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 ------------------------ ## ################################################################