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Figure8-Simulate-Network-timing-config-new-model.py
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Figure8-Simulate-Network-timing-config-new-model.py
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from Neuron_model_extended import NeuronModel
import sys
import numpy as np
from neuron import h
import bluepyopt.ephys as ephys
import json
import matplotlib.pyplot as plt
import elephant
import neo
import quantities as pq
import random
import time
with open("config-Figure8-Simulate-Network-timing.json",'r') as parameter_file:
parameters_file = json.load(parameter_file)
new_sim=ephys.simulators.NrnSimulator(dt=0.025,cvode_active=False)
cortex_frequency=parameters_file["Cortex_frequency"]
thalamus_frequency=parameters_file["Thalamus_frequency"]
checkerweight=parameters_file["checkerweight"]
Checkerweight=[1.1,checkerweight] #0.05 for LTS there is no response
'''
Thalamic input
'''
Background_activity_Th = dict()
for i in range(752):
np.random.seed(i)
Th_B = elephant.spike_train_generation.homogeneous_poisson_process(4* pq.Hz,t_stop = parameters_file["simulation_time"] * pq.ms, t_start=0 * pq.ms)
Vec_Th_B=new_sim.neuron.h.VecStim()
Th_A_B=new_sim.neuron.h.Vector(Th_B)
Vec_Th_B.play(Th_A_B)
Background_activity_Th.update({"input_"+str(i): Vec_Th_B})
position_for_thalamic_input=np.random.choice(range(752), 140, replace=False)
Burst_activity_Th = dict()
for position in position_for_thalamic_input:
Thalamic_activity = np.concatenate([np.arange(12500,13200,50),np.arange(15500,16200,50),np.arange(18500,19200,50),np.arange(21500,22200,50),np.arange(24500,25200,50)])
VecStim_TH=new_sim.neuron.h.VecStim()
Thalamic_Vector_Activity=new_sim.neuron.h.Vector(Thalamic_activity)
VecStim_TH.play(Thalamic_Vector_Activity)
Burst_activity_Th.update({"input_"+str(position): VecStim_TH})
'''
Cortical input
'''
Background_activity_Crtx_ChIN = dict()
Background_activity_Crtx_LTS = dict()
position_background_for_cortical_input_LTS=np.arange(250)
position_background_for_cortical_input_ChIN=np.random.choice(range(752),294, replace=False)
print(position_background_for_cortical_input_ChIN)
for i in range(752):
np.random.seed(i)
Crtx_B = elephant.spike_train_generation.homogeneous_poisson_process(2* pq.Hz,t_stop = parameters_file["simulation_time"] * pq.ms, t_start=0 * pq.ms)
Vec_Crtx_B=new_sim.neuron.h.VecStim()
Crtx_A_B=new_sim.neuron.h.Vector(Crtx_B)
Vec_Crtx_B.play(Crtx_A_B)
if i in position_background_for_cortical_input_LTS:
Background_activity_Crtx_LTS.update({"input_"+str(i): Vec_Crtx_B})
if i in position_background_for_cortical_input_ChIN:
Background_activity_Crtx_ChIN.update({"input_"+str(i): Vec_Crtx_B})
print(Background_activity_Crtx_ChIN)
print(Background_activity_Crtx_LTS)
position_cortical_input_ChIN=np.random.choice(range(397), 55, replace=False)
position_cortical_input_LTS=np.arange(66)
activity_Crtx_ChIN=dict()
activity_Crtx_LTS=dict()
