def build_hippocampus():
net = NetworkBuilder('CA3')
net.add_nodes(N=1,
pop_name='CA3e',
model_type='biophysical',
model_template='hoc:Spikingcell',
morphology='blank.swc')
inputnet = NetworkBuilder('input')
inputnet.add_nodes(N=1, model_type='virtual', pat='pat1', pop_name='virt')
conn = inputnet.add_edges(
target=net.nodes(pop_name='CA3e'),
source={'pat': 'pat1'},
connection_rule=1,
dynamics_params='stsp.json',
model_template='Exp2Syn1_STSP',
delay=0,
syn_weight=.5,
target_sections=['soma'], # target soma
distance_range=[0.0, 300])
conn.add_properties(['sec_id', 'sec_x'],
rule=(0, 0.9),
dtypes=[np.int32, np.float])
net.build()
net.save_nodes(output_dir='network')
net.save_edges(output_dir='network')
inputnet.build()
inputnet.save(output_dir='network')
def build_net():
net = NetworkBuilder("slice")
net.add_nodes(
N=5,
pop_name='Scnn1a',
synapse_model='a',
firing_rate=firing_rate,
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
dynamics_params='472363762_fit.json',
morphology='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
rotation_angle_zaxis=-3.646878266,
model_processing='aibs_perisomatic,extracellular')
net.add_nodes(
N=5,
pop_name='Scnn1a',
synapse_model='b',
firing_rate=firing_rate,
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic,extracellular',
dynamics_params='472363762_fit.json',
morphology='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
rotation_angle_zaxis=-3.646878266)
net.build()
net.save_nodes(nodes_file_name='network/slice_nodes.h5',
node_types_file_name='network/slice_node_types.csv')
return net
def build_sim():
from bmtk.builder.networks import NetworkBuilder
# My all active-model (does not work):
#Model ID 496497595
#Cell ID 487667205
# Other perisomatic model (available on Allen Brain Institute - CellTypes):
#Model ID 491623973
#Cell ID 490387590
print('BMTK import success')
net = NetworkBuilder('mcortex')
print('Network builder initiated')
# Testing other models: # Surprise, surprise, this does not work...
net.add_nodes(cell_name='Pvalb_490387590_m',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='491623973_fit.json',
morphology='Pvalb_490387590_m.swc')
''' # Standard
net.add_nodes(cell_name='Scnn1a_473845048',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472363762_fit.json',
morphology='Scnn1a_473845048_m.swc')
'''
print('Node added')
net.build()
net.save_nodes(output_dir='network')
for node in net.nodes():
print(node)
print('Node printed')
from bmtk.utils.sim_setup import build_env_bionet
print('Setting environment')
build_env_bionet(
base_dir='sim_ch01', # Where to save the scripts and config files
network_dir='network', # Location of directory containing network files
tstop=1200.0,
dt=0.1, # Run a simulation for 2000 ms at 0.1 ms intervals
report_vars=[
'v'
], # Tells simulator we want to record membrane potential and calcium traces
current_clamp={ # Creates a step current from 500.ms to 1500.0 ms
'amp': 0.61, # 0.12#0.610
'delay': 100.0, # 100, #500
'duration': 1000.0
},
include_examples=True, # Copies components files
compile_mechanisms=True # Will try to compile NEURON mechanisms
)
print('Build done')
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')
def build_l4():
net = NetworkBuilder("V1/L4")
net.add_nodes(N=1, pop_name='DG_GC', node_type_id=478230220,
positions=[(28.753, -364.868, -161.705)],
#tuning_angle=[0.0, 25.0],
rotation_angle_yaxis=[3.55501],
#location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='478230220.json',
morphology_file='478230220.swc',
rotation_angle_zaxis=-3.646878266,
set_params_function='Biophys1')
net.build()
net.save_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
# net.save_edges(edges_file_name='output/v1_v1_edges.h5', edge_types_file_name='output/v1_v1_edge_types.csv')
return net
syn_weight=20.0e-03,
target_sections=['somatic'],
delay=2.0,
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
####################################################################################
########################## Build and save network ##################################
