import numpy as np from bmtk.builder.networks import NetworkBuilder from bmtk.builder.auxi.node_params import positions_columinar from bmtk.builder.auxi.edge_connectors import distance_connector """Create Nodes""" net = NetworkBuilder("V1") net.add_nodes(N=80, # Create a population of 80 neurons positions=positions_columinar(N=80, center=[0, 50.0, 0], max_radius=30.0, height=100.0), pop_name='Scnn1a', location='VisL4', ei='e', # optional parameters model_type='point_process', # Tells the simulator to use point-based neurons model_template='nest:iaf_psc_alpha', # tells the simulator to use NEST iaf_psc_alpha models dynamics_params='472363762_point.json' # File containing iaf_psc_alpha mdoel parameters ) net.add_nodes(N=20, pop_name='PV', location='VisL4', ei='i', positions=positions_columinar(N=20, center=[0, 50.0, 0], max_radius=30.0, height=100.0), model_type='point_process', model_template='nest:iaf_psc_alpha', dynamics_params='472912177_point.json') net.add_nodes(N=200, pop_name='LIF_exc', location='L4', ei='e', positions=positions_columinar(N=200, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0), model_type='point_process', model_template='nest:iaf_psc_alpha', dynamics_params='IntFire1_exc_point.json') net.add_nodes(N=100, pop_name='LIF_inh', location='L4', ei='i', positions=positions_columinar(N=100, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0), model_type='point_process', model_template='nest:iaf_psc_alpha', dynamics_params='IntFire1_inh_point.json') """Create edges""" ## E-to-E connections net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Scnn1a'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=5.0, delay=2.0, dynamics_params='ExcToExc.json', model_template='static_synapse') net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=10.0, delay=2.0, dynamics_params='instantaneousExc.json', model_template='static_synapse') ### Generating I-to-I connections net.add_edges(source={'ei': 'i'}, target={'pop_name': 'PV'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=-1.0, delay=2.0, dynamics_params='InhToInh.json', model_template='static_synapse') net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'pop_name': 'LIF_inh'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=-1.0, delay=2.0, dynamics_params='instantaneousInh.json', model_template='static_synapse') ### Generating I-to-E connections net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'pop_name': 'Scnn1a'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=-15.0, delay=2.0, dynamics_params='InhToExc.json', model_template='static_synapse') net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'pop_name': 'LIF_exc'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=-15.0, delay=2.0, dynamics_params='instantaneousInh.json', model_template='static_synapse') ### Generating E-to-I connections net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=15.0, delay=2.0, dynamics_params='ExcToInh.json', model_template='static_synapse') net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'}, connection_rule=distance_connector, connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7}, syn_weight=5.0, delay=2.0, dynamics_params='instantaneousExc.json', model_template='static_synapse') net.build() net.save_nodes(output_dir='network') net.save_edges(output_dir='network') lgn = NetworkBuilder('LGN') lgn.add_nodes(N=500, pop_name='tON', potential='exc', model_type='virtual') def select_source_cells(sources, target, nsources_min=10, nsources_max=30, nsyns_min=3, nsyns_max=12): total_sources = len(sources) nsources = np.random.randint(nsources_min, nsources_max) selected_sources = np.random.choice(total_sources, nsources, replace=False) syns = np.zeros(total_sources) syns[selected_sources] = np.random.randint(nsyns_min, nsyns_max, size=nsources) return syns lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='Scnn1a'), iterator='all_to_one', connection_rule=select_source_cells, connection_params={'nsources_min': 10, 'nsources_max': 25}, syn_weight=20.0, delay=2.0, dynamics_params='ExcToExc.json', model_template='static_synapse') lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='PV1'), connection_rule=select_source_cells, connection_params={'nsources_min': 15, 'nsources_max': 30}, iterator='all_to_one', syn_weight=20.0, delay=2.0, dynamics_params='ExcToInh.json', model_template='static_synapse') lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_exc'), connection_rule=select_source_cells, connection_params={'nsources_min': 10, 'nsources_max': 25}, iterator='all_to_one', syn_weight=10.0, delay=2.0, dynamics_params='instantaneousExc.json', model_template='static_synapse') lgn.add_edges(source=lgn.nodes(), target=net.nodes(pop_name='LIF_inh'), connection_rule=select_source_cells, connection_params={'nsources_min': 15, 'nsources_max': 30}, iterator='all_to_one', syn_weight=10.0, delay=2.0, dynamics_params='instantaneousExc.json', model_template='static_synapse') lgn.build() lgn.save_nodes(output_dir='network') lgn.save_edges(output_dir='network')