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_input_network(net, output_dir='network/source_input'):
def select_source_cells(sources, target, N_syn=10):
""" Note here that "sources" are given (not "source"). So the iterations occur through every target
with all sources as potential inputs. Faster than before and better if will have common rules.
"""
target_id = target.node_id
source_ids = [s.node_id for s in sources]
nsyns_ret = [N_syn] * len(source_ids)
return nsyns_ret
filter_models = {
'inputFilter': {
'N': 25,
'ei': 'e',
'pop_name': 'input_filter',
'model_type': 'virtual'
}
}
inputNetwork = NetworkBuilder("inputNetwork")
inputNetwork.add_nodes(**filter_models['inputFilter'])
inputNetwork.add_edges(target=net.nodes(pop_name='Scnn1a'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.0007,
distance_range=[0.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
inputNetwork.add_edges(target=net.nodes(pop_name='LIF_exc'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.07,
delay=2.0,
dynamics_params='instanteneousExc.json')
inputNetwork.add_edges(target=net.nodes(pop_name='PV1'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.002,
distance_range=[0.0, 1.0e+20],
target_sections=['basal', 'somatic'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
inputNetwork.add_edges(target=net.nodes(pop_name='LIF_inh'),
iterator='all_to_one',
connection_rule=select_source_cells,
syn_weight=0.01,
delay=2.0,
dynamics_params='instanteneousExc.json')
inputNetwork.build()
inputNetwork.save(output_dir=output_dir)
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():
if not os.path.exists('output/network/VisL4'):
os.makedirs('output/network/VisL4')
net = NetworkBuilder("V1/L4")
net.add_nodes(node_type_id=0, pop_name='excitatory', params_file='excitatory_pop.json')
net.add_nodes(node_type_id=1, pop_name='inhibitory', params_file='inhibitory_pop.json')
net.connect(target={'pop_name': 'excitatory'}, source={'pop_name': 'inhibitory'},
edge_params={'weight': -0.001, 'delay': 0.002, 'nsyns': 2, 'params_file': 'ExcToInh.json'})
net.connect(target={'pop_name': 'inhibitory'}, source={'pop_name': 'excitatory'},
edge_params={'weight': 0.001, 'delay': 0.002, 'nsyns': 5, 'params_file': 'ExcToInh.json'})
net.save_types(filename='output/network/VisL4/node_types.csv',
columns=['node_type_id', 'pop_name', 'params_file'])
net.save_edge_types('output/network/VisL4/edge_types.csv',
opt_columns=['weight', 'delay', 'nsyns', 'params_file'])
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
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'])
import numpy as np
import math
import random
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.aux.node_params import positions_columinar, xiter_random
from bmtk.builder.aux.edge_connectors import distance_connector
net = NetworkBuilder("V1")
net.add_nodes(N=80, pop_name='Scnn1a',
positions=positions_columinar(N=80, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=xiter_random(N=80, min_x=0.0, max_x=2*np.pi),
tuning_angle=np.linspace(start=0.0, stop=360.0, num=80, endpoint=False),
location='L4',
ei='e',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a.swc',
set_params_function='Biophys1')
net.add_nodes(N=20, pop_name='PV',
positions=positions_columinar(N=20, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
rotation_angle_yaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),
rotation_angle_zaxis=xiter_random(N=20, min_x=0.0, max_x=2*np.pi),
location='L4',
ei='i',
level_of_detail='biophysical',
params_file='472912177_fit.json',
morphology_file='Pvalb.swc',
set_params_function='Biophys1')
phi_index = 0
net = NetworkBuilder("DG")
net.add_nodes(
N=N_GC_1, # specifiy the number of cells belong to said group.
pop_name='GC',
location='Granule_cell_layer',
ei=
'e', # pop_name, location, and ei are optional parameters that help's identifies properties of the cells. The modeler can choose whatever key-value pairs as they deem appropiate.
