//! main function for Conditioning experiment
int main() {
// set up random number generator:
gsl_rng_env_setup();
r = gsl_rng_alloc(gsl_rng_default);
gsl_rng_set(r, RANDOM_SEED);
// initialise the specifice parameters for this simulation.
initDerivedParams();
//char filename[1024];
//sprintf(filename, "data/conditioning_%d_%4f_%d.dat", TRAININGCYCLES, ETA, TAU_ALPHA);
FILE *F = fopen(FILENAME, "w+b"); //! output file name
gsl_vector *psp = gsl_vector_alloc(NPRE); //! to hold PSPs
gsl_vector *pspS = gsl_vector_alloc(NPRE); //! to hold (filtered?) PSPs
gsl_vector *sue = gsl_vector_alloc(NPRE); //! "positive" part of PSP fur integration
gsl_vector *sui = gsl_vector_alloc(NPRE); //! "negative" part of PSP fur integration
gsl_vector *pspTilde = gsl_vector_alloc(NPRE); //! low pass (2nd) filtered PSS
gsl_vector *wB = gsl_vector_alloc(NPRE); //! weights of connections to Bacon neuron
gsl_vector *wW = gsl_vector_alloc(NPRE); //! weights of connections to Water neuron
gsl_vector *wR = gsl_vector_alloc(NPRE); //! weights of connections to Reward neuron
gsl_vector *ou = gsl_vector_alloc(NPRE); //! xxxx
gsl_vector *oum = gsl_vector_alloc(NPRE); //! xxx
gsl_vector *pres = gsl_vector_alloc(NPRE); //! xxx
//! short-hand pointer to the datastructures above.
double *pspP = gsl_vector_ptr(psp,0);
double *pspSP = gsl_vector_ptr(pspS,0);
double *sueP = gsl_vector_ptr(sue,0);
double *suiP = gsl_vector_ptr(sui,0);
double *pspTildeP = gsl_vector_ptr(pspTilde,0);
double *wBP = gsl_vector_ptr(wB,0);
double *wWP = gsl_vector_ptr(wW,0);
double *wRP = gsl_vector_ptr(wR,0);
double *ouP = gsl_vector_ptr(ou,0);
double *oumP = gsl_vector_ptr(oum,0);
double *presP = gsl_vector_ptr(pres,0);
// initialise data structures:
for(int i=0; i<NPRE; i++) {
*(pspP+i) = 0;
*(sueP+i) = 0;
*(suiP+i) = 0;
// random connections to B, W, and R neurons
*(wBP+i) = gsl_ran_gaussian(r, .04) + .07;
*(wWP+i) = gsl_ran_gaussian(r, .04) + .07;
*(wRP+i) = gsl_ran_gaussian(r, .04) + .07;
}
/*! Bacon neuron:
- uB soma potential
- uVB potential from dendrited alone
- rU rate from some
- rVB rate from dentritic input alone
and then the same for W,R neurons
*/
double uB = 0, uVB = 0, rUB = 0, rVB = 0;
double uW = 0, uVW = 0, rUW = 0, rVW = 0;
double uR = 0, uVR = 0, rUR = 0, rVR = 0;
//! we only recorded activites of this many pres
int nOfRecordedPre = 50;
//! this describes the length of the network state we store
int stateLength = 4 * nOfRecordedPre + 12;
//! the states consistes of u, uV, rU, rV of all recorded pres and of B,R,W neurons.
double *state[stateLength];
for(int i = 0; i < nOfRecordedPre; i++) {
*(state + 0*nOfRecordedPre + i) = wBP + i; // points to weight vector of connection to B etc
*(state + 1*nOfRecordedPre + i) = wWP + i;
*(state + 2*nOfRecordedPre + i) = wRP + i;
*(state + 3*nOfRecordedPre + i) = presP + i; // points to presP values
}
*(state + 4*nOfRecordedPre) = &uB; // points to potential u of B etc
*(state + 4*nOfRecordedPre+1) = &uVB;
*(state + 4*nOfRecordedPre+2) = &rUB;
*(state + 4*nOfRecordedPre+3) = &rVB;
*(state + 4*nOfRecordedPre+4) = &uW;
*(state + 4*nOfRecordedPre+5) = &uVW;
*(state + 4*nOfRecordedPre+6) = &rUW;
*(state + 4*nOfRecordedPre+7) = &rVW;
*(state + 4*nOfRecordedPre+8) = &uR;
*(state + 4*nOfRecordedPre+9) = &uVR;
*(state + 4*nOfRecordedPre+10) = &rUR;
*(state + 4*nOfRecordedPre+11) = &rVR;
// pointers to input currents xxx to do with B,W,R
//! \todo why initialised this way?
