Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b)

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Accession:206380
This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron.
Reference:
1 . Morozova EO, Myroshnychenko M, Zakharov D, di Volo M, Gutkin B, Lapish CC, Kuznetsov A (2016) Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting. J Neurophysiol 116:1900-1923 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Substantia nigra pars compacta DA cell; Ventral tegmental area dopamine neuron; Ventral tegmental area GABA neuron ;
Channel(s): I K,Ca; I Calcium; I Na,t; I Potassium;
Gap Junctions:
Receptor(s): GabaA; NMDA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate; Dopamine;
Simulation Environment: MATLAB; XPP;
Model Concept(s): Action Potentials; Bifurcation; Bursting; Synaptic Convergence;
Implementer(s): Morozova, Ekaterina O [emorozov at indiana.edu]; Kuznetsov, Alexey ;
Search NeuronDB for information about:  Substantia nigra pars compacta DA cell; GabaA; AMPA; NMDA; I Na,t; I K,Ca; I Calcium; I Potassium; Dopamine; Gaba; Glutamate;
#include <stdio.h>
#include <stdlib.h>
#include "mex.h"         
#include "matrix.h"      
#include <string.h> 
#include <math.h>
#include <time.h>
#include <iostream>

//---------- parameters ----------------------
double dt=0.02, TT, tgl; //step, total time, global time
int N, NG=30, Neq=8+NG*4;// # of steps,  # of GABA neurons, # of equations
double gL=0.18, gbarK=1, gbarCa=2.5, gbarKCa=7.8, gSNa=0.13; //maximal conductances
double gCa, gKCa, gK; //conductances
double gampa=0, gnmda, gbarnmda, ggaba; //synaptic conductances
double nmdasig, ampasig, allgaba; //synaptic activation functions
double EL=-35, EK=-90, ENa=55, ECa=50, ENMDA=0, EAMPA=0, EGABA=-90; // reversal potentials
double k=160, buf=0.00023, zF=0.019298, tc=0.52, r=0.2; //for Ca  balance equation
double caleak=0.1; // fraction of Ca current in a leak current
double Vcah=-52, Sca=3; // for Ca current
double VHK=-10, VSK=7; //for K current
double vhna=-50, slna=5; // for subthreshold Na current
double aact=1., tades=6.1, adesrel=40, adeact=1.6, nact=7., ndeact=170.; //for AMPA current
double nmdathresh=0.2, nmdasl=0.03, ampathresh=0.2, ampasl=0.03; //for NMDA current
double Mg=0.5, me=0.062; //for gnmda 
double amg, bmg, minfgg, ang, bng, ahg, bhg, gnmdagg, gspikeg, vgnz; // for GABA population
double glg=0.1, gna=22, gdrg=7, tng=1, thg=5, tbn=0.7, as=12, bs=0.1; // for GABA population
double gampag=0, gnmdabarg=0; //GABA synaptic conductances
double inp, Iapp; // synaptic input, input current

double *y, *f;
double *glg1=new double[NG];

void system (void)
{
// for K current    
gK=gbarK/(1. + exp(-(y[0]-VHK)/VSK)); 
// for subthreshold Na current
double na=1/(1+exp(-(y[0]-vhna)/slna));
// for Ca current
double alphac=((fabs(y[0]-Vcah))>0.00001)? (-0.0032*(y[0]-Vcah)/(exp(-(y[0]-Vcah)/Sca) - 1.)) : (-0.0032*0.00001/(exp(-0.00001/Sca)-1.));
double betac=0.05*exp(-(y[0]-Vcah+5)/40.);
double csinf=alphac/(alphac+betac);
double csqr=csinf*csinf;
gCa=gbarCa*(csqr*csqr); // Ca conductance
// for Ca-dependent K current
double ksq=k*k; // K^2
double casq=y[1]*y[1]; // Ca^2
gKCa=gbarKCa*(casq*casq)/((casq*casq) + (ksq*ksq)); //SK conductance
// for NMDAR current
double nmdasig=1/(1+exp(-(y[4]-nmdathresh)/nmdasl));
gnmda=gbarnmda/(1+0.28*Mg*exp(-me*y[0])); //NMDA conductance
//for AMPAR current
double ampasig=1/(1+exp(-(y[2]*y[3]-ampathresh)/ampasl));

// Cumulative activation of GABAR by all GABA neurons in a population
allgaba=0;
for (int ig=0; ig<NG; ig++){
	allgaba+=y[8+ig*4];
}
allgaba/=NG;

f[0]= gnmda*nmdasig*(ENMDA-y[0])+ gampa*ampasig*(EAMPA-y[0])+ ggaba*allgaba*(EGABA-y[0]) + gCa*(ECa-y[0])
+ (gKCa+gK)*(EK-y[0])+ gL*(EL-y[0])+ gSNa*na*(ENa-y[0])+Iapp;// Voltage
f[1]= 2.*buf*((gCa+caleak*gL)*(ECa - y[0])/zF - y[1]/(1*tc))/r; //Ca
f[2]=inp*(1-y[2])/aact-(1-inp)*y[2]/adeact; // AMPA activation
f[3]=(1-inp)*(1-y[3])/adesrel-inp*y[3]/tades; //AMPA desensitization
f[4]=inp*(1-y[4])/nact-(1-inp)*y[4]/ndeact; // NMDA