Crtx_Stim = elephant.spike_train_generation.homogeneous_poisson_process(cortex_frequency* pq.Hz,t_stop = 10000 * pq.ms, t_start=7000 * pq.ms)
Vec_Crtx=new_sim.neuron.h.VecStim()
Crtx_A=new_sim.neuron.h.Vector(Crtx_Stim)
Vec_Crtx.play(Crtx_A)
for i in range(752):
if i in position_cortical_input_ChIN:
activity_Crtx_ChIN.update({"input_"+str(i): Vec_Crtx})
if i in position_cortical_input_LTS:
activity_Crtx_LTS.update({"input_"+str(i): Vec_Crtx})
start_time=time.time()
Cells = dict()
NOsynapses = list()
thalamicsynapses = list()
corticalsynapses = list()
muscsynapses=list()
configs= parameters_file["single-cell-models"]
cellssynapseattached = ["Unknown[0]", "Unknown[1]"]
synapse_conductance_ChIN_cortical = parameters_file["Cortex_synapse_ChIN_conductance"]
synapse_conductance_ChIN_thalamic = parameters_file["Thalamus_synapse_ChIN_conductance"]
synapse_conductance_LTS_cortical = parameters_file["Cortex_synapse_LTS_conductance"]
synapse_config_cortical_MR = parameters_file["Cortex-synapse"]
synapse_config_thalamic = parameters_file["Thalamus-synapse"]
Gaussian = []
Synapse_TOTAL={"ChIN_TH":0,"LTS_Crtx":0,"ChIN_Crtx":0}
name= ["ChIN","LTS"]
print()
for cells in range(2):
with open(configs[cells],'r') as config_file:
config = json.load(config_file)
morph = config["morphology"]
print("morphology-DONE")
param=config["parameters"]
print("parameters-DONE")
mech=config["mechanisms"]
print("print mechanisms done")
info_cell = dict()
ChIN_cell=NeuronModel(param_file=param,morph_file=morph,mech_file=mech)
new_sim=ephys.simulators.NrnSimulator(cvode_active=False)
ChIN_cell.instantiate(sim=new_sim)
info_cell.update({"soma_sec": ChIN_cell})
info_cell.update({"soma_sim": new_sim})
vSave = new_sim.neuron.h.Vector()
for isec, sec in enumerate(ChIN_cell.icell.soma):
for seg in sec:
if "0.5" in str(seg):
gauss=new_sim.neuron.h.InGauss(seg)
gauss.delay=0
gauss.dur=10e10
gauss.mean=0
gauss.stdev=0.05
Gaussian.append(gauss)
vSave.record(getattr(seg,'_ref_v'))
info_cell.update({"soma_access":seg})
spike_time = new_sim.neuron.h.Vector()
recording_netcon = new_sim.neuron.h.NetCon(getattr(seg,'_ref_v'),None, sec = sec)
recording_netcon.threshold = 0
recording_netcon.record(spike_time)
random_stream_TH=0
random_stream_Crtx_ChIN=0
random_stream_Crtx_LTS=0
for sec in new_sim.neuron.h.allsec():
if cellssynapseattached[cells] in sec.name() and "Unknown[0]" in sec.name() and "soma" not in sec.name() and "axon" not in sec.name():
for seg in sec:
for k in range(2):
if random_stream_TH in range(752):
print('attaching ChIN thalamic')
stim_thalamic_B=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_thalamic["Unknown[0]"]
stim_thalamic_B.U=synapse["U"]
stim_thalamic_B.tau=synapse['tau']
stim_thalamic_B.tauR=synapse['tauR']
stim_thalamic_B.tauR=synapse['tauF']
stim_thalamic_B.nmda_ratio=synapse['nmda_ratio']
stimulation=Background_activity_Th["input_"+str(random_stream_TH)]
nc_thalamic_B = new_sim.neuron.h.NetCon(stimulation,stim_thalamic_B)
nc_thalamic_B.delay=1