positions=generate_arc_positions_3D(
phi_GC[phi_index:N_GC_1], rho_GC[phi_index:N_GC_1]
), # The following properties we are passing in lists # of size N. Doing so will uniquely assign different
rotation_angle_xaxis=generate_rotation_angle_1D(rho_GC[phi_index:N_GC_1],
3.130742385893708),
rotation_angle_yaxis=generate_rotation_angle_1D(rho_GC[phi_index:N_GC_1],
0),
rotation_angle_zaxis=generate_rotation_angle_1D(rho_GC[phi_index:N_GC_1],
-0.524013338073954),
phi=phi_GC[phi_index:N_GC_1],
rho=rho_GC[phi_index:N_GC_1],
model_type='biophysical', # The type of cell we are using
model_template=
'ctdb:Biophys1.hoc', # Tells the simulator that when building cells models use a hoc_template specially created for parsing Allen Cell-types file models. Value would be different if we were using NeuronML or different model files
model_processing=
'aibs_allactive', # further instructions for how to processes a cell model. In this case aibs_perisomatic is a built-in directive to cut the axon in a specific way
dynamics_params=
'1706014110201.json', # Name of file (downloaded from Allen Cell-Types) used to set model parameters and channels
morphology_file='1706014110201.swc') # Name of morphology file downloaded
phi_index = phi_index + N_GC_1
from bmtk.builder.auxi.node_params import positions_columinar, xiter_random
from bmtk.builder.auxi.edge_connectors import distance_connector
net = NetworkBuilder("V1")
net.add_nodes(N=80,
pop_name='Scnn1a',
positions=positions_columinar(N=80,
center=[0, 50.0, 0],
max_radius=30.0,
height=100.0),
rotation_angle_yaxis=xiter_random(N=80,
min_x=0.0,
max_x=2 * np.pi),
rotation_angle_zaxis=xiter_random(N=80,
min_x=0.0,
max_x=2 * np.pi),
tuning_angle=np.linspace(start=0.0,
stop=360.0,
num=80,
endpoint=False),
location='VisL4',
ei='e',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_perisomatic',
dynamics_params='472363762_fit.json',
morphology='Scnn1a.swc')
net.add_nodes(N=20,
pop_name='PV',
positions=positions_columinar(N=20,
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_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
from bmtk.builder.networks import NetworkBuilder
def crule(src, trg):
# print src.node_id, trg.node_id
return 2
net1 = NetworkBuilder('NET1')
net1.add_nodes(N=100,
position=[(0.0, 1.0, -1.0)] * 100,
cell_type='Scnna1',
ei='e')
net1.add_edges(source={'ei': 'e'}, target={'ei': 'e'}, connection_rule=5)
net1.build()
net2 = NetworkBuilder('NET2')
net2.add_nodes(N=10, position=[(0.0, 1.0, -1.0)] * 10, cell_type='PV1', ei='i')
net2.add_edges(connection_rule=10)
net2.add_edges(source=net1.nodes(), connection_rule=1)
net2.add_edges(target=net1.nodes(), connection_rule=crule)
net2.build()
#net1.save_edges(output_dir='tmp_output')
net2.save_edges(output_dir='tmp_output')
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')
from bmtk.builder.networks import NetworkBuilder
net1 = NetworkBuilder('hco_net')
net1.add_nodes(N=1,
cell_name='HCOCell1',
model_type='biophysical',
model_template='hoc:HCOcell',
morphology='blank.swc'
)
net1.add_nodes(N=1,
cell_name='HCOCell2',
model_type='biophysical',
model_template='hoc:HCOcell',
morphology='blank.swc'
)
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,
print("\nCreating Cells")
# Build the main network
net = NetworkBuilder('LUT')
# Specify number of cells in each population #
numPUDaff = 1
numPelaff = 1
numINmplus = 1
numINmminus = 1
numIND = 1
numFB = 1
numSPN = 1
# Create the nodes ----------------------------------------
net.add_nodes(N=numPelaff, pop_name='Pelaff',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numPUDaff, pop_name='PUDaff',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numINmplus, pop_name='INmplus',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numINmminus, pop_name='INmminus',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numIND, pop_name='IND',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numFB, pop_name='FB',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
net.add_nodes(N=numSPN, pop_name='SPN',model_type='biophysical',model_template='hoc:LIF_adapt',morphology=None)
##################################################################################
####################### Connect the cells ########################################
##################################################################################
print("\nConnecting Cells")
def conn_props(source,target,mu,sigma):
"""
from bmtk.builder.networks import NetworkBuilder
# First thing is to create a network builder object
net = NetworkBuilder('mcortex')
net.add_nodes(
cell_name='Scnn1a',
# positions=[(0.0, 0.0, 0.0)],
potental='exc',
level_of_detail='biophysical',
params_file='472363762_fit.json',
morphology_file='Scnn1a.swc',
set_params_function='Biophys1')
net.build()
net.save_nodes(output_dir='network')
}
}
lgn_net = NetworkBuilder('LGN')
xcoords = []
ycoords = []
for model_name, model_params in cell_models.items():
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:"
from bmtk.builder.networks import NetworkBuilder
net = NetworkBuilder("biophysical")
net.add_nodes(N=1,
pop_name='Pyrc',
potental='exc',
model_type='biophysical',
model_template='hoc:L5PCtemplate',
morphology=None)
net.build()
net.save_nodes(output_dir='network')
from bmtk.utils.sim_setup import build_env_bionet
build_env_bionet(
base_dir='./', # Where to save the scripts and config files
