')
s = s + '%s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref_1[0:4],
'spikes/s')
s = s + '%s %5s %3s \n' % ('Burst 1:', burst_1[0:4], 'spikes/s')
s = s + '%s %5s %3s \n' % ('Burst 2:', burst_2[0:4], 'spikes/s')
header = s
misc.text_save(header, save_header_at)
misc.pickle_save([SNR_list, s], save_result_at)
else:
SNR_list, s = misc.pickle_load(save_result_at)
return SNR_list, s
def simulate_example(load=True):
global SNR_INJECTED_CURRENT
global NEURON_MODELS
global N_GPE
global N_MSN_BURST
global N_MSN
global GPE_BASE_RATE
global FILE_NAME
global OUTPUT_PATH
global SYNAPSE_MODELS_TESTED
global SEL_ONSET
#n_exp =200 # number of experiments
n_exp = 20 # number of experiments
# Path were raw data is saved. For example the spike trains.
save_result_at = OUTPUT_PATH + '/' + FILE_NAME + '-simulate_example.pkl'
save_header_at = OUTPUT_PATH + '/' + FILE_NAME + '-simulate_example_header'
burst_time = 500.
sim_time = burst_time + SEL_ONSET + 1000.
MODEL_LIST = models()
my_nest.ResetKernel()
my_nest.MyLoadModels(MODEL_LIST, NEURON_MODELS)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(MODEL_LIST, SYNAPSE_MODELS_BACKGROUND)
SNR_list = [] # List with SNR groups for synapse.
if not load:
MSN_base = MyPoissonInput(n=N_MSN_BASE * n_exp, sd=True)
MSN_burst = MyPoissonInput(n=N_MSN_BURST * n_exp, sd=True)
GPE = MyPoissonInput(n=N_GPE * n_exp, sd=True)
# Set spike times MSN and GPe
# Non bursting MSNs
for id in MSN_base[:]:
seed = numpy.random.random_integers(0, 1000000.0)
MSN_base.set_spike_times(id=id,
rates=[MSN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=seed)
# Background GPe
for id in GPE[:]:
seed = numpy.random.random_integers(0, 1000000.0)
GPE.set_spike_times(id=id,
rates=[GPE_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=seed)
# Bursting MSNs
for id in MSN_burst[:]:
rates = [MSN_BASE_RATE, MSN_BURST_RATE, MSN_BASE_RATE]
times = [1, SEL_ONSET, burst_time + SEL_ONSET]
t_stop = sim_time
seed = numpy.random.random_integers(0, 1000000.0)
MSN_burst.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop,
seed=seed)
for i_syn in range(len(SYNAPSE_MODELS_TESTED)):
params = []
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
for i in range(n_exp):
#params.append({'I_e':numpy.random.normal(I_e,
# 0.1*I_e)})
params.append({'I_e': I_e})
#{'I_e':SNR_INJECTED_CURRENT}
SNR = MyGroup(NEURON_MODELS[0],
n=n_exp,
sd=True,
params=params,
mm_dt=.1,
record_from=[''])
SNR_list.append(SNR)
# Connect, experiment specific
sources_MSN_SNR_base = numpy.arange(0, n_exp * N_MSN_BASE)
sources_MSN_SNR_burst = numpy.arange(0, n_exp * N_MSN_BURST)
targets_MSN_SNR_base = numpy.mgrid[0:n_exp, 0:N_MSN_BASE][0].reshape(
1, N_MSN_BASE * n_exp)[0]
targets_MSN_SNR_burst = numpy.mgrid[0:n_exp, 0:N_MSN_BURST][0].reshape(
1, N_MSN_BURST * n_exp)[0]
sources_GPE_SNR = numpy.arange(0, n_exp * N_GPE)
targets_GPE_SNR = numpy.mgrid[0:n_exp,
0:N_GPE][0].reshape(1, N_GPE * n_exp)[0]
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
syn = SYNAPSE_MODELS_TESTED[i_syn]
SNR = SNR_list[i_syn]
my_nest.Connect(MSN_base[sources_MSN_SNR_base],
SNR[targets_MSN_SNR_base],
model=syn)
my_nest.Connect(MSN_burst[sources_MSN_SNR_burst],
SNR[targets_MSN_SNR_burst],
model=syn)
my_nest.Connect(GPE[sources_GPE_SNR],
SNR[targets_GPE_SNR],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.MySimulate(sim_time)
MSN_base.get_signal('s', start=0, stop=sim_time)
MSN_burst.get_signal('s', start=0, stop=sim_time)
for SNR in SNR_list:
SNR.get_signal('s', start=0, stop=sim_time)
# Get firing rates of MSNs
MSN_firing_rates = []
MSN_all = copy.deepcopy(MSN_base)
MSN_all.merge(MSN_burst)
time_bin = 20.