####################################################################################
print("\nBuilding network and saving to directory \"" + output_dir + "\"")
net.build()
Blad_aff_virt.build()
EUS_aff_virt.build()
PAG_aff_virt.build()
net.save_nodes(output_dir=output_dir)
net.save_edges(output_dir=output_dir)
Blad_aff_virt.save_nodes(output_dir=output_dir)
Blad_aff_virt.save_edges(output_dir=output_dir)
EUS_aff_virt.save_nodes(output_dir=output_dir)
EUS_aff_virt.save_edges(output_dir=output_dir)
PAG_aff_virt.save_nodes(output_dir=output_dir)
PAG_aff_virt.save_edges(output_dir=output_dir)
print("Done")
net1.add_edges(source={'cell_name': 'HCOCell1'}, target={'cell_name':'HCOCell2'},
connection_rule=1,
syn_weight=40.0e-02,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=[0,999])
net1.add_edges(source={'cell_name': 'HCOCell2'}, target={'cell_name':'HCOCell1'},
connection_rule=1,
syn_weight=40.0e-02,
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=0.0,
target_sections=["soma"],
distance_range=[0,999])
net1.build()
net1.save_nodes(output_dir='network')
net1.build()
net1.save_edges(output_dir='network')
#from bmtk.analyzer import node_types_table
#node_types_table(node_types_file='network/mcortex_node_types.csv', population='mcortex')
from bmtk.builder.networks import NetworkBuilder
cortex = NetworkBuilder('mcortex')
cortex.add_nodes(cell_name='Scnn1a',
potental='exc',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a.swc',
set_params_function='Biophys1')
cortex.build()
cortex.save_nodes(output_dir='network')
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=10,
pop_name='tON',
potential='exc',
level_of_detail='filter')
thalamus.add_edges(source={'pop_name': 'tON'},
target=cortex.nodes(),
connection_rule=5,
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')
thalamus.build()
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)
positions = positions_table[model_name]
xcoords += [p[0] for p in positions]
ycoords += [p[1] for p in positions]
tuning_angles = [calc_tuning_angle(o) for o in offset_table[model_name]]
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),
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(sources={'ei': 'e'}, targets={'name': 'Nr5a1'},
func=distance_tuning_connection_handler,
func_params=cparameters,
weight_max=7.2e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(sources={'ei': 'e'}, targets={'name': 'LIF_exc'},
func=distance_tuning_connection_handler,
func_params=cparameters,
weight_max=0.0019,
weight_function='gaussianLL',
weight_sigma=50.0,
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
net.build()
net.save_nodes('v1_nodes.h5', 'v1_node_types.csv')
net.save_edges('v1_edges.h5', '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_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)
def build_l4():
net = NetworkBuilder("V1/L4")
net.add_nodes(N=2, pop_name='Scnn1a',
positions=[(28.753, -364.868, -161.705), (48.753, -344.868, -141.705)],
tuning_angle=[0.0, 25.0],
rotation_angle_yaxis=[3.55501, 3.55501],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
rotation_angle_zaxis=-3.646878266,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='Rorb',
positions=[(241.092, -349.263, 146.916), (201.092, -399.263, 126.916)],
tuning_angle=[50.0, 75.0],
rotation_angle_yaxis=[3.50934, 3.50934],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='473863510_fit.json',
morphology_file='Rorb-IRES2-Cre-D_Ai14_IVSCC_-168053.05.01.01_325404214_m.swc',
rotation_angle_zaxis=-4.159763785,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='Nr5a1',
positions=[(320.498, -351.259, 20.273), (310.498, -371.259, 10.273)],
tuning_angle=[100.0, 125.0],
rotation_angle_yaxis=[0.72202, 0.72202],
location='VisL4',
ei='e',
level_of_detail='biophysical',
params_file='473863035_fit.json',
morphology_file='Nr5a1-Cre_Ai14_IVSCC_-169250.03.02.01_471087815_m.swc',
rotation_angle_zaxis=-2.639275277,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='PV1',
positions=[(122.373, -352.417, -216.748), (102.373, -342.417, -206.748)],
