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Fixed issues
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‎example.txt

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‎maketrace.py

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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Nov 19 13:22:12 2020
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@author: adam
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"""
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import os.path
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import json
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import matplotlib.pyplot as plt
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import numpy as np
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dirs1 = 't42_data'
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ndends = 1 + 20
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apicnum = 6
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starttime = 300
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asBatch = False
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if asBatch:
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filename1 = dirs1 + '/t42_0' + '.json'
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else:
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filename1 = 'model_output.json'
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if os.path.isfile(filename1):
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with open(filename1) as f:
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data = json.load(f)
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pyrind = data['net']['pops']['PYR_pop']['cellGids'][0]
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x1 = np.array(data['simData']['V_soma']['cell_' + str(pyrind)])
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x1all = np.zeros(x1.shape[0]*ndends).reshape(x1.shape[0],ndends)
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x1all[:,0] = x1all[:,0] + x1
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l1 = [key for key, val in data['simData'].items()
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if (key[0:6]=='V_apic' and 'cell_' + str(pyrind) in val)]
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for x, xval1 in enumerate(l1):
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x1 = np.array(data['simData'][xval1]['cell_' + str(pyrind)])
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x1all[:,x+1] = x1all[:,x+1] + x1
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x1all = x1all - x1all[int(starttime-1),:]
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plt.figure('traces2')
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plt.xlim(0,200)
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plt.plot(x1all[int(starttime-20):,0], 'k' , linewidth = 6, label = 'soma')
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plt.plot(x1all[int(starttime-20):,apicnum], 'xkcd:light blue', linewidth = 6, label = 'apical')
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pltaxis = True
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if pltaxis:
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plt.grid(b=True, which='major')
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plt.minorticks_on()
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plt.grid(b=True, which='minor')
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plt.ylabel('membrane potential (mV)')
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plt.xlabel('time (ms)')
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else:
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plt.axis('off')
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if asBatch:
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plt.savefig(dirs1 + '/traces2' + '.png', transparent = True)
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else:
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plt.savefig('traces2' + '.png', transparent = True)

