def build_tw():
if not os.path.exists('output'):
os.makedirs('output')
TW = NetworkBuilder("TW")
TW.add_nodes(N=3000, pop_name='TW', ei='e', location='TW', level_of_detail='filter')
# Save cells.csv and cell_types.csv
TW.save_nodes(nodes_file_name='output/tw_nodes.h5', node_types_file_name='output/tw_node_types.csv')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Rorb'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00015, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Scnn1a'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00019, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'Nr5a1'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.00019, 'weight_function': 'wmax', 'distance_range': [30.0, 150.0],
'target_sections': ['basal', 'apical'], 'delay': 2.0, 'params_file': 'AMPA_ExcToExc.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'PV1'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.0022, 'weight_function': 'wmax', 'distance_range': [0.0, 1.0e+20],
'target_sections': ['somatic', 'basal'], 'delay': 2.0, 'params_file': 'AMPA_ExcToInh.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'PV2'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.0013, 'weight_function': 'wmax', 'distance_range': [0.0, 1.0e+20],
'target_sections': ['somatic', 'basal'], 'delay': 2.0, 'params_file': 'AMPA_ExcToInh.json',
'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'LIF_exc'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.015, 'weight_function': 'wmax', 'delay': 2.0,
'params_file': 'instanteneousExc.json', 'set_params_function': 'exp2syn'}))
VL4.add_edges(source=TW.nodes(), target={'pop_name': 'LIF_inh'},
connection_rule=lambda trg, src: 5,
**({'weight_max': 0.05, 'weight_function': 'wmax', 'delay': 2.0,
'params_file': 'instanteneousExc.json', 'set_params_function': 'exp2syn'}))
VL4.build()
VL4.save_edges(edges_file_name='output/tw_v1_edges.h5', edge_types_file_name='output/tw_v1_edge_types.csv')
lgn_net.add_nodes(model_params['N'],
position=positions,
tuning_angle=tuning_angles,
ei=model_params['ei'],
location=model_params['location'],
level_of_detail=model_params['level_of_detail'],
pop_name=model_params['pop_name'],
pop_id=model_params['pop_id'])
lgn_net.build()
lgn_net.save_nodes('lgn_nodes.h5', 'lgn_node_types.csv')
exit()
v1_net = NetworkBuilder('V1')
v1_net.import_nodes('v1_nodes.h5', 'v1_node_types.csv')
lgn_mean = (np.mean(xcoords), np.mean(ycoords))
lgn_dim = (140.0, 70.0)
print "> LGN mean center:"
# Determine the mean center of the CC cells
xcoords = [n['position'][0] for n in v1_net.nodes()]
ycoords = [n['position'][1] for n in v1_net.nodes()]
zcoords = [n['position'][2] for n in v1_net.nodes()]
l4_mean = (np.mean(xcoords), np.mean(ycoords), np.mean(zcoords))
l4_dim = (max(xcoords) - min(xcoords), max(ycoords) - min(ycoords),
max(zcoords) - min(zcoords))
print "> L4 mean center:", str(l4_mean)
cparams = {
def build_ext5_nodes():
if not os.path.exists('network'):
os.makedirs('network')
EXT = NetworkBuilder("EXT")
# need 5 cells to stimulate at 5 different frequencies
EXT.add_nodes(N=5,
pop_name='EXT',
model_type='virtual',
firing_rate=firing_rate)
# Save cells.csv and cell_types.csv
EXT.save_nodes(nodes_file_name='network/ext_nodes.h5',
node_types_file_name='network/ext_node_types.csv')
net = NetworkBuilder('slice')
net.import_nodes(nodes_file_name='network/slice_nodes.h5',
node_types_file_name='network/slice_node_types.csv')
net.add_edges(source=EXT.nodes(firing_rate=10),
target=net.nodes(firing_rate=10, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=20),
target=net.nodes(firing_rate=20, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=50),
target=net.nodes(firing_rate=50, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=100),
target=net.nodes(firing_rate=100, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=200),
target=net.nodes(firing_rate=200, synapse_model='a'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'AMPA_ExcToExc.json',
'model_template': 'expsyn'
}))
net.add_edges(source=EXT.nodes(firing_rate=10),
target=net.nodes(firing_rate=10, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=20),
target=net.nodes(firing_rate=20, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=50),
target=net.nodes(firing_rate=50, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=100),
target=net.nodes(firing_rate=100, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.add_edges(source=EXT.nodes(firing_rate=200),
target=net.nodes(firing_rate=200, synapse_model='b'),
connection_rule=5,
**({
'syn_weight': 0.002,
'weight_function': 'wmax',
'distance_range': [0.0, 50.0],
'target_sections': ['somatic', 'basal', 'apical'],
'delay': 2.0,
'dynamics_params': 'pvalb_pvalb.json',
'model_template': 'stp2syn'
}))
net.build()
net.save_edges(edges_file_name='network/ext_to_slice_edges.h5',
edge_types_file_name='network/ext_to_slice_edge_types.csv')
#print np.random.geometric(p=0.5)
#print np.sqrt(1-0.005)/0.005
"""
def connect_random(source, target, nsyn_min=0, nsyn_max=10, distribution=None):
return np.random.randint(nsyn_min, nsyn_max)
"""
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=100,
pop_name='tON',
