if sys.argv[-1] == 'plot':
## load file
data = ntwk.load_dict_from_hdf5('CellRep2019_data.h5')
print(data)
# ## plot
fig, _ = ntwk.raster_and_Vm_plot(data, smooth_population_activity=10.)
ntwk.show()
else:
import numpy as np
from analyz.processing.signanalysis import smooth
NTWK = ntwk.build_populations(Model, ['RecExc', 'RecInh', 'DsInh'],
AFFERENT_POPULATIONS=['AffExc'],
with_raster=True,
with_Vm=4,
verbose=True)
ntwk.build_up_recurrent_connections(NTWK, SEED=5, verbose=True)
#######################################
########### AFFERENT INPUTS ###########
#######################################
t_array = ntwk.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
faff = smooth(np.array([4 * int(tt / 1000) for tt in t_array]),
int(200 / 0.1))
# ######################
# ## ----- Plot ----- ##
def run_sim(Model,
with_Vm=0,
with_synaptic_currents=False,
firing_rate_only=False,
tdiscard=100):
if 'RATES' in Model.keys():
RATES = Model['RATES']
else:
RATES = {}
for pop in Model['POP_STIM']:
RATES['F_' + pop] = Model['F_' + pop]
if tdiscard >= Model['tstop']:
print('discard time higher than simulation time -> set to 0')
tdiscard = 0
t_array = np.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
############################################################################
# everything is reformatted to have it compatible with the network framework
############################################################################
aff_pops_discard_self = []
for p in Model['POP_STIM']:
if p != Model['NRN_KEY']:
aff_pops_discard_self.append(p)
# note that number of neurons become number of different seeds
NTWK = ntwk.build_populations(
Model, [Model['NRN_KEY']],
NEURONS=[{
'name': Model['NRN_KEY'],
'N': Model['N_SEED']
}],
AFFERENT_POPULATIONS=aff_pops_discard_self,
with_Vm=with_Vm,
with_raster=True,
with_synaptic_currents=with_synaptic_currents)
ntwk.initialize_to_rest(
NTWK) # (fully quiescent State as initial conditions)
SPKS, SYNAPSES, PRESPKS = [], [], []
for i, afferent_pop in enumerate(Model['POP_STIM']):
rate_array = RATES['F_' + afferent_pop] + 0. * t_array
ntwk.construct_feedforward_input(NTWK,
Model['NRN_KEY'],
afferent_pop,
t_array,
rate_array,
SEED=i + Model['SEED'])
sim = ntwk.collect_and_run(NTWK)
# calculating firing rate
vec = np.zeros(Model['N_SEED'])
ispikes = np.array(NTWK['RASTER'][0].i)
tspikes = np.array(NTWK['RASTER'][0].t / ntwk.ms)
for nrn in range(Model['N_SEED']):
i0 = np.argwhere(ispikes == nrn).flatten()
ts = tspikes[i0]
fout = 1e3 * len(ts[ts > tdiscard]) / (Model['tstop'] - tdiscard
) # from ms to Hz
vec[nrn] = fout
if firing_rate_only:
return vec.mean(), vec.std()
else:
output = {
'ispikes': np.array(NTWK['RASTER'][0].i),
'tspikes': np.array(NTWK['RASTER'][0].t / ntwk.ms),
'Model': Model,
'fout_mean': vec.mean(),
'fout_std': vec.std()
}
if with_Vm:
output['i_prespikes'] = NTWK['iRASTER_PRE']
output['t_prespikes'] = [
vv / ntwk.ms for vv in NTWK['tRASTER_PRE']
]
output['Vm'] = np.array([vv.V / ntwk.mV for vv in NTWK['VMS'][0]])
if with_synaptic_currents:
output['Ie'] = np.array(
[vv.Ie / ntwk.pA for vv in NTWK['ISYNe'][0]])
output['Ii'] = np.array(
[vv.Ii / ntwk.pA for vv in NTWK['ISYNi'][0]])
return output
## plot
fig, AX = plt.subplots(2)
AX[0].plot(data['tRASTER_Exc'], data['iRASTER_Exc'], 'bo', ms=2)
