'dynamics_params': 'IntFire1_exc_1.json' }, { 'model_name': 'LIF_inh', 'ei': 'i', 'dynamics_params': 'IntFire1_inh_1.json' }] # Build a network of 300 biophysical cells to simulate internal = NetworkBuilder("internal") for i, model_props in enumerate(bio_models): n_cells = 80 if model_props[ 'ei'] == 'e' else 30 # 80% excitatory, 20% inhib # Randomly get positions uniformly distributed in a column positions = positions_columinar(N=n_cells, center=[0, 10.0, 0], max_radius=50.0, height=200.0) internal.add_nodes( N=n_cells, x=positions[:, 0], y=positions[:, 1], z=positions[:, 2], rotation_angle_yaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi), # randomly rotate y axis rotation_angle_zaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi), # model_type='biophysical',
from bmtk.builder.networks import NetworkBuilder from bmtk.builder.aux.node_params import positions_columinar, xiter_random from bmtk.builder.aux.edge_connectors import distance_connector import math import numpy as np import random cortex = NetworkBuilder('mcortex') cortex.add_nodes(N=100, pop_name='Scnn1a', positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0), rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi), rotation_angle_zaxis=3.646878266, potental='exc', model_type='biophysical', model_template='ctdb:Biophys1.hoc', model_processing='aibs_perisomatic', dynamics_params='472363762_fit.json', morphology='Scnn1a_473845048_m.swc') cortex.add_edges(source={'pop_name': 'Scnn1a'}, target={'pop_name': 'Scnn1a'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 50.0, 'nsyn_min': 0, 'nsyn_max': 10}, syn_weight=2.0e-04, distance_range=[30.0, 150.0], target_sections=['basal', 'apical', 'soma'], delay=2.0, dynamics_params='AMPA_ExcToExc.json', model_template='exp2syn')
}, 'LIF_inh': { 'N': 40, 'ei': 'i', 'pop_name': 'LIF_inh', 'model_type': 'point_process', 'model_template': 'nest:iaf_psc_delta', 'dynamics_params': 'iaf_psc_delta_inh.json' } } net = NetworkBuilder('cortex') for model in LIF_models: params = LIF_models[model].copy() positions = positions_columinar(N=LIF_models[model]['N'], center=[0, 10.0, 0], max_radius=50.0, height=200.0) net.add_nodes(x=positions[:, 0], y=positions[:, 1], z=positions[:, 2], **params) net.add_edges(source={'ei': 'e'}, connection_rule=random_connections, connection_params={'p': 0.1}, syn_weight=2.0, delay=1.5, dynamics_params='ExcToInh.json', model_template='static_synapse') net.add_edges(source={'ei': 'i'},