tuning_angle=['NA', 'NA'],
rotation_angle_yaxis=[2.92043, 2.92043],
location='VisL4',
ei='i',
level_of_detail='biophysical',
params_file='472912177_fit.json',
morphology_file='Pvalb-IRES-Cre_Ai14_IVSCC_-176847.04.02.01_470522102_m.swc',
rotation_angle_zaxis=-2.539551891,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='PV2',
positions=[(350.321, -372.535, -18.282), (360.321, -371.535, -12.282)],
tuning_angle=['NA', 'NA'],
rotation_angle_yaxis=[5.043336, 5.043336],
location='VisL4',
ei='i',
level_of_detail='biophysical',
params_file='473862421_fit.json',
morphology_file='Pvalb-IRES-Cre_Ai14_IVSCC_-169125.03.01.01_469628681_m.swc',
rotation_angle_zaxis=-3.684439949,
set_params_function='Biophys1')
net.add_nodes(N=2, pop_name='LIF_exc',
positions=[(-243.04, -342.352, -665.666), (-233.04, -332.352, -675.666)],
tuning_angle=['NA', 'NA'],
#rotation_angle_yaxis=[5.11801, 5.11801],
location='VisL4',
ei='e',
level_of_detail='intfire',
params_file='IntFire1_exc_1.json',
set_params_function='IntFire1')
net.add_nodes(N=2, pop_name='LIF_inh',
positions=[(211.04, -321.333, -631.593), (218.04, -327.333, -635.593)],
tuning_angle=[150.0, 175.0],
#rotation_angle_yaxis=[4.566091, 4.566091],
location='VisL4',
ei='i',
level_of_detail='intfire',
params_file='IntFire1_inh_1.json',
set_params_function='IntFire1')
print("Setting connections...")
print("Generating I-to-I connections.")
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'level_of_detail': 'biophysical'},
connection_rule=5,
weight_max=0.0002,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='GABA_InhToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'level_of_detail': 'intfire'},
connection_rule=5,
weight_max=0.00225,
weight_function='wmax',
delay=2.0,
params_file='instanteneousInh.json',
set_params_function='exp2syn')
print("Generating I-to-E connections.")
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'level_of_detail': 'biophysical'},
connection_rule=lambda trg, src: 5,
weight_max=0.00018,
weight_function='wmax',
distance_range=[0.0, 50.0],
target_sections=['somatic', 'basal', 'apical'],
delay=2.0,
params_file='GABA_InhToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'level_of_detail': 'intfire'},
connection_rule=5,
weight_max=0.009,
weight_function='wmax',
delay=2.0,
params_file='instanteneousInh.json',
set_params_function='exp2syn')
print("Generating E-to-I connections.")
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV1'},
connection_rule=5,
weight_max=0.00035,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'PV2'},
connection_rule=5,
weight_max=0.00027,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
params_file='AMPA_ExcToInh.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_inh'},
connection_rule=5,
weight_max=0.0043,
weight_function='wmax',
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
print("Generating E-to-E connections.")
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Scnn1a'},
connection_rule=5,
weight_max=6.4e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Rorb'},
connection_rule=5,
weight_max=5.5e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'Nr5a1'},
connection_rule=5,
weight_max=7.2e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
params_file='AMPA_ExcToExc.json',
set_params_function='exp2syn')
net.add_edges(source={'ei': 'e'}, target={'pop_name': 'LIF_exc'},
connection_rule=5,
weight_max=0.0019,
weight_function='gaussianLL',
weight_sigma=50.0,
delay=2.0,
params_file='instanteneousExc.json',
set_params_function='exp2syn')
net.build()
net.save_nodes(nodes_file_name='output/v1_nodes.h5', node_types_file_name='output/v1_node_types.csv')
net.save_edges(edges_file_name='output/v1_v1_edges.h5', edge_types_file_name='output/v1_v1_edge_types.csv')
assert(os.path.exists('output/v1_node_types.csv'))
node_types_csv = pd.read_csv('output/v1_node_types.csv', sep=' ')
assert(len(node_types_csv) == 7)
assert(set(node_types_csv.columns) == {'node_type_id', 'location', 'ei', 'level_of_detail', 'params_file',
