2018023/run_simulation_python3.py at main · ModelDBRepository/2018023 · GitHub

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#!/usr/bin/python

from neuron import h

from neuron import gui

import numpy as np

import os

import pickle

from scipy.stats.stats import pearsonr

import sys

sys.path.append(os.getcwd())

import itertools

print ('Setting cell model...')

neuron_num = 9

synaptic_map_file_name = 'synapse_location_for_neuron_{}.pickle'.format(neuron_num)

rate_E = 1.75

rate_I = 10

# make "slice" version with no synaptic activity tmm 20240529

rate_E = 0

rate_I = 0

h.load_file('model_files/{}.hoc'.format(neuron_num))

h.v_init = -80

h.dt = 0.025

def set_cell(neuron_num):

h.load_file('model_files/{}.hoc'.format(neuron_num))

h('''

objref somatic, apical, axonal, basal

somatic = new SectionList()

basal = new SectionList()

apical = new SectionList()

axonal = new SectionList()

celsius=37

proc distribute(){local x localobj sl

strdef stmp,distfunc,mech

sl = $o1

mech = $s2

distfunc = $s3

sprint(distfunc,"%%s %s(%%f) = %s",mech, distfunc)

forsec sl for(x) {

sprint (stmp,distfunc,secname(),x,distance(x))

execute(stmp)

}

create axon[2]''')

h('''

access axon[0]

{

L = 30

diam = 0.66

nseg = 30

all.append()

axonal.append()

}

access axon[1]

{

L = 30

diam = 0.33

nseg = 30

all.append()

axonal.append()

}

nSecAxonal = 2

soma connect axon[0](0), 1

axon[0] connect axon[1](0), 1

''')

h("celsius = 37.0")

for sec in h.all:

if sec.name().startswith('soma') or sec.name().startswith('axon'): continue

h("{}.nseg = {}".format(sec.name(), max(eval('h.{}.L'.format(sec.name())), 1)))

for sec in h.all:

if sec.name().startswith('dend_5') or sec.name().startswith('apic'):

h('access {}'.format(sec.name()))

h('apical.append()')

h.pop_section()

elif sec.name().startswith('dend_7'):

h('access {}'.format(sec.name()))

h('basal.append()')

h.pop_section()

elif 'soma' in sec.name() or sec.name().startswith('dend_0'):

h('access {}'.format(sec.name()))

h('somatic.append()')

h.pop_section()

elif 'axon' in sec.name():

h('access {}'.format(sec.name()))

h('axonal.append()')

h.pop_section()

else:

print('no section list for {}'.format(sec.name()))

h('''

forsec all {

insert pas

}

forsec all {

e_pas = -75

}

forsec all {

Ra = 150

}

forsec all {

cm = 1

}

forsec all {

g_pas = 1.0 / 12000.0

}

forsec apical {

cm = 1

}

forsec basal {

cm = 1

}

''')

def active_soma():

h('''

forsec somatic {

insert Ca_HVA

insert SKv3_1

insert SK_E2

insert Ca_LVAst

insert Ih

insert NaTs2_t

insert CaDynamics_E2

}

forsec axonal {

insert Ca_HVA

insert SKv3_1

insert SK_E2

insert CaDynamics_E2

insert Nap_Et2

insert K_Pst

insert K_Tst

insert Ca_LVAst

insert NaTa_t

}

forsec axonal {

ena = 50

ek = -85

}

forsec somatic {

ena = 50

}

forsec somatic {

ek = -85

}

access soma

distance()

distribute(axonal,"gNaTa_tbar_NaTa_t","(0.0 * %g + 1.0)*3.429725")

distribute(axonal,"gK_Tstbar_K_Tst","(0.0 * %g + 1.0)*0.001035")

distribute(axonal,"gamma_CaDynamics_E2","(0.0 * %g + 1.0)*0.016713")

distribute(axonal,"gNap_Et2bar_Nap_Et2","(0.0 * %g + 1.0)*0.009803")

distribute(axonal,"gSK_E2bar_SK_E2","(0.0 * %g + 1.0)*0.008085")

distribute(axonal,"gCa_HVAbar_Ca_HVA","(0.0 * %g + 1.0)*0.000306")

distribute(axonal,"gK_Pstbar_K_Pst","(0.0 * %g + 1.0)*0.959296")

distribute(axonal,"gSKv3_1bar_SKv3_1","(0.0 * %g + 1.0)*0.094971")

distribute(axonal,"decay_CaDynamics_E2","(0.0 * %g + 1.0)*384.114655")

distribute(axonal,"gCa_LVAstbar_Ca_LVAst","(0.0 * %g + 1.0)*0.000050")

distribute(somatic,"gamma_CaDynamics_E2","(0.0 * %g + 1.0)*0.000533")

distribute(somatic,"gSKv3_1bar_SKv3_1","(0.0 * %g + 1.0)*0.102517")

distribute(somatic,"gSK_E2bar_SK_E2","(0.0 * %g + 1.0)*0.099433")

distribute(somatic,"gCa_HVAbar_Ca_HVA","(0.0 * %g + 1.0)*0.000374")

distribute(somatic,"gNaTs2_tbar_NaTs2_t","(0.0 * %g + 1.0)*0.926705")

distribute(somatic,"gIhbar_Ih","(0.0 * %g + 1.0)*0.000080")

distribute(somatic,"decay_CaDynamics_E2","(0.0 * %g + 1.0)*342.544232")

distribute(somatic,"gCa_LVAstbar_Ca_LVAst","(0.0 * %g + 1.0)*0.000778")

