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
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_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_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]
def simulate_rate_first_and_second_bursts(
selection_intervals=[0.0, 500.0, 1000., 1500.], load=True):
global SNR_INJECTED_CURRENT
global NEURON_MODELS
global N_GPE
global N_MSN_BURST
global N_MSN
global N_STN
global MSN_BASE_RATE
global GPE_BASE_RATE
global STN_BASE_RATE
global FILE_NAME
global OUTPUT_PATH
global SYNAPSE_MODELS_TESTED
global SEL_ONSET
#n_exp=20
n_exp = 200
msn_burst_rate = 20
n_msn_burst = N_MSN_BURST
transient_stop = selection_intervals[2] - selection_intervals[1]
save_result_at = (OUTPUT_PATH + '/simulate_rate_first_and_second_bursts_' +
str(transient_stop) + 'ms.pkl')
save_header_at = (OUTPUT_PATH + '/simulate_rate_first_and_second_bursts_' +
str(transient_stop) + 'ms_header')
burst_time = 500.
sim_time = SEL_ONSET + selection_intervals[3] + 500.
EXPERIMENTS = range(n_exp)
model_list = models()
my_nest.ResetKernel(threads=1)
my_nest.MyLoadModels(model_list, NEURON_MODELS)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_TESTED)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS_BACKGROUND)
if not load:
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, 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(STN)
SNR_list = [] # SNR groups for each synapse and number of selected MSN
SNR_list_experiments = []
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
SNR = []
for i_sel in range(1):
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
SNR.append(
MyGroup(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]
STN = STN_list[i_exp]
# Set spike times
# Base rate
for id in MSN[1: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
for id in MSN[N_MSN:N_MSN + n_msn_burst]:
rates = [
MSN_BASE_RATE, msn_burst_rate, MSN_BASE_RATE,
msn_burst_rate, MSN_BASE_RATE
]
t1 = selection_intervals[0]
t2 = selection_intervals[1]
t3 = selection_intervals[2]
t4 = selection_intervals[3]
times = [
1, SEL_ONSET + t1, SEL_ONSET + t2, SEL_ONSET + t3,
SEL_ONSET + t4
]
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))
# 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 GPE
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))
# Connect
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
# i_sel goes over 0,..., n_max_sel
for i_sel, n_sel in enumerate(
range(n_msn_burst, n_msn_burst + 1)):
target = SNR_list[i_syn][i_sel][i_exp]
my_nest.ConvergentConnect(MSN[0:N_MSN - n_sel], [target],
model=syn)
my_nest.ConvergentConnect(MSN[N_MSN:N_MSN + n_sel],
[target],
model=syn)
my_nest.ConvergentConnect(
GPE[:], [target], model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.ConvergentConnect(
STN[:], [target], model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
for SNR_sel in SNR_list:
for SNR in SNR_sel:
SNR.get_signal('s')
t1 = selection_intervals[0]
t3 = selection_intervals[2]
mean_rates = []
mean_rates_std = []
# Time until arrival of spikes in SNr
delay = my_nest.GetDefaults(SYNAPSE_MODELS_BACKGROUND[0])['delay']
for SNR_sel in SNR_list:
m_r = []
m_r_std = []
for SNR in SNR_sel:
# Mean rate during first 200 ms
m_r.append(SNR.signals['spikes'].mean_rate(
SEL_ONSET + t1 + delay, SEL_ONSET + t1 + 200 + delay))
m_r.append(SNR.signals['spikes'].mean_rate(
SEL_ONSET + t3 + delay, SEL_ONSET + t3 + 200 + delay))
m_r_std.append(SNR.signals['spikes'].mean_rate_std(
SEL_ONSET + t1 + delay, SEL_ONSET + t1 + 200 + delay))
m_r_std.append(SNR.signals['spikes'].mean_rate_std(
SEL_ONSET + t3 + delay, SEL_ONSET + t3 + 200 + delay))
mean_rates.append(m_r)
mean_rates_std.append(m_r_std)
mean_rates = numpy.array(mean_rates)
mean_rates_std = numpy.array(mean_rates_std)
s = '\n'
s = s + 'simulate_rate_first_and_second_bursts\n'