groups = [MSN_base, MSN_burst, MSN_all]
for group in groups:
timeAxis, firingRates = group.signals['spikes'].my_firing_rate(
bin=time_bin, display=False)
MSN_firing_rates.append([timeAxis, firingRates])
# Pick out spikes for burst, base and all to use in scatter plot
MSN_spikes_and_ids = []
g1 = MSN_burst.slice(MSN_burst[0:N_MSN_BURST])
g2 = MSN_base.slice(MSN_base[0:N_MSN_BASE])
ids_MSN_burst = range(450, 450 + N_MSN_BURST)
ids_MSN_base = [id for id in range(N_MSN) if id not in IDS_MSN_BURST]
# Rename ids for plotting purpose
g1_dict = dict([[id1, id2] for id1, id2 in zip(g1.ids, ids_MSN_burst)])
g2_dict = dict([[id1, id2] for id1, id2 in zip(g2.ids, ids_MSN_base)])
groups = [g1, g2]
dics = [g1_dict, g2_dict]
for group, dic in zip(groups, dics):
raw_data = group.signals['spikes'].raw_data()
for i in range(raw_data.shape[0]):
raw_data[i, 1] = dic[raw_data[i, 1]]
MSN_spikes_and_ids.append(raw_data)
#times, binned_data=MSN_base.signals['spikes'].binned_raw_data(0, sim_time, res=1, clip=0)
#filtered_binned_data=misc.time_resolved_rate(binned_data, 100, kernel_type='triangle', res=1)
pre_ref_1 = str(SNR_list[0].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
pre_ref_2 = str(SNR_list[1].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
pre_dyn = str(SNR_list[2].signals['spikes'].mean_rate(
SEL_ONSET - 500, SEL_ONSET))
s = '\n'
s = s + 'Simulate example:\n'
s = s + '%s %5s %3s \n' % ('N experiments:', str(n_exp), '#')
s = s + '%s %5s %3s \n' % ('Bin size MSN hz:', str(time_bin), 'ms')
s = s + '%s %5s %3s \n' % ('MSN base rate:', str(MSN_BASE_RATE), 'Hz')
s = s + '%s %5s %3s \n' % ('MSN burst rate:', str(MSN_BURST_RATE),
'Hz')
s = s + '%s %5s %3s \n' % ('GPe rate:', str(GPE_BASE_RATE), 'Hz')
s = s + '%s %5s %3s \n' % ('Burst time:', str(burst_time), 'ms')
s = s + '%s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref_1[0:4], 'Hz')
s = s + '%s %5s %3s \n' % ('Pre sel rate Ref:', pre_ref_2[0:4], 'Hz')
s = s + '%s %5s %3s \n' % ('Pre sel rate Dyn:', pre_dyn[0:4], 'Hz')
header = HEADER_SIMULATION_SETUP + s
misc.pickle_save([MSN_firing_rates, MSN_spikes_and_ids, SNR_list, s],
save_result_at)
misc.text_save(header, save_header_at)
else:
MSN_firing_rates, MSN_spikes_and_ids, SNR_list, s = misc.pickle_load(
save_result_at)
return MSN_firing_rates, MSN_spikes_and_ids, SNR_list, s
def simulate_example(MSN_hz=20, GPE_hz=0, load=True, n_gpe_sel=3, sel_time_GPE=500):
global GPE_BASE_RATE
global STN_BASE_RATE
global MSN_BASE_RATE
global MSN_BURST_TIME
global NEURON_MODELS
global N_GPE
global N_STN
global N_MSN
global N_MSN_BURST
global SNAME
global SPATH
global SYNAPSE_MODELS
global SEL_ONSET
global SNR_INJECTED_CURRENT
n_exp = 200
msn_rate_sel = MSN_hz # Selection rate
gpe_sel_rate = GPE_hz # Selection rate
sel_time_MSN = MSN_BURST_TIME
sim_time = sel_time_MSN+SEL_ONSET+500.
