PLOTLIB_INLINE PVec::PVec( double min, double max, size_t n )
: PVec( LinSpace( min, max, n ) )
{
}
void main(int argc, char **argv) {
// ***** DECLARATION *****//
int dim = 4;
double *vstart, *spkTimes;;
double x1 = 0, // simulation start time
x2 = 100, // simulation end time
thetaStep = 0;
int nSteps, nThetaSteps;
FILE *fp;
int kNeuron, clmNo, loopIdx=0;
long idem;
// ***** INITIALIZATION *****//
dt = DT;
nSteps = (int)((x2 - x1) / dt);
xx = vector(1, nSteps);
y = matrix(1, N_StateVars * N_Neurons, 1, nSteps);
input_cur = vector(1, nSteps);
vstart = vector(1, N_StateVars * N_Neurons);
IF_SPK = vector(1, N_Neurons);
expSum = vector(1, N_Neurons); // synap input
iSynap = vector(1, N_Neurons);
gEE = vector(1, NE);
gEI = vector(1, NE);
gIE = vector(1, NI);
gII = vector(1, NI);
spkTimes = vector(1, nSteps);
conMat = matrix(1, N_Neurons, 1, N_Neurons);
gaussNoiseE = vector(1, NE);
gaussNoiseI = vector(1, NI);
iBg = vector(1, N_Neurons);
gFF = vector(1, N_Neurons);
iFF = vector(1, N_Neurons);
rTotal = vector(1, N_Neurons);
rTotalPrev = vector(1, N_Neurons);
tempRandnPrev = vector(1, N_Neurons);
tempRandnNew = vector(1, N_Neurons);
tempCurE = vector(1, N_Neurons);
tempCurI = vector(1, N_Neurons);
Itgrl = vector(1, N_Neurons);
ItgrlOld = vector(1, N_Neurons);
randnXiA = vector(1, N_Neurons);
randwZiA = matrix(1, N_Neurons, 1, 4);
randuDelta = vector(1, N_Neurons);
randuPhi = matrix(1, N_Neurons, 1, 3);
spkTimesFp = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/spkTimes","w");
outVars = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/outvars", "w");
isynapFP = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/isynapEI", "w");
rTotalFP = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/rTotal", "w");
gbgrndFP = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/gBg", "w");
srand(time(NULL)); // set the seed for random number generator
// genConMat(); // Generate conection matrix
GenConMat02();
AuxRffTotal(); /* auxillary function, generates random variables for the
simulation run; which are used approximating FF input */
if(thetaStep > 0) {
thetaVec = vector(1, 360 / thetaStep);
LinSpace(0, 360, thetaStep, thetaVec, &nThetaSteps);
}
else {
thetaVec = vector(1, 1);
thetaVec[1] = 0;
nThetaSteps = 1;
}
printf("theta = %f %d\n", thetaVec[1], nThetaSteps);
/* /\********\/ */
//conMat[1][1] = 0;
// conMat[1][2] = 1;
//for(loopIdx = 3; loopIdx <=N_Neurons; ++loopIdx) {
//conMat[loopIdx][2] = 1;
// }
// conMat[1][3] = 1; */
// conMat[2][1] = 0;
//conMat[2][2] = 0;
/* conMat[2][3] = 1; */
/* conMat[3][1] = 1; */
/* conMat[3][2] = 0; */
/* conMat[3][3] = 0; */
// compute
printf("\nNE = %d\n", NE);
printf("\nNI = %d\n", NI);
printf("\nK = %d\n", (int)K);
printf("computing...\n");
//***** PARSE INPUT ARGS *****//