s = s + '%s %5s %3s \n' % ('Simulation time', str(sim_time), '#')
s = s + '%s %5s %3s \n' % ('N MSNs:', str(N_MSN), '#')
s = s + '%s %5s %3s \n' % ('N MSN_bursts:', str(n_msn_burst), '#')
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' % ('MSN burst time:', str(burst_time), 'ms')
s = s + '%s %5s %3s \n' % ('GPe base rate:', str(GPE_BASE_RATE),
'spikes/s')
s = s + '%s %5s %3s \n' % ('SNR injected current:',
str(SNR_INJECTED_CURRENT), 'pA')
for i_interval, interval in enumerate(selection_intervals):
s = s + '%s %5s %3s \n' % ('Sel interval ' + str(i_interval) + ':',
str(selection_intervals), 'ms')
info_string = s
header = s
misc.text_save(header, save_header_at)
misc.pickle_save([mean_rates, mean_rates_std, info_string],
save_result_at)
elif load:
mean_rates, mean_rates_std, info_string = misc.pickle_load(
save_result_at)
return mean_rates, mean_rates_std, info_string
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_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_GPE_vs_SNR_const_syn_events(load=True):
global N_GPE
global N_MSN
global MSN_BASE_RATE
global SNR_INJECTED_CURRENT
save_at = (SPATH+'/'+NEURON_MODELS[0]+'-' + '-GPE_vs_SNR_const_syn_events.pkl')
nGPE_range=numpy.arange(N_GPE,4,-1)
# To maintain CONSTANT_SYN_EVENTS in to SNr while changing number of pausing
# GPe we have to increase the mean rate of the non-pausing GPe's
GPEmeanRates=CONSTANT_SYN_EVENTS/nGPE_range
SNRmeanRates=[]
sim_time=10000.
I_e=0.
if not load:
for r, n_gpe in zip(GPEmeanRates,nGPE_range):
my_nest.ResetKernel()
model_list=models()
my_nest.MyLoadModels( model_list, NEURON_MODELS )
my_nest.MyLoadModels( model_list, SYNAPSE_MODELS_TESTED )
my_nest.MyLoadModels( model_list, SYNAPSE_MODELS_BACKGROUND )
GPE = MyPoissonInput( n=n_gpe)
MSN = MyPoissonInput( n=N_MSN)
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),
sd=True,params={'I_e':I_e})
for id in GPE[:]:
GPE.set_spike_times(id=id, rates=[r], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
for id in MSN[:]:
MSN.set_spike_times(id=id, rates=[MSN_BASE_RATE], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
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(GPE[:],[SNR[i]], model=syn)
my_nest.ConvergentConnect(MSN[:],[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(5000,sim_time))
SNRmeanRates=numpy.array(SNRmeanRates).transpose()
GPEmeanRates=numpy.array(GPEmeanRates)
rateAtThr=''
for SNRr in SNRmeanRates:
tmp=str(GPEmeanRates[SNRr>=SELECTION_THR][-1])
rateAtThr+=' '+tmp[0:4]
s='\n'
s =s + 'GPE vs SNr rate:\n'
s = s + ' %s %5s %3s \n' % ( 'N GPEs:', str ( N_GPE ), '#' )
s = s + ' \n%s %5s %3s \n' % ( 'GPE rates:', str ( GPEmeanRates[0] ) + '-'+
str ( GPEmeanRates[-1] ), 'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Threshold SNr:', str ( SELECTION_THR ), 'spikes/s' )
s = s + ' \n%s %5s %3s \n' % ( 'GPE rate at 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' % ( 'I_e:', str ( I_e ), 'pA' )
s = s + ' %s %5s %3s \n' % ( 'Steady state rate ref:', str ( round(SNRmeanRates[0][0],1) ), 'pA' )
s = s + ' %s %5s %3s \n' % ( 'Steady state rate dyn:', str ( round(SNRmeanRates[1][0],1) ), 'pA' )
statusSynapse=[]
for syn in SYNAPSE_MODELS_TESTED:
statusSynapse.append( my_nest.GetDefaults(syn) )
for ss in statusSynapse:
s = s + '\n'
s = s + ' %s %10s\n' % ( 'Synapse', ss['synapsemodel'])
s = s + ' %s %5s %3s\n' % ( 'Weight',
str( round( ss['weight'], 1) ), 'nS')
infoString=s
misc.pickle_save([GPEmeanRates, SNRmeanRates, infoString],
save_at)
elif load:
GPEmeanRates, SNRmeanRates, infoString = misc.pickle_load(save_at)
return nGPE_range, GPEmeanRates, SNRmeanRates, infoString
def simulate_example(hz=20):
sname_nb = hz + 1000
saveAt = SPATH + '/' + SNAME + '-' + NEURONMODELS[0] + '-example.pkl'
nGPE = 100
EXPERIMENTS = range(20)
nSelected = 50
selectionRate = hz
baseRate = 20.