components_dir='../biophys_components',
network_dir='./network', # Location of directory containing network files
tstop=3000.0,
dt=0.1, # Run a simulation for 2000 ms at 0.1 ms intervals
report_vars=['v'],
#clamp_reports=["se"], # 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': 0.793,
#'amp': 0.346,
'delay': 700,
'duration': 2000,
'gids': "all"
},
spikes_threshold=-10,
from bmtk.builder.networks import NetworkBuilder
# First thing is to create a network builder object
net = NetworkBuilder('mcortex')
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')
net.build()
net.save_nodes(output_dir='network')
net = NetworkBuilder('V1/L4')
for name, model_params in cell_models.items():
N = model_params['N']
cell_props = {'position': np.random.rand(N, 3)*[100.0, -300.0, 100.0],
'rotation_angle': np.random.uniform(0.0, 2*np.pi, (N,))}
if model_params['props']['ei'] == 'e':
cell_props['tuning_angle'] = np.linspace(0, 360.0, N, endpoint=False)
else:
cell_props['tuning_angle'] = ['NA']*N
cell_props.update(model_params['props'])
net.add_nodes(N=N,
pop_name=name,
location='VisL4',
**cell_props)
'''
net.build()
net.save_nodes('tmp_nodes.h5', 'tmp_node_types.csv')
exit()
'''
cparameters = {'d_weight_min': 0.0, 'd_weight_max': 1.0, 'd_max': 160.0, 'nsyn_min': 3, 'nsyn_max': 7}
net.add_edges(sources={'ei': 'i'}, targets={'ei': 'i', 'level_of_detail': 'biophysical'},
func=distance_connection_handler, func_params=cparameters,
weight_max=0.0002,
weight_function='wmax',
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
import numpy as np
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.aux.node_params import positions_columinar
from bmtk.builder.aux.edge_connectors import distance_connector
"""Create Nodes"""
net = NetworkBuilder("V1")
net.add_nodes(N=80, # Create a population of 80 neurons
positions=positions_columinar(N=80, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
pop_name='Scnn1a', location='VisL4', ei='e', # optional parameters
model_type='point_process', # Tells the simulator to use point-based neurons
model_template='nest:iaf_psc_alpha', # tells the simulator to use NEST iaf_psc_alpha models
dynamics_params='472363762_point.json' # File containing iaf_psc_alpha mdoel parameters
)
net.add_nodes(N=20, pop_name='PV', location='VisL4', ei='i',
positions=positions_columinar(N=20, center=[0, 50.0, 0], max_radius=30.0, height=100.0),
model_type='point_process',
model_template='nest:iaf_psc_alpha',
dynamics_params='472912177_point.json')
net.add_nodes(N=200, pop_name='LIF_exc', location='L4', ei='e',
positions=positions_columinar(N=200, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),
model_type='point_process',
model_template='nest:iaf_psc_alpha',
dynamics_params='IntFire1_exc_point.json')
net.add_nodes(N=100, pop_name='LIF_inh', location='L4', ei='i',
positions=positions_columinar(N=100, center=[0, 50.0, 0], min_radius=30.0, max_radius=60.0, height=100.0),
model_type='point_process',
import numpy as np
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.auxi.edge_connectors import connect_random
#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'],
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()
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')
numBladaff = 10 numEUSaff = 10 numPAGaff = 10 numIND = 10 numHypo = 10 numINmplus = 10 numINmminus = 10 numPGN = 10 numFB = 10 numIMG = 10 numMPG = 10 numEUSmn = 10 numBladmn = 10 # Create the nodes ---------------------------------------- net.add_nodes(N=numBladaff, level='high',pop_name='Bladaff',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') net.add_nodes(N=numEUSaff, level='high',pop_name='EUSaff',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') net.add_nodes(N=numPAGaff, pop_name='PAGaff',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') net.add_nodes(N=numIND, pop_name='IND',model_type='biophysical',model_template='hoc:IND',morphology='blank.swc') net.add_nodes(N=numHypo, pop_name='Hypo',model_type='biophysical',model_template='hoc:HYPO',morphology='blank.swc') net.add_nodes(N=numINmplus, pop_name='INmplus',model_type='biophysical',model_template='hoc:INM',morphology='blank.swc') net.add_nodes(N=numINmminus, pop_name='INmminus',model_type='biophysical',model_template='hoc:INM',morphology='blank.swc') net.add_nodes(N=numPGN, level='low', pop_name='PGN',model_type='biophysical',model_template='hoc:PGN',morphology='blank.swc') net.add_nodes(N=numFB, pop_name='FB',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') net.add_nodes(N=numIMG, pop_name='IMG',model_type='biophysical',model_template='hoc:IMG',morphology='blank.swc') net.add_nodes(N=numMPG, pop_name='MPG',model_type='biophysical',model_template='hoc:MPG',morphology='blank.swc') net.add_nodes(N=numEUSmn, pop_name='EUSmn',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') net.add_nodes(N=numBladmn, pop_name='Bladmn',model_type='biophysical',model_template='hoc:PUD',morphology='blank.swc') ################################################################################## ####################### Connect the cells ########################################
def build_cortical_network(output_dir='network/recurrent_network'):
def distance_connection_handler(source, target, d_max, nsyn_min, nsyn_max):
""" Connect cells that are less than d_max apart with a random number of synapses in the
interval [nsyn_min, nsyn_max)
"""
