def build_lgn():
if not os.path.exists('output/network/LGN'):
os.makedirs('output/network/LGN')
net = NetworkBuilder("LGN")
net.add_nodes(N=3000, node_type_id='tON_001', ei='e', location='LGN', pop_name='tON_001', params_file='filter_pop.json')
net.add_nodes(N=3000, node_type_id='tOFF_001', ei='e', location='LGN', pop_name='tOFF_001', params_file='filter_pop.json')
net.add_nodes(N=3000, node_type_id='tONOFF_001', ei='e', location='LGN', pop_name='tONOFF_001',
params_file='filter_pop.json')
net.save_cells(filename='output/network/LGN/nodes.csv',
columns=['node_id', 'node_type_id'])
net.save_types(filename='output/network/LGN/node_types.csv',
columns=['node_type_id', 'ei', 'location', 'pop_name', 'params_file'])
net.connect(target={'pop_name': 'excitatory'},
edge_params={'weight': 0.0015, 'delay': 0.002, 'params_file': 'ExcToExc.json', 'nsyns': 10})
net.connect(target={'pop_name': 'inhibitory'},
edge_params={'weight': 0.0019, 'delay': 0.002, 'params_file': 'ExcToInh.json', 'nsyns': 12})
net.save_edge_types('output/network/LGN/edge_types.csv',
opt_columns=['weight', 'delay', 'nsyns', 'params_file'])
def build_l4():
if not os.path.exists('output/network/VisL4'):
os.makedirs('output/network/VisL4')
net = NetworkBuilder("V1/L4")
net.add_nodes(N=2,
pop_name='Scnn1a',
node_type_id='395830185',
position='points',
position_params={
'location': [(28.753, -364.868, -161.705),
(48.753, -344.868, -141.705)]
},
array_params={"tuning_angle": [0.0, 25.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='472363762_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='Rorb',
node_type_id='314804042',
position='points',
position_params={
'location': [(241.092, -349.263, 146.916),
(201.092, -399.263, 126.916)]
},
array_params={"tuning_angle": [50.0, 75.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='473863510_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='Nr5a1',
node_type_id='318808427',
position='points',
position_params={
'location': [(320.498, -351.259, 20.273),
(310.498, -371.259, 10.273)]
},
array_params={"tuning_angle": [100.0, 125.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='473863035_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='PV1',
node_type_id='330080937',
position='points',
position_params={
'location': [(122.373, -352.417, -216.748),
(102.373, -342.417, -206.748)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='i',
gaba_synapse='y',
params_file='472912177_fit.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='PV2',
node_type_id='318331342',
position='points',
position_params={
'location': [(350.321, -372.535, -18.282),
(360.321, -371.535, -12.282)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='i',
gaba_synapse='y',
params_file='473862421_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='LIF_exc',
node_type_id='100000101',
position='points',
position_params={
'location': [(-243.04, -342.352, -665.666),
(-233.04, -332.352, -675.666)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='e',
gaba_synapse='n',
params_file='IntFire1_exc_1.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='LIF_inh',
node_type_id='100000102',
position='points',
position_params={
'location': [(211.04, -321.333, -631.593),
(218.04, -327.333, -635.593)]
},
array_params={'tuning_angle': [150.0, 175.0]},
location='VisL4',
ei='i',
gaba_synapse='n',
params_file='IntFire1_inh_1.json',
model_type='iaf_psc_alpha')
print("Setting connections...")
net.connect(source={'ei': 'i'},
target={
'ei': 'i',
'gaba_synapse': 'y'
},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -1.8,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={
'ei': 'e',
'gaba_synapse': 'y'
},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -12.6,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={'pop_name': 'LIF_inh'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -1.125,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={'pop_name': 'LIF_exc'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -6.3,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'PV1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 7.7,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'PV2'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 5.4,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'LIF_inh'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 3.44,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
print("Generating E-to-E connections.")
net.connect(source={'ei': 'e'},
target={'pop_name': 'Scnn1a'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 8.448,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'Rorb'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 4.292,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'Nr5a1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 5.184,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'LIF_exc'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 1.995,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.build()
net.save_cells(
filename='output/network/VisL4/nodes.csv',
columns=['node_id', 'node_type_id', 'position', 'tuning_angle'],
position_labels=['x', 'y', 'z'])
net.save_types(filename='output/network/VisL4/node_types.csv',
columns=[
'node_type_id', 'pop_name', 'ei', 'gaba_synapse',
'location', 'model_type', 'params_file'
])
net.save_edge_types('output/network/VisL4/edge_types.csv',
opt_columns=[
'weight_max', 'weight_function', 'weight_sigma',
'delay', 'params_file', 'synapse_model'
])
net.save_edges(filename='output/network/VisL4/edges.h5')
return net
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')