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 ----- ##
# ######################
# # # afferent excitation onto cortical excitation and inhibition
for i, tpop in enumerate(['RecExc', 'RecInh', 'DsInh'
plt.show()
else:
NTWK = ntwk.build_populations(
Model,
['Exc', 'Inh'],
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,
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 ------------------------ ##
################################################################