sid = source['node_id'] # Get source id
tid = target['node_id'] # Get target id
# Avoid self-connections.
if (sid == tid):
return None
# first calculate euclidean distance between cells
src_positions = np.array([source['x'], source['y'], source['z']])
trg_positions = np.array([target['x'], target['y'], target['z']])
separation = np.sqrt(np.sum(src_positions - trg_positions)**2)
# drop the connection if nodes too far apart
if separation >= d_max:
return None
# Add the number of synapses for every connection.
tmp_nsyn = random.randint(nsyn_min, nsyn_max)
return tmp_nsyn
#### Step 1: Figure out what types, and number of, different cells to use in our network ####
# Number of cell models desired
N_Scnn1a = 2
N_PV1 = 2
N_LIF_exc = 2
N_LIF_inh = 2
# Define all the cell models in a dictionary (note dictionaries within a dictionary)
biophysical_models = {
'Scnn1a': {
'N': N_Scnn1a,
'ei': 'e',
'pop_name': 'Scnn1a',
'model_type': 'biophysical',
'model_template': 'ctdb:Biophys1.hoc',
'model_processing': 'aibs_perisomatic',
'morphology_file':
'Scnn1a-Tg3-Cre_Ai14_IVSCC_-177300.01.02.01_473845048_m.swc',
'dynamics_params': '472363762_fit.json',
'rotation_angle_zaxis': -3.646878266
},
'PV1': {
'N': N_PV1,
'ei': 'i',
'pop_name': 'PV1',
'model_type': 'biophysical',
'model_template': 'ctdb:Biophys1.hoc',
'model_processing': 'aibs_perisomatic',
'dynamics_params': '472912177_fit.json',
'morphology_file':
'Pvalb-IRES-Cre_Ai14_IVSCC_-176847.04.02.01_470522102_m.swc',
'rotation_angle_zaxis': -2.539551891
}
}
# Define all the cell models in a dictionary.
LIF_models = {
'LIF_exc': {
'N': N_LIF_exc,
'ei': 'e',
'pop_name': 'LIF_exc',
'model_type': 'point_process',
'model_template': 'nrn:IntFire1',
'dynamics_params': 'IntFire1_exc_1.json'
},
'LIF_inh': {
'N': N_LIF_inh,
'ei': 'i',
'pop_name': 'LIF_inh',
'model_type': 'point_process',
'model_template': 'nrn:IntFire1',
'dynamics_params': 'IntFire1_inh_1.json'
}
}
#### Step 2: Create NetworkBuidler object to build nodes and edges ####
net = NetworkBuilder('Cortical')
#### Step 3: Used add_nodes() method to add all our cells/cell-types
for model in biophysical_models:
# Build our biophysical cells
params = biophysical_models[model]
n_cells = params.pop('N')
# We'll randomly assign positions
positions = generate_random_positions(n_cells)
# Use add_nodes to create a set of N cells for each cell-type
net.add_nodes(
N=n_cells, # Specify the numer of cells belonging to this set of nodes
x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
rotation_angle_yaxis=np.random.uniform(0.0, 2 * np.pi, n_cells),
# The other parameters are shared by all cells of this set in the dictionary
**params) # python shortcut for unrolling a dictionary
for model in LIF_models:
# Same thing as above but for our LIF type cells
params = LIF_models[model].copy()
# Number of cells for this model type
n_cells = params.pop('N')
# Precacluate positions, rotation angles for each N neurons in the population
positions = generate_random_positions(n_cells)
# Adds node populations
net.add_nodes(N=n_cells,
x=positions[:, 0],
y=positions[:, 1],
z=positions[:, 2],
rotation_angle_yaxis=np.random.uniform(
0.0, 2 * np.pi, n_cells),
**params)
#### Step 4: Used add_edges() to set our connections between cells ####
cparameters = {
'd_max':
160.0, # Maximum separation between nodes where connection allowed
'nsyn_min': 3, # If connection exist, minimum number of synapses
'nsyn_max': 7
} # If connection exist, maximum number of synapses
net.add_edges(
source={
'ei': 'i'
}, # Select all inhibitory cells to apply this connection rule too
target={
'ei': 'i',
'model_type': 'biophysical'
}, # for the target cells we will use inhibitory biophysical cells
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.03,
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='GABA_InhToInh.json',
model_template='exp2syn')
# inhibitory --> point-inhibitory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'i',
'model_type': 'point_process'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.3,