nc_thalamic_B.threshold=0
nc_thalamic_B.weight[0]=synapse_conductance_ChIN_thalamic
thalamicsynapses.append([stim_thalamic_B,nc_thalamic_B])
if random_stream_TH in position_for_thalamic_input:
#print('attaching ChIN thalamic')
#print(cellssynapseattached[cells])
stim_thalamic=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_thalamic["Unknown[0]"]
stim_thalamic.U=synapse["U"]
stim_thalamic.tau=synapse['tau']
stim_thalamic.tauR=synapse['tauR']
stim_thalamic.tauR=synapse['tauF']
stim_thalamic.nmda_ratio=synapse['nmda_ratio']
stimulation_burst=Burst_activity_Th["input_"+str(random_stream_TH)]
nc_thalamic = new_sim.neuron.h.NetCon(stimulation_burst,stim_thalamic)
nc_thalamic.delay=1
nc_thalamic.threshold=0
nc_thalamic.weight[0]=synapse_conductance_ChIN_thalamic
Synapse_TOTAL['ChIN_TH']=Synapse_TOTAL['ChIN_TH']+1
thalamicsynapses.append([stim_thalamic,nc_thalamic])
if random_stream_Crtx_ChIN in position_background_for_cortical_input_ChIN:
print('attaching ChIN Cortical')
#print(cellssynapseattached[cells])
stim_cortical_B=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_cortical_MR["Unknown[0]"]
stim_cortical_B.U=synapse['U']
stim_cortical_B.tau=synapse['tau']
stim_cortical_B.tauR=synapse['tauR']
stim_cortical_B.tauR=synapse['tauF']
stim_cortical_B.nmda_ratio=synapse['nmda_ratio']
stimulation_Crtx_ChIN_B=Background_activity_Crtx_ChIN["input_"+str(random_stream_Crtx_ChIN)]
nc_cortical_B = new_sim.neuron.h.NetCon(stimulation_Crtx_ChIN_B,stim_cortical_B)
nc_cortical_B.delay=1
nc_cortical_B.threshold=0
nc_cortical_B.weight[0]=synapse_conductance_ChIN_cortical
corticalsynapses.append([stim_cortical_B,nc_cortical_B])
if random_stream_Crtx_ChIN in position_cortical_input_ChIN:
stim_cortical=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_cortical_MR["Unknown[0]"]
stim_cortical.U=synapse['U']
stim_cortical.tau=synapse['tau']
stim_cortical.tauR=synapse['tauR']
stim_cortical.tauR=synapse['tauF']
stim_cortical.nmda_ratio=synapse['nmda_ratio']
stimulation_Crtx_ChIN=activity_Crtx_ChIN["input_"+str(random_stream_Crtx_ChIN)]
nc_cortical = new_sim.neuron.h.NetCon(stimulation_Crtx_ChIN,stim_cortical)
nc_cortical.delay=1
nc_cortical.threshold=0
nc_cortical.weight[0]=synapse_conductance_ChIN_cortical
corticalsynapses.append([stim_cortical,nc_cortical])
Synapse_TOTAL['ChIN_Crtx']=Synapse_TOTAL['ChIN_Crtx']+1
random_stream_TH=random_stream_TH+1
random_stream_Crtx_ChIN=random_stream_Crtx_ChIN+1
elif cellssynapseattached[cells] in sec.name() and "Unknown[1]" in sec.name() and "soma" not in sec.name() and "axon" not in sec.name():
for seg in sec:
for k in range(4):
if random_stream_Crtx_LTS in position_background_for_cortical_input_LTS:
stim_cortical_B=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_cortical_MR["Unknown[1]"]
stim_cortical_B.U=synapse['U']
stim_cortical_B.tau=synapse['tau']
stim_cortical_B.tauR=synapse['tauR']
stim_cortical_B.tauR=synapse['tauF']
stim_cortical_B.nmda_ratio=synapse['nmda_ratio']
