def build_tw():
if not os.path.exists('output/network/TW'):
os.makedirs('output/network/TW')
TW = NetworkBuilder("TW")
TW.add_nodes(N=3000,
node_type_id='TW_001',
pop_name='TW',
ei='e',
location='TW',
level_of_detail='filter')
# Save cells.csv and cell_types.csv
TW.save_cells(
filename='output/network/TW/tw_nodes.csv',
columns=['node_id', 'node_type_id', 'pop_name', 'ei', 'location'])
TW.save_types(filename='output/network/TW/tw_node_types.csv',
columns=['node_type_id', 'level_of_detail'])
VL4 = NetworkBuilder.load("V1/L4",
nodes='output/network/VisL4/nodes.csv',
node_types='output/network/VisL4/node_types.csv')
VL4.connect(source=TW.nodes(),
target={'pop_name': 'Rorb'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 12.75,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'Scnn1a'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 33.25,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'Nr5a1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 19.0,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'PV1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 83.6,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'PV2'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 32.5,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'LIF_exc'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 22.5,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=TW.nodes(),
target={'pop_name': 'LIF_inh'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 55.0,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.build()
VL4.save_edge_types('output/network/TW/tw_edge_types.csv',
opt_columns=[
'weight_max', 'weight_function', 'delay',
'params_file', 'synapse_model'
])
VL4.save_edges(filename='output/network/TW/tw_edges.h5')
def build_lgn():
def generate_positions(N, x0=0.0, x1=300.0, y0=0.0, y1=100.0):
X = np.random.uniform(x0, x1, N)
Y = np.random.uniform(y0, y1, N)
return np.column_stack((X, Y))
def select_source_cells(src_cells, trg_cell, n_syns):
if trg_cell['tuning_angle'] is not None:
synapses = [
n_syns
if src['pop_name'] == 'tON' or src['pop_name'] == 'tOFF' else 0
for src in src_cells
]
else:
synapses = [
n_syns if src['pop_name'] == 'tONOFF' else 0
for src in src_cells
]
return synapses
if not os.path.exists('output/network/LGN'):
os.makedirs('output/network/LGN')
LGN = NetworkBuilder("LGN")
LGN.add_nodes(N=3000,
position='points',
position_params={'location': generate_positions(3000)},
node_type_id='tON_001',
location='LGN',
model_type='spike_generator',
pop_name='tON',
ei='e',
params_file='filter_point.json')
LGN.add_nodes(N=3000,
position='points',
position_params={'location': generate_positions(3000)},
node_type_id='tOFF_001',
location='LGN',
model_type='spike_generator',
pop_name='tOFF',
ei='e',
params_file='filter_point.json')
LGN.add_nodes(N=3000,
position='points',
position_params={'location': generate_positions(3000)},
node_type_id='tONOFF_001',
location='LGN',
model_type='spike_generator',
pop_name='tONOFF',
ei='e',
params_file='filter_point.json')
LGN.save_cells(filename='output/network/LGN/lgn_nodes.csv',
columns=['node_id', 'node_type_id', 'position'],
position_labels=['x', 'y'])
LGN.save_types(filename='output/network/LGN/lgn_node_types.csv',
columns=[
'node_type_id', 'ei', 'location', 'model_type',
'params_file'
])
VL4 = NetworkBuilder.load("V1/L4",
nodes='output/network/VisL4/nodes.csv',
node_types='output/network/VisL4/node_types.csv')
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'Rorb'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 4.125,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'Nr5a1'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 4.5,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'Scnn1a'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 5.6,
'weight_function': 'wmax',
'distance_range': [0.0, 150.0],
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'PV1'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 1.54,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'PV2'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 1.26,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'LIF_exc'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 4.41,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.connect(source=LGN.nodes(),
target={'pop_name': 'LIF_inh'},
iterator='all_to_one',
connector=select_source_cells,
connector_params={'n_syns': 10},
edge_params={
'weight_max': 2.52,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
VL4.build()
VL4.save_edge_types('output/network/LGN/lgn_edge_types.csv',
opt_columns=[
'weight_max', 'weight_function', 'delay',
'params_file', 'synapse_model'
])
VL4.save_edges(filename='output/network/LGN/lgn_edges.h5')