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')