‎mechanisms/DetAMPANMDA.mod

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COMMENT
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/**
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* @file DetAMPANMDA.mod
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* @brief Adapted from ProbAMPANMDA_EMS.mod by Eilif, Michael and Srikanth
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* @author chindemi
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* @date 2014-05-25
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* @remark Copyright © BBP/EPFL 2005-2014; All rights reserved. Do not distribute without further notice.
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*/
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ENDCOMMENT
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TITLE AMPA and NMDA receptor with presynaptic short-term plasticity
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COMMENT
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AMPA and NMDA receptor conductance using a dual-exponential profile
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presynaptic short-term plasticity based on Fuhrmann et al. 2002, deterministic
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version.
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ENDCOMMENT
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NEURON {
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THREADSAFE
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POINT_PROCESS DetAMPANMDA
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RANGE tau_r_AMPA, tau_d_AMPA, tau_r_NMDA, tau_d_NMDA
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RANGE Use, u, Dep, Fac, u0, mg, NMDA_ratio
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RANGE i, i_AMPA, i_NMDA, g_AMPA, g_NMDA, g, e
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NONSPECIFIC_CURRENT i
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RANGE synapseID, verboseLevel
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}
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PARAMETER {
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tau_r_AMPA = 0.2 (ms) : Dual-exponential conductance profile
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tau_d_AMPA = 1.7 (ms) : IMPORTANT: tau_r < tau_d
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tau_r_NMDA = 9.0 (ms) : Dual-exponential conductance profile (from Andrasfalvy and Magee 2001)
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tau_d_NMDA = 61.0 (ms) : IMPORTANT: tau_r < tau_d (from Andrasfalvy and Magee 2001)
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Use = 1.0 (1) : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values)
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Dep = 100 (ms) : Relaxation time constant from depression
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Fac = 10 (ms) : Relaxation time constant from facilitation
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e = 0 (mV) : AMPA and NMDA reversal potential
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mg = 1 (mM) : Initial concentration of mg2+
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mggate
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gmax = .001 (uS) : Weight conversion factor (from nS to uS)
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u0 = 0 : Initial value of u, which is the running value of Use
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NMDA_ratio = 1.22 (1) : The peak ratio of NMDA to AMPA (from Myme et al. 2003)
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synapseID = 0
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verboseLevel = 0
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}
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ASSIGNED {
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v (mV)
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i (nA)
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i_AMPA (nA)
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i_NMDA (nA)
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g_AMPA (uS)
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g_NMDA (uS)
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g (uS)
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factor_AMPA
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factor_NMDA
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}
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STATE {
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A_AMPA : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_r_AMPA
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B_AMPA : AMPA state variable to construct the dual-exponential profile - decays with conductance tau_d_AMPA
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A_NMDA : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_r_NMDA
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B_NMDA : NMDA state variable to construct the dual-exponential profile - decays with conductance tau_d_NMDA
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}
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INITIAL{
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LOCAL tp_AMPA, tp_NMDA
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A_AMPA = 0
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B_AMPA = 0
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A_NMDA = 0
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B_NMDA = 0
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tp_AMPA = (tau_r_AMPA*tau_d_AMPA)/(tau_d_AMPA-tau_r_AMPA)*log(tau_d_AMPA/tau_r_AMPA) :time to peak of the conductance