potential='exc',
level_of_detail='filter')
cortex = NetworkBuilder('mcortex')
cortex.import_nodes(nodes_file_name='network/mcortex_nodes.h5',
node_types_file_name='network/mcortex_node_types.csv')
thalamus.add_edges(source=thalamus.nodes(),
target=cortex.nodes(),
connection_rule=connect_random,
connection_params={
'nsyn_min': 0,
'nsyn_max': 12
},
syn_weight=1.0e-04,
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
thalamus.build()
import numpy as np
from bmtk.builder.networks import NetworkBuilder
lgn = NetworkBuilder('LGN')
lgn.add_nodes(N=500, pop_name='tON', potential='exc', level_of_detail='filter')
v1 = NetworkBuilder('V1')
v1.import_nodes(nodes_file_name='network/V1_nodes.h5',
node_types_file_name='network/V1_node_types.csv')
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',
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_cell, trg_cell, n_syns_exc, n_syns_inh):
#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]
# will always give synapse number depending on type
if n_syns_exc > 0:
n_synapses = n_syns_exc
if n_syns_inh > 0:
n_synapses = n_syns_inh
return n_synapses
# varible number of synapses
#return np.random.randint(n_syns_min, n_syns_max)
#return synapses
if not os.path.exists('output'):
os.makedirs('output')
LGN = NetworkBuilder("LGN")
LGN.add_nodes(N=1000,
positions=generate_positions(1000),
location='LGN',
level_of_detail='filter',
pop_name='ExcIN',
ei='e')
LGN.add_nodes(N=1,
positions=generate_positions(1),
location='LGN',
level_of_detail='filter',
pop_name='InhIN',
ei='i')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
cm = VL4.add_edges(source=LGN.nodes(ei='e'), #target={'pop_name': 'DG_GC'},
connection_rule=select_source_cells,
connection_params={'n_syns_exc': 1, 'n_syns_inh': 0},
weight_max=10e-03,
weight_function='wmax',
distance_range=[1, 1e+20],
target_sections=['apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.build()
LGN.save_nodes(nodes_file_name='output/lgn_nodes.h5', node_types_file_name='output/lgn_node_types.csv')
VL4.save_edges(edges_file_name='output/lgn_v1_edges.h5', edge_types_file_name='output/lgn_v1_edge_types.csv')
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'):
os.makedirs('output')
LGN = NetworkBuilder("LGN")
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tON',
ei='e')
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tOFF',
ei='e')
LGN.add_nodes(N=3000,
positions=generate_positions(3000),
location='LGN',
level_of_detail='filter',
pop_name='tONOFF',
ei='e')
VL4 = NetworkBuilder('V1/L4')
VL4.import_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Rorb'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Nr5a1'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=5e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'Scnn1a'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=4e-05,
weight_function='wmax',
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'PV1'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.0001,
weight_function='wmax',
distance_range=[0.0, 1.0e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'PV2'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=9e-05,
weight_function='wmax',
distance_range=[0.0, 1.0e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'LIF_exc'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.0045,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
VL4.add_edges(source=LGN.nodes(), target={'pop_name': 'LIF_inh'},
iterator='all_to_one',
connection_rule=select_source_cells,
connection_params={'n_syns': 10},
weight_max=0.002,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
VL4.build()
LGN.save_nodes(nodes_file_name='output/lgn_nodes.h5', node_types_file_name='output/lgn_node_types.csv')
VL4.save_edges(edges_file_name='output/lgn_v1_edges.h5', edge_types_file_name='output/lgn_v1_edge_types.csv')
assert(os.path.exists('output/lgn_node_types.csv'))
node_types_csv = pd.read_csv('output/lgn_node_types.csv', sep=' ')
assert(len(node_types_csv) == 3)
assert(set(node_types_csv.columns) == {'node_type_id', 'location', 'ei', 'level_of_detail', 'pop_name'})
assert(os.path.exists('output/lgn_nodes.h5'))
nodes_h5 = h5py.File('output/lgn_nodes.h5')
assert(len(nodes_h5['/nodes/node_gid']) == 9000)
assert(len(nodes_h5['/nodes/node_type_id']) == 9000)
assert(len(nodes_h5['/nodes/node_group']) == 9000)
assert(len(nodes_h5['/nodes/node_group_index']) == 9000)
assert(set(nodes_h5['/nodes/0'].keys()) == {'positions'})
assert(len(nodes_h5['/nodes/0/positions']) == 9000)
assert(os.path.exists('output/lgn_v1_edge_types.csv'))
edge_types_csv = pd.read_csv('output/lgn_v1_edge_types.csv', sep=' ')
assert(len(edge_types_csv) == 7)
assert(set(edge_types_csv.columns) == {'weight_max', 'edge_type_id', 'target_query', 'params_file',
'set_params_function', 'delay', 'target_sections', 'weight_function',
'source_query', 'distance_range'})
assert(os.path.exists('output/lgn_v1_edges.h5'))
edges_h5 = h5py.File('output/lgn_v1_edges.h5')
assert(len(edges_h5['/edges/index_pointer']) == 14+1)
assert(len(edges_h5['/edges/target_gid']) == 6000*14)
assert(len(edges_h5['/edges/source_gid']) == 6000*14)
assert(len(edges_h5['/edges/0/nsyns']) == 6000*14)