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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='instanteneousExc.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='instanteneousInh.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='instanteneousInh.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='instanteneousExc.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='instanteneousExc.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='instanteneousExc.json',
model_template='static_synapse')
lgn.build()
lgn.save_nodes(output_dir='network')
lgn.save_edges(output_dir='network')
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