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tp_NMDA = (tau_r_NMDA*tau_d_NMDA)/(tau_d_NMDA-tau_r_NMDA)*log(tau_d_NMDA/tau_r_NMDA) :time to peak of the conductance
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factor_AMPA = -exp(-tp_AMPA/tau_r_AMPA)+exp(-tp_AMPA/tau_d_AMPA) :AMPA Normalization factor - so that when t = tp_AMPA, gsyn = gpeak
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factor_AMPA = 1/factor_AMPA
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factor_NMDA = -exp(-tp_NMDA/tau_r_NMDA)+exp(-tp_NMDA/tau_d_NMDA) :NMDA Normalization factor - so that when t = tp_NMDA, gsyn = gpeak
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factor_NMDA = 1/factor_NMDA
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}
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BREAKPOINT {
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SOLVE state METHOD cnexp
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mggate = 1 / (1 + exp(0.062 (/mV) * -(v)) * (mg / 2.62 (mM))) :mggate kinetics - Jahr & Stevens 1990 (LJP corrected)
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g_AMPA = gmax*(B_AMPA-A_AMPA) :compute time varying conductance as the difference of state variables B_AMPA and A_AMPA
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g_NMDA = gmax*(B_NMDA-A_NMDA) * mggate :compute time varying conductance as the difference of state variables B_NMDA and A_NMDA and mggate kinetics
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g = g_AMPA + g_NMDA
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i_AMPA = g_AMPA*(v-e) :compute the AMPA driving force based on the time varying conductance, membrane potential, and AMPA reversal
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i_NMDA = g_NMDA*(v-e) :compute the NMDA driving force based on the time varying conductance, membrane potential, and NMDA reversal
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i = i_AMPA + i_NMDA
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}
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DERIVATIVE state{
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A_AMPA' = -A_AMPA/tau_r_AMPA
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B_AMPA' = -B_AMPA/tau_d_AMPA
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A_NMDA' = -A_NMDA/tau_r_NMDA
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B_NMDA' = -B_NMDA/tau_d_NMDA
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}
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NET_RECEIVE (weight,weight_AMPA, weight_NMDA, R, Pr, u, tsyn (ms)){
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LOCAL result
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weight_AMPA = weight
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weight_NMDA = weight * NMDA_ratio
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INITIAL{
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R=1
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u=u0
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tsyn=t
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}
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: calc u at event-
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if (Fac > 0) {
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u = u*exp(-(t - tsyn)/Fac) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
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} else {
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u = Use
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}
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if(Fac > 0){
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u = u + Use*(1-u) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
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}
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R = 1 - (1-R) * exp(-(t-tsyn)/Dep) :Probability R for a vesicle to be available for release, analogous to the pool of synaptic
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:resources available for release in the deterministic model. Eq. 3 in Fuhrmann et al.
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Pr = u * R :Pr is calculated as R * u (running value of Use)
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R = R - u * R :update R as per Eq. 3 in Fuhrmann et al.
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if( verboseLevel > 0 ) {
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printf("Synapse %f at time %g: R = %g Pr = %g erand = %g\n", synapseID, t, R, Pr, result )
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}
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tsyn = t
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A_AMPA = A_AMPA + Pr*weight_AMPA*factor_AMPA
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B_AMPA = B_AMPA + Pr*weight_AMPA*factor_AMPA
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A_NMDA = A_NMDA + Pr*weight_NMDA*factor_NMDA
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B_NMDA = B_NMDA + Pr*weight_NMDA*factor_NMDA
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if( verboseLevel > 0 ) {
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printf( " vals %g %g %g %g\n", A_AMPA, weight_AMPA, factor_AMPA, weight )
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}
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}
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FUNCTION toggleVerbose() {
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verboseLevel = 1-verboseLevel
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}