delay=2.0,
dynamics_params='instanteneousInh.json')
# inhibiotry --> biophysical-excitatory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'e',
'model_type': 'biophysical'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.3,
distance_range=[0.0, 50.0],
target_sections=['somatic', 'basal', 'apical'],
delay=2.0,
dynamics_params='GABA_InhToExc.json',
model_template='exp2syn')
# inhibitory --> point-excitatory
net.add_edges(source={'ei': 'i'},
target={
'ei': 'e',
'model_type': 'point_process'
},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.4,
delay=2.0,
dynamics_params='instanteneousInh.json')
# excitatory --> PV1 cells
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'PV1'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
distance_range=[0.0, 1e+20],
target_sections=['somatic', 'basal'],
delay=2.0,
dynamics_params='AMPA_ExcToInh.json',
model_template='exp2syn')
# excitatory --> LIF_inh
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'LIF_inh'},
connection_rule=distance_connection_handler,
connection_params=cparameters,
syn_weight=0.2,
delay=2.0,
dynamics_params='instanteneousExc.json')
# excitatory --> Scnn1a
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'Scnn1a'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],
delay=2.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
# excitatory --> LIF_exc
net.add_edges(source={'ei': 'e'},
target={'pop_name': 'LIF_exc'},
connection_rule=distance_connection_handler,
connection_params={
'd_max': 160.0,
'nsyn_min': 3,
'nsyn_max': 7
},
syn_weight=0.05,
delay=2.0,
dynamics_params='instanteneousExc.json')
#### Step 5: Build and save the network ####
net.build()
net.save(output_dir=output_dir)
return net
from bmtk.builder.networks import NetworkBuilder
from bmtk.builder.auxi.node_params import positions_columinar, xiter_random
from bmtk.builder.auxi.edge_connectors import distance_connector
import math
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')
import os
import numpy as np
from bmtk.builder.networks import NetworkBuilder
# Step 1: Create a v1 mock network of 14 cells (nodes) with across 7 different cell "types"
net = NetworkBuilder("v1")
net.add_nodes(N=1, # specifiy the number of cells belong to said group.
pop_name='', location='L2', ei='e', # pop_name, location, and ei are optional parameters that help's identifies properties of the cells. The modeler can choose whatever key-value pairs as they deem appropiate.
positions=[(2.753, -464.868, -161.705)], # The following properties we are passing in lists
tuning_angle=[0.0], # values to each individual cell
rotation_angle_xaxis=-0.010516,
rotation_angle_yaxis=0,
rotation_angle_zaxis=-3.011895, # Note that the y-axis rotation is differnt for each cell (ie. given a list of size N), but with z-axis rotation all cells have the same value
model_type='biophysical', # The type of cell we are using
model_template='ctdb:Biophys1.hoc', # Tells the simulator that when building cells models use a hoc_template specially created for parsing Allen Cell-types file models. Value would be different if we were using NeuronML or different model files
model_processing='aibs_allactive_ani_directed', # further instructions for how to processes a cell model. In this case aibs_perisomatic is a built-in directive to cut the axon in a specific way
dynamics_params='optim_param_571654895.json', # Name of file (downloaded from Allen Cell-Types) used to set model parameters and channels
morphology='571654895.swc'), # Name of morphology file downloaded
# Step 2: We want to connect our network. Just like how we have node-types concept we group our connections into
# "edge-types" that share rules and properties
net.add_edges(source={'ei': 'e'}, target={'pop_name': ''},
connection_rule=5,
syn_weight=4e-05,
weight_function='gaussianLL',
weight_sigma=50.0,
distance_range=[30.0, 150.0],
target_sections=['basal', 'apical'],