// model of GABA neurons
for (int ig=0; ig<NG; ig++)
{
// for Na current	
double amg=0.1*(y[5+ig*4]+30.0)/(1.0-exp(-(y[5+ig*4]+30.0)/10.0));
double bmg=4.0*exp(-(y[5+ig*4]+55.0)/18.0);
double minfgg=amg/(amg+bmg);
double ahg=0.07*exp(-(y[5+ig*4]+53.0)/20.0);
double bhg=1.0/(1.0+exp(-(y[5+ig*4]+23.0)/10.0));
// for K current
double ang=0.01*(y[5+ig*4]+29.0)/(1.0-exp(-(y[5+ig*4]+29.0)/10.0));
double bng=tbn*0.125*exp(-(y[5+ig*4]+39.0)/80.0);
// NMDA conductance on GABA neuron
double gnmdagg=gnmdabarg/(1+0.28*Mg*exp(-me*y[5+ig*4]));
// GABA "release" depending on GABA voltage
double gspikeg=1/(1+exp(-y[5+ig*4]/2));

f[5+ig*4]=gnmdagg*nmdasig*(ENMDA-y[5+ig*4]) + gampag*ampasig*(EAMPA-y[5+ig*4])
	-glg1[ig]*(y[5+ig*4]+51)-gna*pow(minfgg,3)*y[7+ig*4]*(y[5+ig*4]-55)
    -gdrg*pow(y[6+ig*4],4)*(y[5+ig*4]+90)+Iapp;
f[6+ig*4]=tng*(ang*(1-y[6+ig*4])-bng*y[6+ig*4]); // K receptor activation 
f[7+ig*4]=thg*(ahg*(1-y[7+ig*4])-bhg*y[7+ig*4]); //Na receptor activation
f[8+ig*4]=as*gspikeg*(1-y[8+ig*4])-bs*(1-gspikeg)*y[8+ig*4]; // GABA receptor activation on the DA neuron
}

}
void euler(double ggaba_1, double gbarnmda_1)
{
ggaba=ggaba_1;
gbarnmda=gbarnmda_1;
 int i=0;
 system();
  while (i<Neq){y[i]+=dt*f[i]; i++;}
} 

//nlhs - Number of expected output mxArrays
//plhs - Array of pointers to the expected output mxArrays
//nrhs - Number of input mxArrays
//prhs - Array of pointers to the input mxArrays 

void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] )
{	
mxArray *tempy = mxCreateDoubleMatrix(1,Neq,mxREAL);
y=mxGetPr(tempy);    
mxArray *tempf = mxCreateDoubleMatrix(1,Neq,mxREAL);
f=mxGetPr(tempf);

TT   = *(mxGetPr(prhs[0]));
double *input, *ggaba1, *gbarnmda1;
input = mxGetPr(prhs[1]);
ggaba1 = (mxGetPr(prhs[2]));
gbarnmda1 = (mxGetPr(prhs[3]));
Iapp = *(mxGetPr(prhs[4]));

int kk;
tgl=0; dt=0.02; N=(int)(TT/dt);
    
double *Vm, *allgaba1, *Ca, *nmdasig1, *mas_Vgaba;  

plhs[0] = mxCreateDoubleMatrix(1,N,mxREAL);
plhs[1] = mxCreateDoubleMatrix(1,N,mxREAL);
plhs[2] = mxCreateDoubleMatrix(1,N,mxREAL);
plhs[3] = mxCreateDoubleMatrix(1,N,mxREAL);
plhs[4] = mxCreateDoubleMatrix(1,NG*N,mxREAL);
    
Vm = mxGetPr(plhs[0]);     
allgaba1 = mxGetPr(plhs[1]);
Ca = mxGetPr(plhs[2]);
nmdasig1 = mxGetPr(plhs[3]);
mas_Vgaba = mxGetPr(plhs[4]);   

 for(int i=0; i<NG;i++)
{ for(int j=0;j<N;j++) mas_Vgaba[i*N+j] = 0; } 

double glg_m[30];

 glg_m[0]=0.0555; glg_m[1]=0.0675; glg_m[2]=0.0250; glg_m[3]=0.0698; glg_m[4]=0.0322;
 glg_m[5]=0.0701; glg_m[6]=0.037; glg_m[7]=0.0647; glg_m[8]=0.0264; glg_m[9]=0.0743; 
 glg_m[10]=0.0592; glg_m[11]=0.0417; glg_m[12]=0.0308; glg_m[13]=0.0480; glg_m[14]=0.0353;
 glg_m[15]=0.0745; glg_m[16]=0.0698; glg_m[17]=0.0706; glg_m[18]=0.0517; glg_m[19]=0.0316;
 glg_m[20]=0.0671; glg_m[21]=0.0462; glg_m[22]=0.0648; glg_m[23]=0.0273; glg_m[24]=0.0325;
 glg_m[25]=0.0692; glg_m[26]=0.073; glg_m[27]=0.054; glg_m[28]=0.0383; glg_m[29]=0.0539;


// initial conditions  
 y[0]=-60.; y[1]=50.; y[2]=0; y[3]=1; y[4]=0;
 for (int ig=0; ig<NG; ig++){y[5+ig*4]=-40; y[6+ig*4]=0; y[7+ig*4]=0; y[8+ig*4]=0.;}
	
   kk=0;
    for(int i=0; i<N; i++)
    {
	for(int ii=0; ii<NG; ii++) glg1[ii]=glg_m[ii];
	tgl+=dt;
	inp = input[i];
	ggaba=ggaba1[i];
	gbarnmda=gbarnmda1[i];
	euler (ggaba, gbarnmda);
	Vm[kk]=y[0];
	Ca[kk]=y[1];
	nmdasig1[i]=y[4];
	allgaba1[i]=allgaba;
	
    for(int ig=0;ig<NG;ig++)
		{		
			mas_Vgaba[ig*N+kk]=y[5+ig*4];	
		}
		
	if(kk>N) break;
kk++;
   }
   return;
} // end mexFunction()

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