model_list = models()
I_e = -5.
simTime = 3500.
selectionTime = 500.
selectionOnset = 2500.
my_nest.ResetKernel()
my_nest.MyLoadModels(model_list, NEURONMODELS)
my_nest.MyLoadModels(model_list, SYNAPSE_MODELS)
GPE_list = [] # GPE input for each experiment
for iExp in EXPERIMENTS:
GPE_list.append(
MyPoissonInput(n=nGPE,
sd=True,
spath=SPATH,
sname_nb=sname_nb + iExp))
SNR_list = [] # SNR groups for each synapse
for iSyn, syn in enumerate(SYNAPSE_MODELS):
SNR_list.append(
MyGroup(NEURONMODELS[0],
n=len(EXPERIMENTS),
params={'I_e': I_e},
mm_dt=.1,
record_from=[''],
spath=SPATH,
sname_nb=sname_nb + iSyn))
if not LOAD:
for iExp in EXPERIMENTS:
GPE = GPE_list[iExp]
# Base rate
for id in GPE[:]:
GPE.set_spike_times(id=id,
rates=[baseRate],
times=[1],
t_stop=simTime)
# Selection
for id in GPE[:]:
GPE.set_spike_times(
id=id,
rates=[baseRate, selectionRate, baseRate],
times=[1, selectionOnset, selectionTime + selectionOnset],
t_stop=simTime)
for iSyn, syn in enumerate(SYNAPSE_MODELS):
target = SNR_list[iSyn][iExp]
my_nest.ConvergentConnect(GPE[:], [target], model=syn)
my_nest.MySimulate(simTime)
for GPE in GPE_list:
GPE.get_signal('s') # retrieve signal
for SNR in SNR_list:
SNR.get_signal('s') # retrieve signal
misc.pickle_save([GPE_list, SNR_list], saveAt)
if LOAD:
GPE_list, SNR_list = misc.pickle_load(saveAt)
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(baseRate), '#')
s = s + ' %s %5s %3s \n' % ('Selection rate:', str(selectionRate), '#')
return GPE_list, SNR_list, s
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_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_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_selection_vs_neurons(selRateInterval=[0.0, 500.0],
hz=0,
load=True):
global SEL_ONSET
global SNR_INJECTED_CURRENT
global N_GPE
global N_STN
global MSN_RATE_BASE
global GPE_BASE_RATE
global STN_BASE_RATE
SNAME_NB = hz
#n_exp=200
n_exp = 20
N_MAX_SEL = N_GPE + 1 # Plus one to account for the case when all GPe have stopped
RATE_SELE = hz
save_at = (SPATH + '/' + NEURON_MODELS[0] + '-' + str(SNAME_NB) +
'-hz.pkl')
#SEL_TIME = 1000.