'pop_name', 'set_params_function', 'rotation_angle_zaxis',
'morphology_file'})
assert(os.path.exists('output/v1_nodes.h5'))
nodes_h5 = h5py.File('output/v1_nodes.h5')
assert(len(nodes_h5['/nodes/node_gid']) == 14)
assert(len(nodes_h5['/nodes/node_type_id']) == 14)
assert(len(nodes_h5['/nodes/node_group']) == 14)
assert(len(nodes_h5['/nodes/node_group_index']) == 14)
assert(set(nodes_h5['/nodes/0'].keys()) == {'positions', 'rotation_angle_yaxis', 'tuning_angle'})
assert(len(nodes_h5['/nodes/0/positions']) == 10)
assert(len(nodes_h5['/nodes/1/positions']) == 4)
assert(os.path.exists('output/v1_v1_edge_types.csv'))
edge_types_csv = pd.read_csv('output/v1_v1_edge_types.csv', sep=' ')
assert(len(edge_types_csv) == 11)
assert(set(edge_types_csv.columns) == {'weight_max', 'edge_type_id', 'target_query', 'params_file',
'set_params_function', 'delay', 'target_sections', 'weight_function',
'weight_sigma', 'source_query', 'distance_range'})
assert(os.path.exists('output/v1_v1_edges.h5'))
edges_h5 = h5py.File('output/v1_v1_edges.h5')
assert(len(edges_h5['/edges/index_pointer']) == 14+1)
assert(len(edges_h5['/edges/target_gid']) == 14*14)
assert(len(edges_h5['/edges/source_gid']) == 14*14)
assert(len(edges_h5['/edges/0/nsyns']) == 14*14)
return net
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
conn = net.add_edges(source=net.nodes(pop_name='PMC'), target=net.nodes(pop_name='IND'),
connection_rule=one_to_one,
delay=0.5,
syn_weight = 0.033,
target_sections=['somatic'],
distance_range=[0.0, 300.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn')
net.build()
net.save_nodes(output_dir=output_dir)
net.save_edges(output_dir=output_dir)
#inp = NetworkBuilder('INPUT') # Virtual cells delivering input to PUD
#inp.add_nodes(N=1, pop_name = 'Pel_aff_virt', model_type='virtual', potential='exc')
#inp.add_edges(source=inp.nodes(pop_name='Pel_aff_virt'), target=net.nodes(pop_name='SPN'),
# connection_rule=1,
# syn_weight=0.06,
# target_sections=['somatic'],
# delay=0.5,
# distance_range=[0.0, 300.0],
# dynamics_params='AMPA_ExcToExc.json',
# model_template='Exp2Syn')
#
#inp.build()
#inp.save_nodes(output_dir=output_dir)
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')
import numpy as np
import random
cortex = NetworkBuilder('mcortex')
cortex.add_nodes(N=100,
pop_name='Scnn1a',
positions=positions_columinar(N=100, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=100, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=3.646878266,
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472363762_fit.json',
morphology='Scnn1a_473845048_m.swc')
cortex.add_edges(source={'pop_name': 'Scnn1a'}, target={'pop_name': 'Scnn1a'},
connection_rule=distance_connector,
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.34, 'd_max': 50.0, 'nsyn_min': 0, 'nsyn_max': 10},
syn_weight=2.0e-04,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical', 'soma'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
cortex.build()
cortex.save_nodes(output_dir='network')
cortex.save_edges(output_dir='network')
'd_weight_min': 0.0,
'd_weight_max': 0.34,
'd_max': 50.0,
'nsyn_min': 0,
'nsyn_max': 10
},
syn_weight=2.0e-04,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical', 'soma'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
# Build and save the network
cortex.build()
cortex.save_nodes(output_dir='%s/network' % (savedir))
##########################################
# External network
##########################################
# Add spike generating (thalamus) cells
'''We will also want a collection of external spike-generating cells that will synapse onto our cell. To do this we create
a second network which can represent thalamic input. We will call our network "mthalamus", and it will consist of 10 cells.
These cells are not biophysical but instead "virtual" cells. Virtual cells don't have a morphology or the normal properties
of a neuron, but rather act as spike generators'''
thalamus = NetworkBuilder('mthalamus')
thalamus.add_nodes(N=100,
pop_name='tON',
potential='exc',
model_type='virtual')