EXPERIMENTS=range(n_exp)
MODEL_LIST=models()
my_nest.ResetKernel()
my_nest.MyLoadModels( MODEL_LIST, NEURON_MODELS )
my_nest.MyLoadModels( MODEL_LIST, SYNAPSE_MODELS)
my_nest.MyLoadModels( MODEL_LIST, SYNAPSE_MODELS_BACKGROUND)
MSN_list=[] # MSN input for each experiment
for i_exp in EXPERIMENTS:
MSN = MyPoissonInput( n=N_MSN+N_MSN_BURST, sd=True)
MSN_list.append(MSN)
GPE_list=[] # GPE input for each experiment
for i_exp in EXPERIMENTS:
GPE = MyPoissonInput( n=N_GPE+n_gpe_sel, sd=True)
GPE_list.append(GPE)
STN_list=[] # GPE input for each experiment
for i_exp in EXPERIMENTS:
STN = MyPoissonInput( n=N_STN, sd=True)
STN_list.append(GPE)
SNR_list=[] # SNR groups for each synapse
for i, SNR_i_c in enumerate(SNR_INJECTED_CURRENT):
I_e=my_nest.GetDefaults(NEURON_MODELS[0])['I_e']+SNR_i_c
SNR = MyGroup( NEURON_MODELS[0], n=n_exp, params={'I_e':I_e},
sd=True, mm=False,
mm_dt=.1, record_from=[''])
SNR_list.append(SNR)
if not load:
for i_exp in EXPERIMENTS:
# MSN
MSN = MSN_list[i_exp]
# Set spike times
# Base rate
for id in MSN[0:N_MSN]:
MSN.set_spike_times(id=id, rates=[MSN_BASE_RATE], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
# Selection MSN
for id in MSN[N_MSN:N_MSN+N_MSN_BURST]:
rates = [MSN_BASE_RATE, msn_rate_sel, MSN_BASE_RATE]
times = [1, SEL_ONSET, sel_time_MSN + SEL_ONSET]
t_stop = sim_time
MSN.set_spike_times(id=id, rates=rates, times=times,
t_stop=t_stop,
seed=int(numpy.random.random()*10000.0))
# GPE
GPE = GPE_list[i_exp]
# Set spike times
# Base rate
for id in GPE[:]:
GPE.set_spike_times(id=id, rates=[GPE_BASE_RATE], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
# Selection GPE
for id in GPE[N_GPE:N_GPE+n_gpe_sel]:
rates = [GPE_BASE_RATE, gpe_sel_rate, GPE_BASE_RATE]
# If GPe excited smaller selection time
times = [1, SEL_ONSET, sel_time_GPE + SEL_ONSET]
t_stop = sim_time
GPE.set_spike_times(id=id, rates=rates, times=times,
t_stop=t_stop, seed=int(numpy.random.random()*100000.0))
# Base rate STN
for id in STN[:]:
STN.set_spike_times(id=id, rates=[STN_BASE_RATE], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
idx_MSN_s=range(0,N_MSN-N_MSN_BURST)
idx_MSN_s.extend(range(N_MSN,N_MSN+N_MSN_BURST))
idx_GPE_s=range(0,N_GPE-n_gpe_sel)
idx_GPE_s.extend(range(N_GPE,N_GPE+n_gpe_sel))
# Connect with MSN burst
target=SNR_list[0][i_exp]
my_nest.ConvergentConnect(MSN[idx_MSN_s], [target], model=SYNAPSE_MODELS[0])
my_nest.ConvergentConnect(GPE[0:N_GPE], [target], model=SYNAPSE_MODELS[1])
my_nest.ConvergentConnect(STN[:], [target], model=SYNAPSE_MODELS_BACKGROUND[0])
# With GPe pause
target=SNR_list[1][i_exp]
my_nest.ConvergentConnect(MSN[0:N_MSN], [target], model=SYNAPSE_MODELS[0])
my_nest.ConvergentConnect(GPE[idx_GPE_s], [target], model=SYNAPSE_MODELS[1])
my_nest.ConvergentConnect(STN[:], [target], model=SYNAPSE_MODELS_BACKGROUND[0])
# With MSN burst and GPe pause
target=SNR_list[2][i_exp]
my_nest.ConvergentConnect(MSN[idx_MSN_s], [target], model=SYNAPSE_MODELS[0])
my_nest.ConvergentConnect(GPE[idx_GPE_s], [target], model=SYNAPSE_MODELS[1])
my_nest.ConvergentConnect(STN[:], [target], model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.MySimulate( sim_time )
for MSN in MSN_list:
MSN.get_signal( 's' )
for GPE in GPE_list:
GPE.get_signal( 's' )
for SNR in SNR_list:
SNR.get_signal( 's' )
misc.pickle_save([MSN_list, GPE_list,SNR_list] , save_at)
if load:
MSN_list, GPE_list, SNR_list=misc.pickle_load(save_at)