if(argc > 1) {
theta = atof(argv[2]);
contrast = atof(argv[3]);
muE = atof(argv[4]);
muI = atof(argv[5]);
// current pulse - tStart, tStop, stepSize
for(loopIdx = 1; loopIdx < nSteps+1; ++loopIdx) {
if(loopIdx * dt > atof(argv[6]) && loopIdx *dt <= atof(argv[7])) {
input_cur[loopIdx] = atof(argv[8]);
}
else {
input_cur[loopIdx] = 0;
}
}
}
else {
theta = 0;
contrast = 0.25;
muE = 0.1;
muI = 0.1;
for(loopIdx =1; loopIdx < nSteps+1; ++loopIdx) {
input_cur[loopIdx] = 0;
}
}
//***** INITIALIZE STATE VARIABLES *****//
for(kNeuron = 1; kNeuron < N_Neurons + 1; ++kNeuron) {
clmNo = (kNeuron - 1) * N_StateVars;
idem = -1 * rand();
vstart[1 + clmNo] = -70 + 40 * ran1(&idem); // Vm(0) ~ U(-70, -30)
vstart[2 + clmNo] = 0.3176;
vstart[3 + clmNo] = 0.1;
vstart[4 + clmNo] = 0.5961;
}
//***** INTEGRATE *****//
for(loopIdx = 1; loopIdx <= nThetaSteps; ++loopIdx) {
theta = thetaVec[loopIdx];
fprintf(spkTimesFp, "%f %f\n", theta, theta);
rkdumb(vstart, N_StateVars * N_Neurons, x1, x2, nSteps, derivs);
fprintf(spkTimesFp, "%d %d\n", 11, 11); // delimiters for thetas
fprintf(spkTimesFp, "%d %d\n", 73, 73);
}
printf("Done! \n");
fclose(spkTimesFp);
//***** SAVE TO DISK *****//
fp = fopen("/home/shrisha/Documents/cnrs/results/network_model_outFiles/outputFile.csv", "w");
for(loopIdx = 1; loopIdx < nSteps+1; ++loopIdx) {
fprintf(fp, "%f ", xx[loopIdx]);
for(kNeuron = 1; kNeuron < N_Neurons + 1; ++kNeuron) {
clmNo = (kNeuron - 1) * N_StateVars;
// y = [t, V_m, n, z, h, I_input]
// fprintf(fp, "%f %f %f %f %f ", y[1 + clmNo][loopIdx], y[2 + clmNo][loopIdx], y[3 + clmNo][loopIdx], y[4 + clmNo][loopIdx], input_cur[loopIdx]);
fprintf(fp, "%f ", y[1 + clmNo][loopIdx]);
}
fprintf(fp, "\n");
if(loopIdx%100 == 0) {
printf("\r%d", loopIdx);
}
}
printf("\n");
printf("nSteps = %d \n", loopIdx);
fclose(fp);
fclose(outVars);
fclose(isynapFP);
fclose(rTotalFP);
fclose(gbgrndFP);
//***** FREE MEMORY *****//
free_vector(expSum, 1, N_Neurons);
free_vector(iSynap, 1, N_Neurons);
free_vector(gEE, 1, NE);
free_vector(gIE, 1, NI);
free_vector(gEI, 1, NE);
free_vector(gII, 1, NI);
free_vector( gaussNoiseE, 1, NE);
free_vector(gaussNoiseI, 1, NI);
free_vector(rTotal, 1, N_Neurons);
free_vector(rTotalPrev, 1, N_Neurons);
free_vector(tempRandnPrev, 1, N_Neurons);
free_vector(tempRandnNew, 1, N_Neurons);
free_vector(iBg, 1, N_Neurons);
free_vector(gFF, 1, N_Neurons);
free_vector(iFF, 1, N_Neurons);
free_vector(tempCurI, 1, N_Neurons);
free_vector(tempCurE, 1, N_Neurons);
free_matrix(conMat, 1, N_Neurons, 1, N_Neurons);
free_matrix(randwZiA, 1, N_Neurons, 1, 4);
free_matrix(randuPhi, 1, N_Neurons, 1, 3);
}
PLOTLIB_INLINE PVec::PVec( double min, double max, size_t n, std::function< double( double ) > function )
: PVec( LinSpace( min, max, n ), function )
{
}
