def __init__(self, params_file, seed=123):
"""Initializes the simulation builder,
setting up attributes but not actually building the BMTK network.
Parameters
----------
params_file : str
path to the JSON file with network parameters
seed : int
base random seed for the simulation
"""
#Loads the JSON file with information about the network.
with open(params_file) as f:
self.params = json.load(f)
self.seed = seed
#Loads synapse templates.
synapses.load()
self.syn = synapses.syn_params_dicts()
avg_exc_div = np.mean(list(self.params["divergence"]["exc"].values()))
self.n_dend_exc = int(
(self.params["lengths"]["basal_dist"] *
self.params["syn_density"]["exc"]) / avg_exc_div)
self.n_apic_exc = int(
(self.params["lengths"]["apic"] *
self.params["syn_density"]["exc"]) / avg_exc_div)
self.n_dend_inh = int((self.params["lengths"]["basal_dist"] *
self.params["syn_density"]["inh"]) /
self.params["divergence"]["basal_inh"]["m"])
self.n_apic_inh = int((self.params["lengths"]["apic"] *
self.params["syn_density"]["inh"]) /
self.params["divergence"]["apic_inh"]["m"])
self.n_prox_dend_inh = int((self.params["lengths"]["basal_prox"] *
self.params["syn_density"]["inh"]) /
self.params["divergence"]["peri_inh"]["m"])
self.n_soma_inh = int(self.params["n_soma_syns"] /
self.params["divergence"]["peri_inh"]["m"])
self.clust_per_group = int(
(self.params["groups"]["cells_per_group"] * avg_exc_div) //
(self.params["syn_density"]["exc"] * 10))
if self.params["file_current_clamp"]["input_file"] == "None":
self.file_current_clamp = None
else:
self.file_current_clamp = self.params["file_current_clamp"]
fullsecname = seg.sec.name()
source_pops.append(source._population)
node_ids.append(source._node_id)
weights.append(float(syn.initW))
release_probs.append(float(syn.P_0))
names.append(str(seg))
sec_types.append(fullsecname.split(".")[1][:4])
dists.append(float(h.distance(seg)))
df = pd.DataFrame()
df["Node ID"] = node_ids
df["Distance"] = dists
df["Conductance"] = weights
df["Type"] = sec_types
df["Name"] = names
df["Source Population"] = source_pops
df["Release Probability"] = release_probs
df.to_csv("Connections.csv", index=False)
if __name__ == "__main__":
synapses.load()
syn = synapses.syn_params_dicts()
np.random.seed(42)
run.run_network(
[save_connections],
v_report_all=False) #make v_report_all True to save all segments
#Number of cells in each population
numPN_A = 1
numPN_C = 1
numBask = 1
num_cells = numPN_A + numPN_C + numBask
dist_constraint = False
min_conn_dist = 0.0
max_conn_dist = 9999.9
if dist_constraint:
max_conn_dist = 300.0 #9999.9# Distance constraint for all cells
# Load synapse dictionaries
synapses.load()
syn = synapses.syn_params_dicts('../../components/synaptic_models')
###################################################################################
####################################Pyr Type A#####################################
# Add a population of numPN_A nodes (all of which share model_type, dynamics_params, etc.)
net.add_nodes(N=numPN_A, pop_name='PyrA',
mem_potential='e',
model_type='biophysical',
#model_template='hoc:feng_typeA',#Ben's model
model_template='hoc:Cell_Af',
morphology=None)
##################################################################################
###################################Pyr Type C#####################################
from bmtk.builder import NetworkBuilder
import numpy as np
import sys
import synapses
synapses.load()
syn = synapses.syn_params_dicts(
syn_dir='../biophys_components/synaptic_models')
if __name__ == '__main__':
if __file__ != sys.argv[-1]:
inp = sys.argv[-1]
else:
raise Exception("no work" + str(sys.argv[-1]))
dist = float(inp)
net = NetworkBuilder("biophysical")
# net.add_nodes(N=1, pop_name='PyrC',
# 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.add_nodes(N=1,
pop_name='Pyrc',
potental='exc',
model_type='biophysical',
model_template='ctdb:Biophys1.hoc',
model_processing='aibs_allactive',