sim_time = SEL_TIME + 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_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 + N_MAX_SEL)
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 = [] # STN 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 and number of selected GPE
I_e = my_nest.GetDefaults(NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
for i, i_syn in enumerate(SYNAPSE_MODELS_TESTED):
SNR = []
for i_sel in range(N_MAX_SEL):
SNR.append(
MyGroup(NEURON_MODELS[0],
n=n_exp,
params={'I_e': I_e},
sd=True,
sd_params={
'start': 0.,
'stop': sim_time
}))
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
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]:
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_GPE + N_MAX_SEL]:
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 in enumerate(SYNAPSE_MODELS_TESTED):
for i_sel in range(N_MAX_SEL):
target = SNR_list[i][i_sel][i_exp]
my_nest.ConvergentConnect(GPE[0:N_GPE - i_sel], [target],
model=syn)
my_nest.ConvergentConnect(GPE[N_GPE:N_GPE + i_sel],
[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 SNR_sel in SNR_list:
for SNR in SNR_sel:
SNR.get_signal('s')
misc.pickle_save_groups(SNR_list, save_at)
elif load:
SNR_list = misc.pickle_load_groups(save_at)
t1 = selRateInterval[0]
t2 = selRateInterval[1]
mean_rates = []
delay = my_nest.GetDefaults(SYNAPSE_MODELS_TESTED[0])['delay']
for SNR_sel in SNR_list:
m_r = []
for SNR in SNR_sel:
m_r_pre = SNR.signals['spikes'].mean_rate(SEL_ONSET - (t2 - t1),
SEL_ONSET)
m_r_post = SNR.signals['spikes'].mean_rate(SEL_ONSET + t1 + delay,
SEL_ONSET + t2 + delay)
m_r.append(m_r_post)
mean_rates.append(m_r)
mean_rates = numpy.array(mean_rates)
nb_neurons = numpy.arange(0, N_MAX_SEL, 1)
s = '\n'
s = s + ' %s %5s %3s \n' % ('N GPEs:', str(N_GPE), '#')
s = s + ' %s %5s %3s \n' % ('N experiments:', str(n_exp), '#')
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' % ('SNR_INJECTED_CURRENT:',
str(SNR_INJECTED_CURRENT), 'pA')
info_string = s
return nb_neurons, mean_rates, info_string
def simulate_MSN_vs_SNR_const_syn_events(load=True):
global SNR_INJECTED_CURRENT
global N_MSN
global N_GPE
global MSN_BURST_RATE
global GPE_BASE_RATE
# Path were raw data is saved. For example the spike trains.
save_result_at = OUTPUT_PATH + '/simulate_MSN_vs_SNR_const_syn_events.pkl'
save_header_at = OUTPUT_PATH + '/simulate_MSN_vs_SNR_const_syn_events_header'
# REMARK can not be more than rate=const_syn_events/burst_rate
n_MSN_bursting = numpy.arange(0, N_MAX_BURSTING + 1)
n_exp = 200
#n_exp=20
# Solve (500-n)*x + 20*n=600, where 500 is total number of MSNs, 20 is burst
# activation, x is MSN mean rate and n is number of bursters.
# Then x=(600-20*n)/(500-n)
MSNmeanRates = (SYN_EVENTS -
MSN_BURST_RATE * n_MSN_bursting) / (N_MSN - n_MSN_bursting)
SNRmeanRates = []
sim_time = 3000.
if not load:
for r, n_MSN_b in zip(MSNmeanRates, n_MSN_bursting):
my_nest.ResetKernel(threads=4)
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 = []
SNR = []
GPE = []
STN = []
for i in range(n_exp):
MSN.append(MyPoissonInput(n=N_MSN, sd=True))
GPE.append(MyPoissonInput(n=N_GPE, sd=True))
STN.append(MyPoissonInput(n=N_STN, sd=True))
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
SNR.append(
MyGroup(NEURON_MODELS[0],
n=len(SYNAPSE_MODELS_TESTED),
params={'I_e': I_e},
sd=True))
for i_exp in range(n_exp):
for id in MSN[i_exp][:N_MSN - n_MSN_b]:
MSN[i_exp].set_spike_times(id=id,
rates=numpy.array([r]),
times=numpy.array([1]),
t_stop=sim_time,
seed=int(numpy.random.random() *
10000.0))
for id in MSN[i_exp][N_MSN - n_MSN_b:]:
MSN[i_exp].set_spike_times(
