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',
'DsInh']): # both on excitation and inhibition
ntwk.construct_feedforward_input(
NTWK,
tpop,
'AffExc',
t_array,
0. * t_array + 0.3,
additional_spikes_in_terms_of_pre_pop={
'indices': NTWK['iRASTER_AffExc'],
'times': NTWK['tRASTER_AffExc']
},
SEED=i + 3,
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='visual_input_data.h5')
print('Results of the simulation are stored as:', 'visual_input_data.h5')
print('--> Run \"python visual_input.py plot\" to plot the results')
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')
# # noise excitation
for i, tpop in enumerate(REC_POPS): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'NoiseExc',
t_array,
fnoise + 0. * t_array,
verbose=True,
SEED=5)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
#####################
## ----- Save ----- ##
#####################
ntwk.write_as_hdf5(NTWK, filename='mean_field_data.h5')
print('Results of the simulation are stored as:', 'mean_field_data.h5')
print('--> Run \"python mean_field.py plot\" to plot the results')
print(
'--> Run \"python mean_field.py mf\" to run the associated MF and plot the results'
)
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 *
np.random.randn(NTWK['POPS'][0].N)) * ntwk.nS
NTWK['POPS'][0].GInhExc = abs(200 + 120 *
np.random.randn(NTWK['POPS'][0].N)) * ntwk.nS
# # then inhibition
NTWK['POPS'][1].GExcInh = abs(40 + 15 *
np.random.randn(NTWK['POPS'][1].N)) * ntwk.nS
NTWK['POPS'][1].GInhInh = abs(200 + 120 *
np.random.randn(NTWK['POPS'][1].N)) * ntwk.nS
# #####################
# ## ----- Run ----- ##
# #####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='Vogels-Abbott.h5')
print('Results of the simulation are stored as:', 'Vogels-Abbott.h5')
print('--> Run \"python coba_LIF.py plot\" to plot the results')
print('-------------------------------------------------------')
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']): # both on excitation and inhibition
ntwk.construct_feedforward_input_correlated(
NTWK,
tpop,
'AffExc',
t_array,
faff + 0. * t_array,
# with_presynaptic_spikes=True,
verbose=True,
SEED=int(37 * faff + i) % 37)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='with_correl_drive_data.h5')
print('Results of the simulation are stored as:',
'with_correl_drive_data.h5')
print('--> Run \"python with_correl_drive.py plot\" to plot the results')
#######################################
########### 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',
'DsInh']): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'AffExc',
t_array,
faff + 0. * t_array,
verbose=True,
SEED=int(37 * faff + i) % 37)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='3pop_model_data.h5')
print('Results of the simulation are stored as:', '3pop_model_data.h5')
print('--> Run \"python 3pop_model.py plot\" to plot the results')
#######################################
########### AFFERENT INPUTS ###########
#######################################
faff = 4.
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']): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'AffExc',
t_array,
faff + 0. * t_array,
verbose=True,
SEED=int(37 * faff + i) % 37)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='RS-FS.h5')
print('Results of the simulation are stored as:', 'RS-FS.h5')
print('--> Run \"python RS-FS.py plot\" to plot the results')
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
for i, tpop in enumerate(['L23Exc', 'PVInh', 'SOMInh',
'VIPInh']): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK,
tpop,
'L4Exc',
t_array,
faff,
verbose=True,
SEED=int(i) % 37)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='sinusoidal_input_data.h5')
print('Results of the simulation are stored as:',
'sinusoidal_input_data.h5')
print('--> Run \"python sinusoidal_input.py plot\" to plot the results')
#######################################
########### AFFERENT INPUTS ###########
#######################################
faff = 0.5
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', 'oscillExc']): # both on excitation and inhibition
ntwk.construct_feedforward_input(NTWK, tpop, 'AffExc',
t_array, faff+0.*t_array,
verbose=True,
SEED=int(37*faff+i)%37)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='rhythmic_ntwk_data.h5')
print('Results of the simulation are stored as:', 'rhythmic_ntwk_data.h5')
print('--> Run \"python rhythmic_ntwk.py plot\" to plot the results')
verbose=True)
ntwk.build_up_recurrent_connections(NTWK,
SEED=5,
verbose=True,
with_ring_geometry=True)
#######################################
########### AFFERENT INPUTS ###########
#######################################
t_array = ntwk.arange(int(Model['tstop'] / Model['dt'])) * Model['dt']
faff = 3. + 0 * t_array
ntwk.construct_feedforward_input(NTWK, 'Inh', 'AffExc', t_array, faff)
faff[(t_array > 400) & (t_array > 500)] += 2.
ntwk.construct_feedforward_input(NTWK, 'Exc', 'AffExc', t_array, faff)
################################################################
## --------------- Initial Condition ------------------------ ##
################################################################
ntwk.initialize_to_rest(NTWK)
#####################
## ----- Run ----- ##
#####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='ring_ntwk_data.h5')
print('Results of the simulation are stored as:', 'ring_ntwk_data.h5')
print('--> Run \"python ring_ntwk.py plot\" to plot the results')
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 *
np.random.randn(NTWK['POPS'][0].N)) * ntwk.nS
NTWK['POPS'][0].GInhExc = abs(200 + 120 *
np.random.randn(NTWK['POPS'][0].N)) * ntwk.nS
# # then inhibition
NTWK['POPS'][1].GExcInh = abs(40 + 15 *
np.random.randn(NTWK['POPS'][1].N)) * ntwk.nS
NTWK['POPS'][1].GInhInh = abs(200 + 120 *
np.random.randn(NTWK['POPS'][1].N)) * ntwk.nS
# #####################
# ## ----- Run ----- ##
# #####################
network_sim = ntwk.collect_and_run(NTWK, verbose=True)
ntwk.write_as_hdf5(NTWK, filename='coba_LIF_data.h5')
print('Results of the simulation are stored as:', 'coba_LIF_data.h5')
print('--> Run \"python coba_LIF.py plot\" to plot the results')
