def load_connectome_varshney():
"""
Get gap junction and chemical synapse connectivity matrix from Varshney et al., 2011.
The returned value is a tuple of (gap junction matrix, chem matrix)
"""
return np.load(get_data_file_abs_path('Gg.npy')), np.load(
get_data_file_abs_path('Gs.npy'))
def main():
"""
:return:
"""
neuron_metadata_collection = NeuronMetadataCollection.load_from_chem_json(\
get_data_file_abs_path('chem.json'))
inhibitory_neuron_ids = np.argwhere(\
np.load(get_data_file_abs_path('emask.npy')).flatten()).flatten()
inhibitory_neuron_names = []
for neuron_id in inhibitory_neuron_ids:
metadata = neuron_metadata_collection.get_metadata(neuron_id)
inhibitory_neuron_names.append(metadata.name)
print(sorted(inhibitory_neuron_names))
def load_connectome_cook():
"""
Get gap junction and chemical synapse connectivity matrix from Cook et al., 2019.
The returned value is a tuple of (gap junction matrix, chem matrix)
"""
# Cook has extra neurons. It's on purpose that we use Varshney's list of neurons.
neuron_metadata_collection = \
NeuronMetadataCollection.load_from_chem_json(get_data_file_abs_path('chem.json'))
conn_spec_to_weight_gap, conn_spec_to_weight_chem = load_connectome_dict_cook(
)
return (build_connectome_matrix_from_dict(conn_spec_to_weight_gap,
neuron_metadata_collection),
build_connectome_matrix_from_dict(conn_spec_to_weight_chem,
neuron_metadata_collection))
def load_connectome_dict_cook():
"""
Get gap junction and chemical synapse connectivity matrix from Cook et al., 2019.
The returned value is a tuple of (conn_spec_to_weight_gap, conn_spec_to_weight_chem)
Each conn_spec_to_weight is a dictionary with key of conn_spec to weight
conn_spec is a tuple (source neuron, target neuron)
Usage:
conn_spec_to_weight_gap, conn_spec_to_weight_chem = load_connectome_dict_cook()
# This gives gap junction weight from ASHL to ASHR
conn_spec_to_weight_gap[('ASHL', 'ASHR')]
"""
connectome_file = 'herm_full_edgelist.csv'
# key = conn_spec = (from, to)
# value = total weight
conn_spec_to_weight_chem = {}
conn_spec_to_weight_gap = {}
with open(get_data_file_abs_path(connectome_file), newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
source = row['Source'].upper().strip()
target = row['Target'].upper().strip()
weight = row['Weight'].upper().strip()
conn_type = row['Type'].upper().strip()
conn_spec = (source, target)
conn_spec_to_weight = None
if conn_type == "CHEMICAL":
conn_spec_to_weight = conn_spec_to_weight_chem
elif conn_type == "ELECTRICAL":
conn_spec_to_weight = conn_spec_to_weight_gap
else:
raise Exception("Invalid connection type: " + conn_type)
if conn_spec in conn_spec_to_weight:
raise Exception(
"Duplicate entry exists for %s. Previous value is %d, new value is %d" % \
(conn_type, conn_spec_to_weight[conn_spec], weight))
conn_spec_to_weight[conn_spec] = weight
return (conn_spec_to_weight_gap, conn_spec_to_weight_chem)
# Simple script to run the neural model without displaying anything.
# Useful for debugging.
import numpy as np
import project_path
from model.data_accessor import get_data_file_abs_path
from model.neuron_metadata import *
from model.neural_model import NeuralModel
neuron_metadata_collection = NeuronMetadataCollection.load_from_chem_json(get_data_file_abs_path('chem.json'))
N = neuron_metadata_collection.get_size()
# Constant current injections will be made to these neurons.
stimulus = {
"PLML": 1.4,
"PLMR": 1.4,
}
# How many timesteps to run simulation for.
simul_time = 2000
# Initial condition
# If you want a fixed-seed initial condition, uncomment the line below.
# np.random.seed(0)
init_conds = 10**(-4)*np.random.normal(0, 0.94, 2*N)
model = NeuralModel(neuron_metadata_collection)
model.init_conds = init_conds
model.set_I_ext_constant_currents(stimulus)
model.init_kunert_2017()
import numpy as np
import pylab as plt
import project_path
from model.data_accessor import get_data_file_abs_path
from model.neuron_metadata import *
from model.neural_model import NeuralModel
import model.init_conds as init_conds
from util.plot_util import *
import pdb
neurons_to_observe = ["PLML", "PLMR"]
neuron_metadata_collection = NeuronMetadataCollection.load_from_chem_json(
get_data_file_abs_path('chem.json'))
N = neuron_metadata_collection.get_size()
model = NeuralModel(neuron_metadata_collection)
def main():
for neurons_to_stimulate in [["PLML"], ["PLML", "PLMR"]]:
for stim_amp_nA in [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 2, 5]:
case_suffix = "_".join(neurons_to_stimulate) + "_" + str(
stim_amp_nA)
run_one_case(case_name="cook_static_vth_" + case_suffix,
neurons_to_stimulate=neurons_to_stimulate,
stim_amp_nA=stim_amp_nA,
use_cook_connectome=True,
use_static_vth=True)
run_one_case(case_name="varshney_static_vth_" + case_suffix,