int(200 / 0.1))
# ######################
# ## ----- Plot ----- ##
# ######################
# # # afferent excitation onto cortical excitation and inhibition
for i, tpop in enumerate(['RecExc', 'RecInh', 'DsInh'
]): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'AffExc',
t_array,
faff,
verbose=True)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='CellRep2019_data.h5')
print('Results of the simulation are stored as:',
'CellRep2019_data.h5')
print('--> Run \"python CellRep2019.py plot\" to plot the results')
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