id=id,
rates=numpy.array([r, MSN_BURST_RATE, r]),
times=numpy.array([1, SEL_ONSET, SEL_OFFSET]),
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Base rate GPE
for id in GPE[i_exp][:]:
GPE[i_exp].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[i_exp][:]:
STN[i_exp].set_spike_times(id=id,
rates=[STN_BASE_RATE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() *
10000.0))
for j, syn in enumerate(SYNAPSE_MODELS_TESTED):
my_nest.ConvergentConnect(MSN[i_exp][:], [SNR[i_exp][j]],
model=syn)
my_nest.ConvergentConnect(
GPE[i_exp][:], [SNR[i_exp][j]],
model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.ConvergentConnect(
STN[i_exp][:], [SNR[i_exp][j]],
model=SYNAPSE_MODELS_BACKGROUND[1])
my_nest.MySimulate(sim_time)
delay = my_nest.GetDefaults(SYNAPSE_MODELS_BACKGROUND[0])['delay']
SNRmeanRates_tmp = []
for i in range(n_exp):
SNR[i].get_signal('s') # retrieve signal
SNRmeanRates_tmp.append(SNR[i].signals['spikes'].mean_rates(
SEL_ONSET + delay, SEL_OFFSET + delay))
SNRmeanRates.append(numpy.mean(SNRmeanRates_tmp, axis=0))
SNRmeanRates = numpy.array(SNRmeanRates).transpose()
s = '\n'
s = s + 'simulate_MSN_vs_SNR_const_syn_events:\n'
s = s + '%s %5s %3s \n' % ('Syn events:', str(SYN_EVENTS), '#')
s = s + '%s %5s %3s \n' % ('n_exp:', str(n_exp), '#')
infoString = s
header = HEADER_SIMULATION_SETUP + s
misc.text_save(header, save_header_at)
misc.pickle_save([SNRmeanRates, infoString], save_result_at)
elif load:
SNRmeanRates, infoString = misc.pickle_load(save_result_at)
return n_MSN_bursting, MSNmeanRates, SNRmeanRates, infoString
def simulate_selection_vs_neurons(selection_intervals=[0.0, 500.0],
hz=20,
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=100
n_exp = 2
if hz > 7:
n_max_sel = 60
if hz > 20:
n_max_sel = 30
else:
n_max_sel = 100
RATE_BASE = 0.1
RATE_SELE = hz
save_result_at = (OUTPUT_PATH + '/' + FILE_NAME +
'-simulate_selection_vs_neurons' + str(hz) + '-hz.pkl')
save_header_at = (OUTPUT_PATH + '/' + FILE_NAME +
'-simulate_selection_vs_neurons' + str(hz) +
'-hz_header')
burst_time = 500.
sim_time = burst_time + 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_TESTED)
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_max_sel, sd=True)
MSN_list.append(MSN)
GPE_list = [] # GPE input for each experiment
for i_exp in EXPERIMENTS:
GPE = MyPoissonInput(n=N_GPE, sd=True)
GPE_list.append(GPE)
SNR_list = [] # SNR groups for each synapse and number of selected MSN
SNR_list_experiments = []
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
SNR = []
for i_sel in range(n_max_sel + 1): # Plus one to get no burst point
I_e = my_nest.GetDefaults(
NEURON_MODELS[0])['I_e'] + SNR_INJECTED_CURRENT
SNR.append(
MyGroup(NEURON_MODELS[0],
n=n_exp,
sd=True,
params={'I_e': I_e}))
SNR_list.append(SNR)
if not load:
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:N_MSN]:
MSN.set_spike_times(id=id,
rates=[RATE_BASE],
times=[1],
t_stop=sim_time,
seed=int(numpy.random.random() * 10000.0))
# Selection
for id in MSN[N_MSN:N_MSN + n_max_sel]:
rates = [RATE_BASE, RATE_SELE, RATE_BASE]
times = [1, SEL_ONSET, burst_time + 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))
# 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))
# Connect
for i_syn, syn in enumerate(SYNAPSE_MODELS_TESTED):
# i_sel goes over 0,..., n_max_sel
for i_sel in range(0, n_max_sel + 1):
target = SNR_list[i_syn][i_sel][i_exp]
my_nest.ConvergentConnect(MSN[0:N_MSN - i_sel], [target],
model=syn)
my_nest.ConvergentConnect(MSN[N_MSN:N_MSN + i_sel],
[target],
model=syn)
my_nest.ConvergentConnect(
GPE[:], [target], model=SYNAPSE_MODELS_BACKGROUND[0])