stimulation_B=Background_activity_Crtx_LTS["input_"+str(random_stream_Crtx_LTS)]
nc_cortical_B = new_sim.neuron.h.NetCon(stimulation_B,stim_cortical_B)
nc_cortical_B.delay=1
nc_cortical_B.threshold=0
nc_cortical_B.weight[0]=synapse_conductance_LTS_cortical
corticalsynapses.append([stim_cortical_B,nc_cortical_B])
if random_stream_Crtx_LTS in position_cortical_input_LTS:
stim_cortical=new_sim.neuron.h.tmGlut(seg)
synapse=synapse_config_cortical_MR["Unknown[1]"]
stim_cortical.U=synapse['U']
stim_cortical.tau=synapse['tau']
stim_cortical.tauR=synapse['tauR']
stim_cortical.tauR=synapse['tauF']
stim_cortical.nmda_ratio=synapse['nmda_ratio']
stimulation=activity_Crtx_LTS["input_"+str(random_stream_Crtx_LTS)]
nc_cortical = new_sim.neuron.h.NetCon(stimulation,stim_cortical)
nc_cortical.delay=1
nc_cortical.threshold=0
nc_cortical.weight[0]=synapse_conductance_LTS_cortical
corticalsynapses.append([stim_cortical,nc_cortical])
Synapse_TOTAL['LTS_Crtx']=Synapse_TOTAL['LTS_Crtx']+1
random_stream_Crtx_LTS=random_stream_Crtx_LTS+1
info_cell.update({"spike_con": recording_netcon})
info_cell.update({"spike_train": spike_time})
info_cell.update({"soma_voltage":vSave})
Cells.update({"ChIN_"+str(cells):info_cell})
'''
#######################################
Implementing the network
########################################
'''
allsynapses=list()
"CONSTRUCTING THE NETWORK"
Muscarinic=True
NO=True
for sec in new_sim.neuron.h.allsec():
for seg in sec:
if str(Cells['ChIN_0']['soma_access']) in str(seg):
#print(str(Cells['ChIN_0']['soma_access']))
print('ATTACHING CHIN SPIKING TO THE LTS')
ChINprojectingsynapses = list()
ChINnicotinicsynapses = list()
Muscarinic_true = list()
for seci in new_sim.neuron.h.allsec():
for segi in seci:
if str('Unknown[1]') in str(segi) and str('axon') not in str(segi) and Muscarinic==True:
print('Muscarinic intracellular')
print(segi)
ach_conce = new_sim.neuron.h.concACh(segi)
pointer_concentration=ach_conce._ref_concentration
Minput = new_sim.neuron.h.M4(segi)
new_sim.neuron.h.setpointer(pointer_concentration,'conc_ACH',Minput)
pointer_modulation=Minput._ref_Ach_M4R
new_sim.neuron.h.setpointer(pointer_modulation,'muscarinic_modulation', segi.kir23_lts)
Muscarinic_true.append([ach_conce,Minput,pointer_modulation,pointer_concentration])
muscarinic_recording= new_sim.neuron.h.Vector()
muscarinic_recording.record(Minput._ref_Ach_M4R)
acetyl_recording= new_sim.neuron.h.Vector()
acetyl_recording.record(ach_conce._ref_concentration)
if Muscarinic==True:
for syn in Muscarinic_true:
print("Adding muscarinic")
nc_musc = new_sim.neuron.h.NetCon(getattr(seg,'_ref_v'),syn[0])
nc_musc.delay=0
nc_musc.threshold=0
nc_musc.weight[0]= parameters_file["muscarinic_weight"]
muscsynapses.append([nc_musc,syn[0]])
elif str(Cells['ChIN_1']['soma_access']) in str(seg):
#print(str(Cells['ChIN_1']['soma_access']))
print('ATTACHING LTS SPIKING TO THE ChIN')
LTSHzChecker = new_sim.neuron.h.IntFire1()
LTSHzChecker.tau=1000
LTSHzChecker.refrac = 10