''')

def active_dendrites_tuned():

h('''

forsec basal {

insert Ih

}

forsec apical {

insert Im

insert NaTs2_t

insert SKv3_1

insert Ih

}

forsec apical {

ena = 50

ek = -85

}

access soma

distance()

distribute(basal,"gIhbar_Ih","(0.0 * %g + 1.0)*0.000080")

distribute(apical,"gNaTs2_tbar_NaTs2_t","(0.0 * %g + 1.0)*0.008009") // based on ttx comparison

// distribute(apical,"gNaTs2_tbar_NaTs2_t","(0.0 * %g + 1.0)*0.012009") // original

distribute(apical,"gSKv3_1bar_SKv3_1","(0.0 * %g + 1.0)*0.000513")

distribute(apical,"gIhbar_Ih","(-0.869600 + 2.087000*exp((%g-0.000000)*0.020100))*0.000080") // based on attenuation comparison

// distribute(apical,"gIhbar_Ih","(-0.869600 + 2.087000*exp((%g-0.000000)*0.003100))*0.000080") // original

distribute(apical,"gImbar_Im","(0.0 * %g + 1.0)*0.000740")

''')

set_cell(neuron_num)

active_soma()

active_dendrites_tuned()

print ('Setting parameters...')

duration_of_simulation = 1000

number_of_inhibitory_synapses_on_soma = 100

(spine_locations, full_IS_locations) = pickle.load(open('model_files/{}'.format(synaptic_map_file_name), 'rb'), encoding='latin1')

full_IS_locations = list(full_IS_locations)

for loc in np.linspace(0, 1, number_of_inhibitory_synapses_on_soma):

full_IS_locations.append(('soma', loc, 'soma',0,0))

h.tstop = duration_of_simulation

number_of_E_synapses = len(spine_locations)

number_of_I_synapses = len(full_IS_locations)

print ('Set recording...')

voltage_vectors = []

print ('Placing synapses...')

background_E_events = np.random.poisson(rate_E / 1000.0, size=(number_of_E_synapses, duration_of_simulation))

background_I_events = np.random.poisson(rate_I / 1000.0, size=(number_of_I_synapses, duration_of_simulation))

eSynlist = []

eNetconlist = []

iSynlist = []

iNetconlist = []

E_vcs = []

I_vcs = []

E_vcs_events = []

I_vcs_events = []

def placeNMDA(location, conductance):

eSynlist.append(h.ProbAMPANMDA2_RATIO(float(location)))

eSynlist[-1].gmax = conductance

eSynlist[-1].mgVoltageCoeff = 0.08

eNetconlist.append(h.NetCon(E_vcs[-1], eSynlist[-1]))

eNetconlist[-1].weight[0] = 1

eNetconlist[-1].delay = 0

def placeGABA(location, conductance):

iSynlist.append(h.ProbUDFsyn2_lark(float(location)))

iSynlist[-1].tau_r = 0.18

iSynlist[-1].tau_d = 5

iSynlist[-1].e = - 80

iSynlist[-1].gmax = conductance

iNetconlist.append(h.NetCon(I_vcs[-1], iSynlist[-1]))

iNetconlist[-1].weight[0] = 1

iNetconlist[-1].delay = 0

for sec in h.all:

if sec.name().startswith('soma') or sec.name().startswith('axon'): continue

h("{}.nseg = {}".format(sec.name(), max(eval('h.{}.L'.format(sec.name())), 1)))

for sec in h.all:

voltage_vectors.append(h.Vector())

voltage_vectors[-1].record(eval('h.{}({})._ref_v'.format(sec.name(), 0.5)))

spine_ind = 0

spine_firing_times = []

for sec, loc, branch_type, spine_head_width, spine_volume, spine_length in spine_locations:

h("access {}".format(sec))

h("nseg = {}".format(max(h.L, 1)))

E_vcs.append(h.VecStim())

E_vcs_events.append(h.Vector())

spine_firing_times.append([])

events = np.where(background_E_events[spine_ind, :].flatten())[0] + 100

for event in events:

E_vcs_events[-1].append(event)

spine_firing_times[-1].append(event)

E_vcs[-1].play(E_vcs_events[-1])

placeNMDA(loc, 0.0004)

spine_ind += 1

h.pop_section()

IS_ind = 0

IS_firing_times = []

for sec, loc, branch_type, IS_volume, on_spine in full_IS_locations:

h("access {}".format(sec))

h("nseg = {}".format(max(h.L, 1)))

I_vcs.append(h.VecStim())

I_vcs_events.append(h.Vector())

IS_firing_times.append([])

events = np.where(background_I_events[IS_ind, :].flatten())[0] + 100

for event in events:

I_vcs_events[-1].append(event)

IS_firing_times[-1].append(event)

I_vcs[-1].play(I_vcs_events[-1])

h("access {}".format(sec))

placeGABA(loc, 0.001)

h.pop_section()

IS_ind += 1

firing_times = np.zeros(shape=(int(number_of_E_synapses), int(h.tstop) + 100))

for ind in range(len(spine_firing_times)):

firing_times[ind, spine_firing_times[ind]] = 1

print("setting up soma_voltageVector, timeVector recording")

soma_voltageVector = h.Vector()

soma_voltageVector.record(h.soma(0.5)._ref_v)

timeVector = h.Vector()

timeVector.record(h._ref_t)

E_densities = []

I_densities = []

# turn off running so can import the model from this file/modify and then run

"""

print ('Running...')

h.stdinit()

h.run()

voltage_vectors = np.array(voltage_vectors)

timeVector = np.array(timeVector)

"""

2018023/run_simulation_python3.py at main · ModelDBRepository/2018023 · GitHub

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