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_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_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
connection_params={
'nedges': 140,
'nsyns_min': 2,
'nsyns_max': 5,
'divergence': 100
},
iterator='one_to_all',
syn_weight=0.0016,
weight_function='wmax',
distance_range=[0.0, 50.0],
target_sections=['somatic'],
delay=0.85,
model_template='exp2syn',
dynamics_params='GABA_InhToExc_BC_GC.json')
net.build()
net.save(output_dir='network')
# BASELINE EXTERNAL DRIVE TO THE GC population
GC_external_input = NetworkBuilder("GC_external_input")
GC_external_input.add_nodes(N=9000,
pop_name='perforant_path',
ei='e',
positions=generate_positions(9000),
model_type='virtual')
# drive to the GC population
GC_external_input.add_edges(
source=GC_external_input.nodes(),
target=net.nodes(ei='e'),
connection_rule=select_random_srcs,
connection_params={
def build_l4():
if not os.path.exists('output/network/VisL4'):
os.makedirs('output/network/VisL4')
net = NetworkBuilder("V1/L4")
net.add_nodes(N=2,
pop_name='Scnn1a',
node_type_id='395830185',
position='points',
position_params={
'location': [(28.753, -364.868, -161.705),
(48.753, -344.868, -141.705)]
},
array_params={"tuning_angle": [0.0, 25.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='472363762_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='Rorb',
node_type_id='314804042',
position='points',
position_params={
'location': [(241.092, -349.263, 146.916),
(201.092, -399.263, 126.916)]
},
array_params={"tuning_angle": [50.0, 75.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='473863510_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='Nr5a1',
node_type_id='318808427',
position='points',
position_params={
'location': [(320.498, -351.259, 20.273),
(310.498, -371.259, 10.273)]
},
array_params={"tuning_angle": [100.0, 125.0]},
location='VisL4',
ei='e',
gaba_synapse='y',
params_file='473863035_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='PV1',
node_type_id='330080937',
position='points',
position_params={
'location': [(122.373, -352.417, -216.748),
(102.373, -342.417, -206.748)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='i',
gaba_synapse='y',
params_file='472912177_fit.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='PV2',
node_type_id='318331342',
position='points',
position_params={
'location': [(350.321, -372.535, -18.282),
(360.321, -371.535, -12.282)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='i',
gaba_synapse='y',
params_file='473862421_point.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='LIF_exc',
node_type_id='100000101',
position='points',
position_params={
'location': [(-243.04, -342.352, -665.666),
(-233.04, -332.352, -675.666)]
},
array_params={'tuning_angle': ['NA', 'NA']},
location='VisL4',
ei='e',
gaba_synapse='n',
params_file='IntFire1_exc_1.json',
model_type='iaf_psc_alpha')
net.add_nodes(N=2,
pop_name='LIF_inh',
node_type_id='100000102',
position='points',
position_params={
'location': [(211.04, -321.333, -631.593),
(218.04, -327.333, -635.593)]
},
array_params={'tuning_angle': [150.0, 175.0]},
location='VisL4',
ei='i',
gaba_synapse='n',
params_file='IntFire1_inh_1.json',
model_type='iaf_psc_alpha')
print("Setting connections...")
net.connect(source={'ei': 'i'},
target={
'ei': 'i',
'gaba_synapse': 'y'
},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -1.8,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={
'ei': 'e',
'gaba_synapse': 'y'
},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -12.6,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={'pop_name': 'LIF_inh'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -1.125,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'InhToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'i'},
target={'pop_name': 'LIF_exc'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': -6.3,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'PV1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 7.7,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'PV2'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 5.4,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'LIF_inh'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 3.44,
'weight_function': 'wmax',
'delay': 2.0,
'params_file': 'ExcToInh.json',
'synapse_model': 'static_synapse'
})
print("Generating E-to-E connections.")