Int_LTS=new_sim.neuron.h.Vector()
Int_LTS.record(LTSHzChecker._ref_m)
nc_LTS = neuron.h.NetCon(getattr(seg,'_ref_v'),LTSHzChecker)
nc_LTS.weight[0] = Checkerweight[1]
nc_LTS.delay =0
nc_LTS.threshold =-40
Nitric_oxide = list()
for seci in new_sim.neuron.h.allsec():
for segi in seci:
if NO==True and str(Cells['ChIN_0']['soma_access']) in str(segi):
print("NO")
NOinput = new_sim.neuron.h.NO(segi)
Nitric_oxide.append(NOinput)
if NO==True:
for syn in Nitric_oxide:
print("Nitric oxide")
nc_no = new_sim.neuron.h.NetCon(LTSHzChecker,syn)
nc_no.delay=0
nc_no.threshold=0
nc_no.weight[0]= parameters_file["nitric_oxide_weight"]
NOsynapses.append(nc_no)
for sec in new_sim.neuron.h.allsec():
if "" in sec.name():
print(new_sim.neuron.h.psection())
tSave = new_sim.neuron.h.Vector()
tSave.record(new_sim.neuron.h._ref_t)
print("Input to network")
print("----------------")
print(random_stream_Crtx_LTS)
print("---------------- \n")
print(Synapse_TOTAL)
print(random_stream_TH)
print("Simulating whole network \n")
print(str(configs) + '\n')
new_sim.neuron.h.tstop= parameters_file["simulation_time"]
new_sim.neuron.h.run()
k=0
name_cells={"ChIN_0":"ChIN","ChIN_1":"LTS"}
for cell,cell_info in Cells.items():
print(cell)
cell_v=cell_info["soma_voltage"]
plt.figure(k)
plt.plot(tSave,cell_v,label=name_cells[cell],c='black')
plt.title("ChIN_LTS")
plt.legend()
#plt.show()
plt.savefig('Output/Fig-8/'+parameters_file["single-cell-models"][0].split("/")[-1].split(".")[0]+'-cortex'+str(cortex_frequency)+'-thalamus-'+str(thalamus_frequency)+'-checker-'+str(checkerweight)+str(Synapse_TOTAL)+str(name_cells[cell])+'.svg')
np.savetxt('Output/Fig-8/'+parameters_file["single-cell-models"][0].split("/")[-1].split(".")[0]+'-voltage-cortex-'+str(cortex_frequency)+'-thalamus-'+str(thalamus_frequency)+'-checker-'+str(checkerweight)+str(Synapse_TOTAL)+name_cells[cell]+'.txt',[tSave,cell_v])
plt.clf()
k=k+1
plt.figure(2)
plt.plot(tSave,Int_LTS,label='LTS')
plt.title('Checker on LTS')
plt.legend()
plt.savefig('Output/Fig-8/'+parameters_file["single-cell-models"][0].split("/")[-1].split(".")[0]+'-Checker-LTS-cortex-'+str(cortex_frequency)+'-thalamus-'+str(thalamus_frequency)+'-checker-'+str(checkerweight)+str(Synapse_TOTAL)+'.svg')
plt.clf()
plt.figure(4)
plt.plot(tSave,muscarinic_recording)
plt.title('Muscarinic recording')
plt.savefig('Output/Fig-8/'+parameters_file["single-cell-models"][0].split("/")[-1].split(".")[0]+'-M4-recording-cortex-'+str(cortex_frequency)+'-thalamus-'+str(thalamus_frequency)+'-checker-'+str(checkerweight)+str(Synapse_TOTAL)+'.svg')
plt.clf()
plt.figure(5)
plt.plot(tSave,acetyl_recording)
plt.title('Acetylcholine activation')
plt.savefig('Output/Fig-8/'+parameters_file["single-cell-models"][0].split("/")[-1].split(".")[0]+'-Acetylcholine-cortex-'+str(cortex_frequency)+'-thalamus-'+str(thalamus_frequency)+'-checker-'+str(checkerweight)+str(Synapse_TOTAL)+'.svg')
plt.clf()
print('wall time: {}s'.format(time.time() - start_time))