AX[0].plot(data['tRASTER_Inh'], -data['iRASTER_Inh'], 'ro', ms=2)
ntwk.set_plot(AX[0], [], xticks=[], yticks=[])
for v in data['VMS_Exc']:
AX[1].plot(np.arange(len(v)) * data['dt'], v, 'k-', lw=1)
ntwk.set_plot(AX[1], xlabel='time (ms)', ylabel='Vm (mV)')
ntwk.show()
else:
## we build and run the simulation
NTWK = ntwk.build_populations(
Model,
['Exc', 'Inh'],
with_raster=True,
with_Vm=4,
# with_synaptic_currents=True,
# with_synaptic_conductances=True,
verbose=True)
ntwk.build_up_recurrent_connections(NTWK, SEED=5, verbose=True)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
for i in range(2):
NTWK['POPS'][i].V = (-65 + 5 * np.random.randn(NTWK['POPS'][i].N)
) * ntwk.mV # random Vm
# then excitation
NTWK['POPS'][0].GExcExc = abs(40 + 15 *
######################
t_array = ntwk.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
faff = smooth(np.array([4 * int(tt / 1000) for tt in t_array]),
int(200 / 0.1))
fnoise = 3.
#######################################
########### BUILD POPS ################
#######################################
NTWK = ntwk.build_populations(Model,
REC_POPS,
AFFERENT_POPULATIONS=AFF_POPS,
with_pop_act=True,
with_raster=True,
with_Vm=4,
verbose=True)
ntwk.build_up_recurrent_connections(NTWK, SEED=5, verbose=True)
#######################################
########### AFFERENT INPUTS ###########
#######################################
# time-dep afferent excitation
for i, tpop in enumerate(REC_POPS): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'AffExc',
# ## ----- Plot ----- ##
# ######################
## load file
data = ntwk.load_dict_from_hdf5('3pop_model_data.h5')
# ## plot
fig, _ = ntwk.activity_plots(data, smooth_population_activity=10.)
plt.show()
else:
NTWK = ntwk.build_populations(
Model,
['Exc', 'Inh', 'DsInh'],
AFFERENT_POPULATIONS=['AffExc'],
with_raster=True,
with_Vm=4,
# with_synaptic_currents=True,
# with_synaptic_conductances=True,
verbose=True)
ntwk.build_up_recurrent_connections(NTWK, SEED=5, verbose=True)
#######################################
########### AFFERENT INPUTS ###########
#######################################
faff = 1.
t_array = ntwk.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
# # # afferent excitation onto cortical excitation and inhibition
for i, tpop in enumerate(['Exc', 'Inh',
## load file
data = ntwk.load_dict_from_hdf5('sinusoidal_input_data.h5')
data['iRASTER_L4Exc'] = data['iRASTER_PRE1']
data['tRASTER_L4Exc'] = data['tRASTER_PRE1']
# # ## plot
fig, _ = ntwk.activity_plots(
data,
POP_KEYS=['L23Exc', 'PVInh', 'SOMInh', 'VIPInh'],
COLORS=['green', 'red', 'orange', 'purple'],
smooth_population_activity=10.)
plt.show()
else:
NTWK = ntwk.build_populations(Model,
['L23Exc', 'PVInh', 'SOMInh', 'VIPInh'],
AFFERENT_POPULATIONS=['L4Exc'],
with_raster=True,
with_Vm=4,
with_pop_act=True,
verbose=True)
ntwk.build_up_recurrent_connections(NTWK, SEED=5, verbose=True)
#######################################
########### AFFERENT INPUTS ###########
#######################################
t_array = ntwk.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
faff = 0.8 + 0.7 * (1 - np.cos(2 * np.pi * 4e-3 * t_array))
faff[t_array < 750] = 1.
# # # afferent excitation onto cortical excitation and inhibition