pre_dyn_MSN=str(SNR_list[0].signals['spikes'].mean_rate(SEL_ONSET-500,
SEL_ONSET))
pre_dyn_GPE=str(SNR_list[1].signals['spikes'].mean_rate(SEL_ONSET-500,
SEL_ONSET))
s='\n'
s=s+'Example:\n'
s = s + ' %s %5s %3s \n' % ( 'N experiments:', str ( len(EXPERIMENTS) ), '#' )
s = s + ' %s %5s %3s \n' % ( 'N MSN:', str ( N_MSN ), '#' )
s = s + ' %s %5s %3s \n' % ( 'N GPE:', str ( N_GPE ), '#' )
s='\n'
s = s + ' %s %5s %3s \n' % ( 'Base rate MSN:', str ( MSN_BASE_RATE),'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Sel rate MSN:', str ( msn_rate_sel ), 'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Sel time MSN:', str ( sel_time_MSN ), 'ms' )
s='\n'
s = s + ' %s %5s %3s \n' % ( 'Base rate GPe:', str ( GPE_BASE_RATE),'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Sel rate GPe:', str ( gpe_sel_rate ), 'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Sel time GPe:', str ( sel_time_GPE ), 'ms' )
s = s + ' %s %5s %3s \n' % ( 'Pre sel rate Dyn MSN:', pre_dyn_MSN[0:4], 'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Pre sel rate Dyn GPe:', pre_dyn_GPE[0:4], 'spikes/s' )
return MSN_list, GPE_list, SNR_list, s
info_string=s
return MSN_hzs, GPE_hzs, data, info_string
def simulate_basa_line_SNr(msn_rate,
gpe_rate,
stn_rate,
n_msn,
n_gpe,
n_stn,
neuron_model,
snr_current,
sim_time=1000,
threads=8,
stn_syn='STN_SNR_ampa_s'):
SNR_INJECTED_CURRENT = snr_current
SYNAPSE_MODEL_BACKROUND_STN = [stn_syn]
model_list, model_dict = models()
my_nest.ResetKernel(threads=threads)
my_nest.MyLoadModels(model_dict, neuron_model)
my_nest.MyLoadModels(model_dict, SYNAPSE_MODEL_BACKROUND_MSN)
my_nest.MyLoadModels(model_dict, SYNAPSE_MODEL_BACKROUND_GPE)
my_nest.MyLoadModels(model_dict, SYNAPSE_MODEL_BACKROUND_STN)
SNR_list = [] # List with SNR groups for synapse.
if n_msn > 0: MSN_base = MyPoissonInput(n=n_msn, sd=True)
if n_gpe > 0: GPE = MyPoissonInput(n=n_gpe, sd=False)
if n_stn > 0: STN = MyPoissonInput(n=n_stn, sd=False)
if n_msn > 0:
MSN_base.set_spike_times(rates=[msn_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_gpe > 0:
GPE.set_spike_times(rates=[gpe_rate],
times=[1],
t_stop=sim_time,
seed=0)
if n_stn > 0:
STN.set_spike_times(rates=[stn_rate],
times=[1],
t_stop=sim_time,
seed=0)
I_e = my_nest.GetDefaults(neuron_model[0])['I_e'] + SNR_INJECTED_CURRENT
SNR = MyGroup(neuron_model[0],
n=1,
sd=True,
params={'I_e': I_e},
mm_dt=.1,
mm=True)
if n_msn > 0:
my_nest.Connect(MSN_base[:],
SNR[:] * len(MSN_base[:]),
model=SYNAPSE_MODEL_BACKROUND_MSN[0])
if n_gpe > 0:
my_nest.Connect(GPE[:],
SNR[:] * len(GPE[:]),
model=SYNAPSE_MODEL_BACKROUND_GPE[0])
if n_stn > 0:
my_nest.Connect(STN[:],
SNR[:] * len(STN[:]),
model=SYNAPSE_MODEL_BACKROUND_STN[0])
my_nest.MySimulate(sim_time)
SNR.get_signal('s', start=0, stop=sim_time)
meanRate = round(SNR.signals['spikes'].mean_rate(1000, sim_time), 1)
spk = SNR.signals['spikes'].time_slice(1000, sim_time).raw_data()
CV = numpy.std(numpy.diff(spk[:, 0], axis=0)) / numpy.mean(
numpy.diff(spk[:, 0], axis=0))
SNR.get_signal('v', recordable='V_m', start=0, stop=sim_time)
SNR.signals['V_m'].my_set_spike_peak(15, spkSignal=SNR.signals['spikes'])
pylab.rcParams.update({'path.simplify': False})
SNR.signals['V_m'].plot()
pylab.title(str(meanRate) + 'Hz, CV=' + str(CV))
pylab.show()
return
def simulate_get_rates(msn_burst_rate=20, load=True, len_ms=500.):
n_exp = 50
sim_time = DP['SEL_TIME'] + DP['SEL_ONSET'] + 1000.
