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import os
import numpy as np
from bmtk.builder import NetworkBuilder
from bmtk.builder.bionet import SWCReader
from bmtk.utils.io.spike_trains import PoissonSpikesGenerator
from bmtk.builder.auxi.node_params import positions_columinar, xiter_random
build_recurrent_edges = True
# List of non-virtual cell models
bio_models = [
{
'model_name': 'Scnn1a', 'ei': 'e',
'morphology': 'Scnn1a_473845048_m.swc',
'model_template': 'nml:Cell_472363762.cell.nml'
},
{
'model_name': 'Rorb', 'ei': 'e',
'morphology': 'Rorb_325404214_m.swc',
'model_template': 'nml:Cell_473863510.cell.nml'
},
{
'model_name': 'Nr5a1', 'ei': 'e',
'morphology': 'Nr5a1_471087815_m.swc',
'model_template': 'nml:Cell_473863035.cell.nml'
},
{
'model_name': 'PV1', 'ei': 'i',
'morphology': 'Pvalb_470522102_m.swc',
'model_template': 'nml:Cell_472912177.cell.nml'
},
{
'model_name': 'PV2', 'ei': 'i',
'morphology': 'Pvalb_469628681_m.swc',
'model_template': 'nml:Cell_473862421.cell.nml'
}
]
point_models = [
{
'model_name': 'LIF_exc', 'ei': 'e',
'dynamics_params': 'IntFire1_exc_1.json'
},
{
'model_name': 'LIF_inh', 'ei': 'i',
'dynamics_params': 'IntFire1_inh_1.json'
}
]
morphologies = {p['model_name']: SWCReader(os.path.join('../biophys_components/morphologies', p['morphology']))
for p in bio_models}
def build_edges(src, trg, sections=['basal', 'apical'], dist_range=[50.0, 150.0]):
"""Function used to randomly assign a synaptic location based on the section (soma, basal, apical) and an
arc-length dist_range from the soma. This function should be passed into the network and called during the build
process.
:param src: source cell (dict)
:param trg: target cell (dict)
:param sections: list of target cell sections to synapse onto
:param dist_range: range (distance from soma center) to place
:return:
"""
# Get morphology and soma center for the target cell
swc_reader = morphologies[trg['model_name']]
target_coords = [trg['x'], trg['y'], trg['z']]
sec_ids, sec_xs = swc_reader.choose_sections(sections, dist_range) # randomly choose sec_ids
coords = swc_reader.get_coord(sec_ids, sec_xs, soma_center=target_coords) # get coords of sec_ids
dist = swc_reader.get_dist(sec_ids)
swctype = swc_reader.get_type(sec_ids)
return sec_ids, sec_xs, coords[0][0], coords[0][1], coords[0][2], dist[0], swctype[0]
# Build a network of 300 biophysical cells to simulate
internal = NetworkBuilder("internal")
for i, model_props in enumerate(bio_models):
n_cells = 80 if model_props['ei'] == 'e' else 30 # 80% excitatory, 20% inhib
# Randomly get positions uniformly distributed in a column
positions = positions_columinar(N=n_cells, center=[0, 10.0, 0], max_radius=50.0, height=200.0)
internal.add_nodes(N=n_cells,
x=positions[:, 0], y=positions[:, 1], z=positions[:, 2],
rotation_angle_yaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi), # randomly rotate y axis
rotation_angle_zaxis=xiter_random(N=n_cells, min_x=0.0, max_x=2 * np.pi), #
model_type='biophysical',
model_processing='aibs_perisomatic',
**model_props)
for model_props in point_models:
n_cells = 75 # Just assume 75 cells for both point inhibitory and point excitatory
positions = positions_columinar(N=n_cells, center=[0, 10.0, 0], max_radius=50.0, height=200.0)
internal.add_nodes(N=n_cells,
x=positions[:, 0], y=positions[:, 1], z=positions[:, 2],
model_type='point_process',
model_template='nrn:IntFire1',
**model_props)
if build_recurrent_edges:
def n_connections(src, trg, prob=0.1, min_syns=1, max_syns=5):
if src.node_id == trg.node_id:
return 0
return 0 if np.random.uniform() > prob else np.random.randint(min_syns, max_syns)
# Connections onto biophysical components, use the connection map to save section and position of every synapse
# exc --> exc connections
cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'e', 'model_type': 'biophysical'},
connection_rule=n_connections,
connection_params={'prob': 0.2},
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=6.0e-05, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
rule_params={'sections': ['basal', 'apical'], 'dist_range': [30.0, 150.0]},
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
# exc --> inh connections
cm = internal.add_edges(source={'ei': 'e'}, target={'ei': 'i', 'model_type': 'biophysical'},
connection_rule=n_connections,
dynamics_params='AMPA_ExcToInh.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=0.0006, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1.0e+20]},
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
# inh --> exc connections
cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'e', 'model_type': 'biophysical'},
connection_rule=n_connections,
dynamics_params='GABA_InhToExc.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=0.002, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
rule_params={'sections': ['somatic', 'basal', 'apical'], 'dist_range': [0.0, 50.0]},
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
# inh --> inh connections
cm = internal.add_edges(source={'ei': 'i'}, target={'ei': 'i', 'model_type': 'biophysical'},
connection_rule=n_connections,
connection_params={'prob': 0.2},
dynamics_params='GABA_InhToInh.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=0.00015, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
rule_params={'sections': ['somatic', 'basal'], 'dist_range': [0.0, 1.0e+20]},
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
# For connections on point neurons it doesn't make sense to save syanpatic location
cm = internal.add_edges(source={'ei': 'e'}, target={'model_type': 'point_process'},
connection_rule=n_connections,
dynamics_params='instanteneousExc.json',
delay=2.0)
cm.add_properties('syn_weight', rule=0.0019, dtypes=np.float)
cm = internal.add_edges(source={'ei': 'i'}, target={'model_type': 'point_process'},
connection_rule=n_connections,
dynamics_params='instanteneousInh.json',
delay=2.0)
cm.add_properties('syn_weight', rule=0.0019, dtypes=np.float)
internal.build()
print('Saving internal')
internal.save(output_dir='network')
print('Building external connections')
external = NetworkBuilder("external")
external.add_nodes(N=100, model_type='virtual', ei='e')
cm = external.add_edges(target=internal.nodes(ei='e', model_type='biophysical'), source=external.nodes(),
connection_rule=lambda *_: np.random.randint(0, 5),
dynamics_params='AMPA_ExcToExc.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=0.00041, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
rule_params={'sections': ['basal', 'apical', 'somatic']},
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
cm = external.add_edges(target=internal.nodes(ei='i', model_type='biophysical'), source=external.nodes(),
connection_rule=lambda *_: np.random.randint(0, 5),
dynamics_params='AMPA_ExcToInh.json',
model_template='Exp2Syn',
delay=2.0)
cm.add_properties('syn_weight', rule=0.00095, dtypes=np.float)
cm.add_properties(['sec_id', 'sec_x', 'pos_x', 'pos_y', 'pos_z', 'dist', 'type'],
rule=build_edges,
dtypes=[np.int32, np.float, np.float, np.float, np.float, np.float, np.uint8])
cm = external.add_edges(target=internal.nodes(model_type='point_process'), source=external.nodes(),
connection_rule=lambda *_: np.random.randint(0, 5),
dynamics_params='instanteneousExc.json',
delay=2.0)
cm.add_properties('syn_weight', rule=0.045, dtypes=np.float)
external.build()
print('Saving external')
external.save(output_dir='network')
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