double *IB, *IW, *IR = I1, *ou_t, uI;
double IRf = 1; //! reward factor
/* Start of simulations */
// repeat for all training cycles
for( int s = 0; s < TRAININGCYCLES; s++) {
// apply Bacon and Water stimulus alternatinglly with corresponding reward
if( s%2==0 ) {
ou_t = OU2; IB = I2; IW = I1; IRf = .5;
} else {
ou_t = OU1; IB = I1; IW = I2; IRf = 1;
}
// now for all time bins:
for( int t = 0; t < TIMEBINS; t++) {
for( int i = 0; i < NPRE; i++) {
mixOUs(ouP + i, ou_t[t * NPRE + i], MIX[t], oumP + i);
updatePre(sueP+i, suiP+i, pspP + i, pspSP + i, pspTildeP + i, *(presP + i) = spiking(DT * phi(*(oumP + i)), gsl_ran_flat(r,0,1)));
}
updateMembrane(&uB, &uVB, &uI, wB, psp, IB[t], 0);
updateMembrane(&uW, &uVW, &uI, wW, psp, IW[t], 0);
updateMembrane(&uR, &uVR, &uI, wR, psp, IRf*IR[t], 0);
//rUB = spiking(phi(uB), gsl_ran_flat(r,0,1)); rVB = phi(uVB);
//rUW = spiking(phi(uW), gsl_ran_flat(r,0,1)); rVW = phi(uVW);
//rUR = spiking(phi(uR), gsl_ran_flat(r,0,1)); rVR = phi(uVR);
//! do calculates on the potentials only, not the actual spikes:
rUB = phi(uB); rVB = phi(uVB);
rUW = phi(uW); rVW = phi(uVW);
rUR = phi(uR); rVR = phi(uVR);
for(int i = 0; i < NPRE; i++) {
updateWeight(wBP + i, rUB, *(pspTildeP+i), rVB, *(pspSP+i));
updateWeight(wWP + i, rUW, *(pspTildeP+i), rVW, *(pspSP+i));
updateWeight(wRP + i, rUR, *(pspTildeP+i), rVR, *(pspSP+i));
}
// write out states after the first 10 cycles:
if(s > TRAININGCYCLES - 9 ) {
for(int i=0; i<stateLength; i++) {
fwrite(*(state+i), sizeof(double), 1, F);
}
}
}
}
gsl_vector_free(psp); gsl_vector_free(pspS); gsl_vector_free(wB); gsl_vector_free(wW); gsl_vector_free(wR);
free(ou); free(oum); free(OU1); free(OU2); free(MIX); free(I1); free(I2);
fclose(F);
return 0;
}
/**
main simulation loop
*/
int main() {
// init own parameters.
initDerivedParams();
// init random generator
gsl_rng_env_setup();
r = gsl_rng_alloc(gsl_rng_default);
gsl_rng_set(r, SEED_MAIN);
// file handle for xxx file
FILE *postF = fopen(FILENAME_POST, FILEPOST_FLAG);
// file handle for xxx file
FILE *preF = fopen(FILENAME_PRE, "wb");
// set up vectors:
// to hold post synaptic potentials [unused??]
gsl_vector *psp = gsl_vector_alloc(NPRE);
// to hold post synaptic potentials 1st filtered
gsl_vector *pspS = gsl_vector_alloc(NPRE);
// to hold "excitatory" part of psp for Euler integration
gsl_vector *sue = gsl_vector_alloc(NPRE);
// to hold "inhibitory" part of psp for Euler integration
gsl_vector *sui = gsl_vector_alloc(NPRE);
// to hold psp 2nd filter
gsl_vector *pspTilde = gsl_vector_alloc(NPRE);
// to hold weights
gsl_vector *w = gsl_vector_alloc(NPRE);
// to hold xxx
gsl_vector *pres = gsl_vector_alloc(NPRE);
// ?? ou XXX \todo
#ifdef PREDICT_OU
gsl_vector *ou = gsl_vector_alloc(N_OU);
gsl_vector *preU = gsl_vector_calloc(NPRE);
gsl_vector *wInput = gsl_vector_alloc(N_OU);
gsl_matrix *wPre = gsl_matrix_calloc(NPRE, N_OU);
double *preUP = gsl_vector_ptr(preU,0);
double *ouP = gsl_vector_ptr(ou,0);
double *wInputP = gsl_vector_ptr(wInput,0);
double *wPreP = gsl_matrix_ptr(wPre,0,0);
#endif
// get pointers to array within the gsl_vector data structures above.