my_nest.MySimulate(sim_time)
for SNR_sel in SNR_list:
for SNR in SNR_sel:
SNR.get_signal('s')
sel_interval_mean_rates = []
sel_interval_mean_rates_std = []
for i_interval, interval in enumerate(selection_intervals):
t1 = selection_intervals[i_interval][0]
t2 = selection_intervals[i_interval][1]
mean_rates = []
mean_rates_std = []
# Time until arrival of spikes in SNr
delay = my_nest.GetDefaults(SYNAPSE_MODELS_BACKGROUND[0])['delay']
for SNR_sel in SNR_list:
m_r = []
m_r_std = []
for SNR in SNR_sel:
m_r.append(SNR.signals['spikes'].mean_rate(
SEL_ONSET + t1 + delay, SEL_ONSET + t2 + delay))
m_r_std.append(SNR.signals['spikes'].mean_rate_std(
SEL_ONSET + t1 + delay, SEL_ONSET + t2 + delay))
mean_rates.append(m_r)
mean_rates_std.append(m_r_std)
mean_rates = numpy.array(mean_rates)
mean_rates_std = numpy.array(mean_rates_std)
sel_interval_mean_rates.append(mean_rates)
sel_interval_mean_rates_std.append(mean_rates_std)
nb_neurons = numpy.arange(0, n_max_sel + 1, 1)
s = '\n'
s = s + ' %s %5s %3s \n' % ('N MSNs:', str(N_MSN), '#')
s = s + ' %s %5s %3s \n' % ('N experiments:', str(n_exp), '#')
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' % ('SNR_INJECTED_CURRENT:',
str(SNR_INJECTED_CURRENT), 'pA')
for i_interval, interval in enumerate(selection_intervals):
s = s + ' %s %5s %3s \n' % ('Sel interval ' + str(i_interval) +
':', str(selection_intervals), 'ms')
info_string = s
header = HEADER_SIMULATION_SETUP + s
misc.text_save(header, save_header_at)
misc.pickle_save([
nb_neurons, sel_interval_mean_rates, sel_interval_mean_rates_std,
info_string
], save_result_at)
elif load:
nb_neurons, sel_interval_mean_rates, sel_interval_mean_rates_std, info_string = misc.pickle_load(
save_result_at)
return nb_neurons, sel_interval_mean_rates, sel_interval_mean_rates_std, info_string
def simulate_GPE_vs_SNR_rate(load=True):
global N_GPE
global N_MSN
global N_STN
global MSN_BASE_RATE
# Path were data is saved. For example the spike trains.
save_at = (SPATH+'/'+NEURON_MODELS[0]+'-' + '-GPE_vs_SNR_rate.pkl')
GPEmeanRates=numpy.arange(0,150,1)
SNRmeanRates=[]
sim_time=50000.
I_e=0.
if not load:
for r in GPEmeanRates:
my_nest.ResetKernel()
model_list=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),
sd=True,params={'I_e':I_e})
for id in GPE[:]:
GPE.set_spike_times(id=id, rates=[r], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
for id in MSN[:]:
MSN.set_spike_times(id=id, rates=[MSN_BASE_RATE], times=[1],
t_stop=sim_time,
seed=int(numpy.random.random()*10000.0))
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(GPE[:],[SNR[i]], model=syn)
my_nest.ConvergentConnect(MSN[:],[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( 5000, sim_time))
SNRmeanRates=numpy.array(SNRmeanRates).transpose()
GPEmeanRates=numpy.array(GPEmeanRates)
rateAtThr=''
for SNRr in SNRmeanRates:
tmp=str(GPEmeanRates[SNRr>=SELECTION_THR][-1])
rateAtThr+=' '+tmp[0:4]
s='\n'
s =s + 'GPE vs SNr rate:\n'
s = s + ' %s %5s %3s \n' % ( 'N GPEs:', str ( N_GPE ), '#' )
s = s + ' %s %5s %3s \n' % ( 'Max SNr rate:', str ( SNRmeanRates[0] ), '#' )
s = s + ' \n%s \n%5s %3s \n' % ( 'GPE rates:', str ( GPEmeanRates[0] ) + '-'+
str ( GPEmeanRates[-1] ), 'spikes/s' )
s = s + ' %s %5s %3s \n' % ( 'Threshold SNr:', str ( SELECTION_THR ), 'spikes/s' )
s = s + ' \n%s \n%5s %3s \n' % ( 'GPE rate at 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' % ( 'I_e:', str ( I_e ), 'pA' )
infoString=s
misc.pickle_save([GPEmeanRates, SNRmeanRates, infoString],
save_at)
elif load:
GPEmeanRates, SNRmeanRates, infoString = misc.pickle_load(save_at)
return GPEmeanRates, SNRmeanRates, infoString