net.connect(source={'ei': 'e'},
target={'pop_name': 'Scnn1a'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 8.448,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'Rorb'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 4.292,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'Nr5a1'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 5.184,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.connect(source={'ei': 'e'},
target={'pop_name': 'LIF_exc'},
connector=lambda trg, src: 5,
edge_params={
'weight_max': 1.995,
'weight_function': 'gaussianLL',
'weight_sigma': 50.0,
'delay': 2.0,
'params_file': 'ExcToExc.json',
'synapse_model': 'static_synapse'
})
net.build()
net.save_cells(
filename='output/network/VisL4/nodes.csv',
columns=['node_id', 'node_type_id', 'position', 'tuning_angle'],
position_labels=['x', 'y', 'z'])
net.save_types(filename='output/network/VisL4/node_types.csv',
columns=[
'node_type_id', 'pop_name', 'ei', 'gaba_synapse',
'location', 'model_type', 'params_file'
])
net.save_edge_types('output/network/VisL4/edge_types.csv',
opt_columns=[
'weight_max', 'weight_function', 'weight_sigma',
'delay', 'params_file', 'synapse_model'
])
net.save_edges(filename='output/network/VisL4/edges.h5')
return net
def build_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
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)
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:"
# 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))
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')
target_sections=['somatic'],
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()
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()
thalamus.save_nodes(output_dir='network')
thalamus.save_edges(output_dir='network')
lgn.add_edges(source=lgn.nodes(),
target=net.nodes(pop_name='LIF_exc'),
connection_rule=select_source_cells,
connection_params={
'nsources_min': 10,
'nsources_max': 25
},
iterator='all_to_one',
syn_weight=0.045,
weight_function='wmax',
delay=2.0,
dynamics_params='instanteneousExc.json',
model_template='exp2syn')
lgn.add_edges(source=lgn.nodes(),
target=net.nodes(pop_name='LIF_inh'),
connection_rule=select_source_cells,
connection_params={
'nsources_min': 15,
'nsources_max': 30
},
iterator='all_to_one',
syn_weight=0.02,
weight_function='wmax',
delay=2.0,
dynamics_params='instanteneousExc.json',
model_template='exp2syn')
lgn.build()
lgn.save_nodes(output_dir='network')
lgn.save_edges(output_dir='network')
'''
tw = NetworkBuilder("tw")
tw.add_nodes(N=30, pop_name='TW', ei='e', location='TW', model_type='virtual')
tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_exc'),
iterator='all_to_one',
connection_rule=recurrent_connections_low,
connection_params={'n_syns': 1},
syn_weight=0.5,
weight_function='wmax',
delay=0.0,
dynamics_params='instanteneousExc.json')
tw.add_edges(source=tw.nodes(), target=net.nodes(pop_name='LIF_inh'),
connection_rule=1,
syn_weight=0.2,
weight_function='wmax',
delay=0.0,
dynamics_params='instanteneousExc.json')
tw.build()
tw.save(output_dir='../input/network')'''
net_pre.build()
net_pre.save(output_dir='../input/network')
net_post.build()
net_post.save(output_dir='../input/network')
print 'done'
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')
connection_params={'d_weight_min': 0.0, 'd_weight_max': 0.26, 'd_max': 300.0, 'nsyn_min': 3, 'nsyn_max': 7},
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)
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
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()
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'],
delay=2.0,
params_file='GABA_InhToInh.json',
set_params_function='exp2syn')
PAG_aff_virt.add_edges(source=PAG_aff_virt.nodes(), target=net.nodes(pop_name='PAGaff'),
connection_rule=percent_connector,
connection_params={'percent':100.0},
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)
dynamics_params='473863035_fit.json',
morphology='Nr5a1-Cre_Ai14_IVSCC_-169250.03.02.01_471087815_m.swc')
# Create synapse from the high-level cell to the low-level cell (0 --> 1)
net.add_edges(source={'level': 'high'},
target={'level': 'low'},
connection_rule=5,
syn_weight=12.0e-03,
target_sections=['somatic'],
distance_range=[0.0, 300.0],
delay=1.0,
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
# build and save the network.
net.build()
net.save(output_dir='network')
# An external network with 1 (excitatory virtual) cell that will connect to the internal excitatory cell to drive
# it with the spikes inputs
inputs = NetworkBuilder("external")
inputs.add_nodes(N=1, model_type='virtual')
inputs.add_edges(
source=inputs.nodes(),
target=net.nodes(level='low'),
connection_rule=1,
syn_weight=12.0E-03,
# weight_function='wmax',
distance_range=[0.0, 300.0],
target_sections=['somatic'],
delay=1.0,
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')
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')