double *pspP = gsl_vector_ptr(psp,0);
double *pspSP = gsl_vector_ptr(pspS,0);
double *sueP = gsl_vector_ptr(sue,0);
double *suiP = gsl_vector_ptr(sui,0);
double *pspTildeP = gsl_vector_ptr(pspTilde,0);
double *wP = gsl_vector_ptr(w,0);
double *presP = gsl_vector_ptr(pres,0);
for(int i=0; i<NPRE; i++) {
// init pspP etc to zero
*(pspP+i) = 0;
*(sueP+i) = 0;
*(suiP+i) = 0;
#ifdef RANDI_WEIGHTS
// Gaussian weights
*(wP+i) = gsl_ran_gaussian(r, .1);
#else
*(wP+i) = 0;
#endif
}
//! OU \todo what for?
#ifdef PREDICT_OU
for(int j=0; j < N_OU; j++) {
*(ouP + j) = gsl_ran_gaussian(r, 1) + M_OU;
*(wInputP + j) = gsl_ran_lognormal(r, 0., 2.)/N_OU/exp(2.)/2.;
for(int i=0; i < NPRE; i++) *(wPreP + j*NPRE + i) = gsl_ran_lognormal(r, 0., 2.)/N_OU/exp(2.)/2.;
}
#endif
// temp variables for the simulation yyyy
double
u = 0, // soma potential.
uV = 0, // some potential from dendrite only (ie discounted
// dendrite potential
rU = 0, // instantneou rate
rV = 0, // rate on dendritic potential only
uI = 0, // soma potential only from somatic inputs
rI = 0, // rate on somatic potential only
uInput = 0; // for OU?
// run simulatio TRAININGCYCLES number of times
for( int s = 0; s < TRAININGCYCLES; s++) {
// for all TIMEBINS
for( int t = 0; t < TIMEBINS; t++) {
#ifdef PREDICT_OU
for(int i = 0; i < N_OU; i++) {
*(ouP+i) = runOU(*(ouP+i), M_OU, GAMMA_OU, S_OU);
}
gsl_blas_dgemv(CblasNoTrans, 1., wPre, ou, 0., preU);
#endif
// update PSP of our neurons for inputs from all presynaptic neurons
for( int i = 0; i < NPRE; i++) {
#ifdef RAMPUPRATE
/** just read in the PRE_ACT and generate a spike and store it in presP -- so PRE_ACT has inpretation of potential */
updatePre(sueP+i, suiP+i, pspP + i, pspSP + i, pspTildeP + i, *(presP + i) = spiking(PRE_ACT[t*NPRE + i], gsl_rng_uniform(r)));
#elif defined PREDICT_OU
//*(ouP+i) = runOU(*(ouP+i), M_OU, GAMMA_OU, S_OU); // why commented out?
updatePre(sueP+i, suiP+i, pspP + i, pspSP + i, pspTildeP + i, *(presP + i) = DT * phi(*(preUP+i)));//spiking(DT * phi(*(preUP+i)), gsl_rng_uniform(r))); // why commented out?
#else
// PRE_ACT intepreated as spikes
updatePre(sueP+i, suiP+i, pspP + i, pspSP + i, pspTildeP + i, *(presP + i) = PRE_ACT[t*NPRE + i]);
#endif
} // endfor NPRE
#ifdef PREDICT_OU
gsl_blas_ddot(wInput, ou, &uInput);
GE[t] = DT * phi(uInput);
#endif
// now update the membrane potential.
updateMembrane(&u, &uV, &uI, w, psp, GE[t], GI[t]);
// now calculate rates from from potentials.
#ifdef POSTSPIKING // usually switch off as learning is faster when
// learning from U
// with low-pass filtering of soma potential from actual
// generation of spikes (back propgating dentric spikes?
rU = GAMMA_POSTS*rU + (1-GAMMA_POSTS)*spiking(DT * phi(u), gsl_rng_uniform(r))/DT;
#else
// simpler -- direct.
rU = phi(u);
#endif
rV = phi(uV); rI = phi(uI);
// now update weights based on rU, RV, the 2nd filtered PSP and
// the pspSP
for(int i = 0; i < NPRE; i++) {
updateWeight(wP + i, rU, *(pspTildeP+i), rV, *(pspSP+i));
}
#ifdef TAUEFF
/**
write rU to postF, but only for the last run of the
simulation and then only before the STIM_ONSET time --
ie it is the trained output without somatic drive.
