def run(config_file):
synapses.load()
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def gaussianBL(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
return np.random.normal(w0, sigma, 1)
def lognormal(edge_props, source, target):
m = edge_props["syn_weight"]
s = edge_props["weight_sigma"]
mean = np.log(m) - 0.5 * np.log((s / m)**2 + 1)
std = np.sqrt(np.log((s / m)**2 + 1))
return np.random.lognormal(mean, std, 1)
add_weight_function(lognormal)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
sim.run()
bionet.nrn.quit_execution()
def run(config_file):
warnings.simplefilter(action='ignore', category=FutureWarning)
synapses.load()
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
sim = bionet.BioSimulator.from_config(conf, network=graph)
sim.run()
bionet.nrn.quit_execution()
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"]
def run(config_file):
warnings.simplefilter(action='ignore', category=FutureWarning)
synapses.load()
from bmtk.simulator.bionet.pyfunction_cache import add_weight_function
def gaussianBL(edge_props, source, target):
w0 = edge_props["syn_weight"]
sigma = edge_props["weight_sigma"]
return np.random.normal(w0, sigma, 1)
def lognormal(edge_props, source, target):
m = edge_props["syn_weight"]
s = edge_props["weight_sigma"]
mean = np.log(m) - 0.5 * np.log((s/m)**2+1)
std = np.sqrt(np.log((s/m)**2 + 1))
return np.random.lognormal(mean, std, 1)
add_weight_function(lognormal)
add_weight_function(gaussianBL)
conf = bionet.Config.from_json(config_file, validate=True)
conf.build_env()
graph = bionet.BioNetwork.from_config(conf)
# This fixes the morphology error in LFP calculation
pop = graph._node_populations['SPWR_biophysical']
for node in pop.get_nodes():
node._node._node_type_props['morphology'] = node.model_template[1]
sim = bionet.BioSimulator.from_config(conf, network=graph)
# This calls insert_mechs() on each cell to use its gid as a seed
# to the random number generator, so that each cell gets a different
# random seed for the point-conductance noise
#cells = graph.get_local_cells()
#for cell in cells:
# cells[cell].hobj.insert_mechs(cells[cell].gid)
sim.run()
bionet.nrn.quit_execution()
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