‎mechanisms/DetGABAAB.mod

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COMMENT
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/**
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* @file DetGABAAB.mod
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* @brief Adapted from ProbGABAA_EMS.mod by Eilif, Michael and Srikanth
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* @author chindemi
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* @date 2014-05-25
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* @remark Copyright © BBP/EPFL 2005-2014; All rights reserved. Do not distribute without further notice.
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*/
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ENDCOMMENT
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TITLE GABAA and GABAB receptor with presynaptic short-term plasticity
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COMMENT
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GABAA and GABAB receptor conductance using a dual-exponential profile
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presynaptic short-term plasticity based on Fuhrmann et al. 2002, deterministic
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version.
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ENDCOMMENT
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NEURON {
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THREADSAFE
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POINT_PROCESS DetGABAAB
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RANGE tau_r_GABAA, tau_d_GABAA, tau_r_GABAB, tau_d_GABAB
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RANGE Use, u, Dep, Fac, u0, GABAB_ratio
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RANGE i, i_GABAA, i_GABAB, g_GABAA, g_GABAB, g, e_GABAA, e_GABAB
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NONSPECIFIC_CURRENT i
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RANGE synapseID, verboseLevel
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}
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PARAMETER {
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tau_r_GABAA = 0.2 (ms) : dual-exponential conductance profile
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tau_d_GABAA = 8 (ms) : IMPORTANT: tau_r < tau_d
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tau_r_GABAB = 3.5 (ms) : dual-exponential conductance profile :Placeholder value from hippocampal recordings SR
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tau_d_GABAB = 260.9 (ms) : IMPORTANT: tau_r < tau_d :Placeholder value from hippocampal recordings
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Use = 1.0 (1) : Utilization of synaptic efficacy
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Dep = 100 (ms) : relaxation time constant from depression
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Fac = 10 (ms) : relaxation time constant from facilitation
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e_GABAA = -80 (mV) : GABAA reversal potential
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e_GABAB = -97 (mV) : GABAB reversal potential
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gmax = .001 (uS) : weight conversion factor (from nS to uS)
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u0 = 0 :initial value of u, which is the running value of release probability
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synapseID = 0
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verboseLevel = 0
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GABAB_ratio = 0 (1) : The ratio of GABAB to GABAA
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}
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ASSIGNED {
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v (mV)
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i (nA)
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i_GABAA (nA)
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i_GABAB (nA)
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g_GABAA (uS)
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g_GABAB (uS)
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g (uS)
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factor_GABAA
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factor_GABAB
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}
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STATE {
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A_GABAA : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAA
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B_GABAA : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAA
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A_GABAB : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAB
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B_GABAB : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAB
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}
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INITIAL{
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LOCAL tp_GABAA, tp_GABAB
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A_GABAA = 0
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B_GABAA = 0
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A_GABAB = 0
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B_GABAB = 0
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tp_GABAA = (tau_r_GABAA*tau_d_GABAA)/(tau_d_GABAA-tau_r_GABAA)*log(tau_d_GABAA/tau_r_GABAA) :time to peak of the conductance
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tp_GABAB = (tau_r_GABAB*tau_d_GABAB)/(tau_d_GABAB-tau_r_GABAB)*log(tau_d_GABAB/tau_r_GABAB) :time to peak of the conductance
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factor_GABAA = -exp(-tp_GABAA/tau_r_GABAA)+exp(-tp_GABAA/tau_d_GABAA) :GABAA Normalization factor - so that when t = tp_GABAA, gsyn = gpeak
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factor_GABAA = 1/factor_GABAA
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factor_GABAB = -exp(-tp_GABAB/tau_r_GABAB)+exp(-tp_GABAB/tau_d_GABAB) :GABAB Normalization factor - so that when t = tp_GABAB, gsyn = gpeak
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factor_GABAB = 1/factor_GABAB
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}
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BREAKPOINT {
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SOLVE state METHOD cnexp
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g_GABAA = gmax*(B_GABAA-A_GABAA) :compute time varying conductance as the difference of state variables B_GABAA and A_GABAA
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g_GABAB = gmax*(B_GABAB-A_GABAB) :compute time varying conductance as the difference of state variables B_GABAB and A_GABAB
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g = g_GABAA + g_GABAB
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i_GABAA = g_GABAA*(v-e_GABAA) :compute the GABAA driving force based on the time varying conductance, membrane potential, and GABAA reversal
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i_GABAB = g_GABAB*(v-e_GABAB) :compute the GABAB driving force based on the time varying conductance, membrane potential, and GABAB reversal
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i = i_GABAA + i_GABAB
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}
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DERIVATIVE state{
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A_GABAA' = -A_GABAA/tau_r_GABAA
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B_GABAA' = -B_GABAA/tau_d_GABAA
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A_GABAB' = -A_GABAB/tau_r_GABAB
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B_GABAB' = -B_GABAB/tau_d_GABAB
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}
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NET_RECEIVE (weight,weight_GABAA, weight_GABAB, R, Pr, u, tsyn (ms)){
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LOCAL result
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weight_GABAA = weight
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weight_GABAB = weight * GABAB_ratio
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INITIAL{
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R=1
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u=u0
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tsyn=t
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}
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: calc u at event-
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if (Fac > 0) {
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u = u*exp(-(t - tsyn)/Fac) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
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} else {
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u = Use
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}
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if(Fac > 0){
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u = u + Use*(1-u) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
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}
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R = 1 - (1-R) * exp(-(t-tsyn)/Dep) :Probability R for a vesicle to be available for release, analogous to the pool of synaptic
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:resources available for release in the deterministic model. Eq. 3 in Fuhrmann et al.
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Pr = u * R :Pr is calculated as R * u (running value of Use)
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R = R - u * R :update R as per Eq. 3 in Fuhrmann et al.
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if( verboseLevel > 0 ) {
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printf("Synapse %f at time %g: R = %g Pr = %g erand = %g\n", synapseID, t, R, Pr, result )
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}
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tsyn = t
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A_GABAA = A_GABAA + Pr*weight_GABAA*factor_GABAA
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B_GABAA = B_GABAA + Pr*weight_GABAA*factor_GABAA
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A_GABAB = A_GABAB + Pr*weight_GABAB*factor_GABAB
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B_GABAB = B_GABAB + Pr*weight_GABAB*factor_GABAB
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if( verboseLevel > 0 ) {
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printf( " vals %g %g %g %g\n", A_GABAA, weight_GABAA, factor_GABAA, weight )
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}
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}
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FUNCTION toggleVerbose() {
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verboseLevel = 1-verboseLevel
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}

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