*/
if(s == TRAININGCYCLES - 1 && t < STIM_ONSET/DT) {
fwrite(&rU, sizeof(double), 1, postF);
}
#else
/**
for every 10th training cycle write all variables below to
postF in order:
*/
if(s%(TRAININGCYCLES/10)==0) {
fwrite(&rU, sizeof(double), 1, postF);
fwrite(GE+t, sizeof(double), 1, postF);
fwrite(&rV, sizeof(double), 1, postF);
fwrite(&rI, sizeof(double), 1, postF);
fwrite(&u, sizeof(double), 1, postF);
}
if(s == TRAININGCYCLES - 1) {
#ifdef RECORD_PREACT
// for the last cycle also record the activity of the
// presynaptic neurons
fwrite(PRE_ACT + t * NPRE, sizeof(double), 20, preF);
//fwrite(ouP, sizeof(double), 20, preF);
fwrite(presP, sizeof(double), 20, preF);
#else
// and the 1st and 2nd filtered PSP
fwrite(pspSP, sizeof(double), 1, preF);
fwrite(pspTildeP, sizeof(double), 1, preF);
#endif
}
#endif
}
}
fclose(preF);
fclose(postF);
return 0;
}
int main() {
gsl_rng_env_setup();
r = gsl_rng_alloc(gsl_rng_default);
gsl_rng_set(r, RANDOM_SEED);
initDerivedParams();
FILE *outF = fopen("data/recurrent.dat", "w+b");
FILE *wDF = fopen("data/recurrentWD.dat", "w+b");
FILE *wF = fopen("data/recurrentW.dat", "w+b");
gsl_rng_set(r, 0);
gsl_vector *psp = gsl_vector_calloc(N);
gsl_vector *pspS = gsl_vector_calloc(N);
gsl_vector *sue = gsl_vector_calloc(N);
gsl_vector *sui = gsl_vector_calloc(N);
gsl_vector *pspTilde = gsl_vector_calloc(N);
gsl_matrix *w = gsl_matrix_calloc(N, N);
gsl_vector *u = gsl_vector_calloc(N);
gsl_vector *uV = gsl_vector_calloc(N);
gsl_vector *pre = gsl_vector_calloc(N);
gsl_vector *rU = gsl_vector_calloc(N);
gsl_vector *rV = gsl_vector_calloc(N);
double *pspP = gsl_vector_ptr(psp,0);
double *pspSP = gsl_vector_ptr(pspS,0);
double *sueP = gsl_vector_ptr(sue,0);
double *suiP = gsl_vector_ptr(sui,0);
double *pspTildeP = gsl_vector_ptr(pspTilde,0);
double *wP = gsl_matrix_ptr(w,0,0);
double *uP = gsl_vector_ptr(u,0);
double *uVP = gsl_vector_ptr(uV,0);
double *preP = gsl_vector_ptr(pre,0);
double *rUP = gsl_vector_ptr(rU,0);
double *rVP = gsl_vector_ptr(rV,0);
int stateLength = 2 * N ;
double *state[stateLength];
for(int i = 0; i < N; i++) {
*(state + 0*N + i) = preP + i;
*(state + 1*N + i) = rUP + i;
}
gsl_vector_view tmpv; double gE, wij, wd, uI;
for( int s = 0; s < TRAININGCYCLES+5; s++) {
wd = 0;
for( int t = 0; t < TIMEBINS; t++) {
for( int i = 0; i < N; i++) {
updatePre(sueP+i, suiP+i, pspP + i, pspSP + i, pspTildeP + i, *(preP + i) = spiking(*(rUP+i), gsl_rng_uniform_pos(r)));
tmpv = gsl_matrix_row(w, i);
if(s < TRAININGCYCLES - 1) gE = *(GE + t*N + i);
else if(t < 1*TIMEBINS/8 && s == TRAININGCYCLES + 3) gE = *(GE + i);
//&& t < 2*TIMEBINS/3 + TIMEBINS/NGROUPS/5
else gE = 0;
updateMembrane(uP+i, uVP+i, &uI, &tmpv.vector, psp, gE, 0);
*(rUP+i) = phi(*(uP+i)); *(rVP+i) = phi(*(uVP+i));
for(int j = 0; j < N; j++) {
wij = *(wP + i*N + j);
if(i != j) {
updateWeight(wP + i*N + j, *(rUP+i), *(pspTildeP+j), *(rVP+i), *(pspSP+j));
wd += (wij - *(wP + i*N + j)) * (wij - *(wP + i*N + j));
}
if( s == TRAININGCYCLES - 1 && t == TIMEBINS - 1) fwrite(&wij, sizeof(double), 1, wF);
}
}
if(s > TRAININGCYCLES - 4) {
for(int i=0; i<stateLength; i++) fwrite(*(state+i), sizeof(double), 1, outF);
}
}
fwrite(&wd, sizeof(double), 1, wDF);
}
fclose(outF);
fclose(wDF);
fclose(wF);
return 0;
}