experiments = range(n_exp)
model_list = models()
my_nest.ResetKernel()
my_nest.MyLoadModels(model_list, DP['NEURON_MODELS'])
my_nest.MyLoadModels(model_list, DP['SYNAPSE_MODELS_TESTED'])
my_nest.MyLoadModels(model_list, DP['SYNAPSE_MODELS_BACKGROUND'])
MSN_list = [] # MSN input for each experiment
for i_exp in experiments:
MSN = MyPoissonInput(n=DP['N_MSN'], sd=True)
MSN_list.append(MSN)
GPE_list = [] # GPE input for each experiment
for i_exp in experiments:
GPE = MyPoissonInput(n=DP['N_GPE'], sd=True)
GPE_list.append(GPE)
SNR_list = [] # SNR groups for each synapse
for i_syn, syn in enumerate(DP['SYNAPSE_MODELS_TESTED']):
I_e = my_nest.GetDefaults(
DP['NEURON_MODELS'][0])['I_e'] + DP['SNR_INJECTED_CURRENT'][i_syn]
SNR = MyGroup(DP['NEURON_MODELS'][0],
n=n_exp,
sd=True,
params={'I_e': I_e})
SNR_list.append(SNR)
for i_exp in experiments:
MSN = MSN_list[i_exp]
GPE = GPE_list[i_exp]
# Set spike times
# Base rate
for id in MSN[1:DP['N_MSN']]:
MSN.set_spike_times(id=id,
rates=[DP['MSN_BASE_RATE']],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Set spike times
# Base rate
for id in GPE[:]:
GPE.set_spike_times(id=id,
rates=[DP['GPE_BASE_RATE']],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Selection
for id in MSN[DP['N_MSN'] - DP['N_MSN_BURST'] - 50:DP['N_MSN'] - 50]:
rates = [DP['MSN_BASE_RATE'], msn_burst_rate, DP['MSN_BASE_RATE']]
times = [1, DP['SEL_ONSET'], DP['SEL_TIME'] + DP['SEL_ONSET']]
t_stop = sim_time
MSN.set_spike_times(id=id,
rates=rates,
times=times,
t_stop=t_stop,
seed=int(numpy.random.random() * 10000.0))
# Connect
for i_syn, syn in enumerate(DP['SYNAPSE_MODELS_TESTED']):
target = SNR_list[i_syn][i_exp]
my_nest.ConvergentConnect(MSN[:], [target], model=syn)
my_nest.ConvergentConnect(GPE[:], [target],
model=DP['SYNAPSE_MODELS_BACKGROUND'][0])
my_nest.MySimulate(sim_time)
for SNR in SNR_list:
SNR.get_signal('s')
rate_ref_1 = str(SNR_list[0].signals['spikes'].mean_rate(
DP['SEL_ONSET'], DP['SEL_ONSET'] + len_ms))
rate_ref_2 = str(SNR_list[1].signals['spikes'].mean_rate(
DP['SEL_ONSET'], DP['SEL_ONSET'] + len_ms))
rate_dyn = str(SNR_list[2].signals['spikes'].mean_rate(
DP['SEL_ONSET'], DP['SEL_ONSET'] + len_ms))
return [rate_ref_1, rate_ref_2, rate_dyn]
