nemo::Network*
construct(unsigned ncount, unsigned scount, unsigned dmax, bool stdp)
{
rng_t rng;
/* Neuron parameters and weights are partially randomised */
urng_t randomParameter(rng, boost::uniform_real<double>(0, 1));
uirng_t randomTarget(rng, boost::uniform_int<>(0, ncount-1));
uirng_t randomDelay(rng, boost::uniform_int<>(1, dmax));
nemo::Network* net = new nemo::Network();
for(unsigned nidx=0; nidx < ncount; ++nidx) {
if(nidx < (ncount * 4) / 5) { // excitatory
addExcitatoryNeuron(net, nidx, randomParameter);
for(unsigned s = 0; s < scount; ++s) {
net->addSynapse(nidx, randomTarget(), randomDelay(), 0.5f * float(randomParameter()), stdp);
}
} else { // inhibitory
addInhibitoryNeuron(net, nidx, randomParameter);
for(unsigned s = 0; s < scount; ++s) {
net->addSynapse(nidx, randomTarget(), 1U, float(-randomParameter()), 0);
}
}
}
return net;
}
nemo::Network* constructWattsStrogatz(unsigned k, unsigned p, unsigned ncount, unsigned dmax, bool stdp) {
rng_t rng;
/* Neuron parameters and weights are partially randomised */
urng_t randomParameter(rng, boost::uniform_real<double>(0, 1));
uirng_t randomDelay(rng, boost::uniform_int<>(1, dmax));
std::vector<std::vector<unsigned> > matrix(ncount, std::vector<unsigned>(ncount));
networkWattsStrogatz(matrix, ncount, k, p);
nemo::Network* net = new nemo::Network();
for ( unsigned nidx = 0; nidx < ncount; ++nidx ) {
if ( nidx < (ncount * 4) / 5 ) { // excitatory
addExcitatoryNeuron(net, nidx, randomParameter);
for ( unsigned j = 0; j < ncount; ++j ) {
if ( matrix[nidx][j] == 1 )
net->addSynapse(nidx, j, randomDelay(), 0.5f * float(randomParameter()), stdp);
}
}
else { // inhibitory
addInhibitoryNeuron(net, nidx, randomParameter);
for ( unsigned j = 0; j < ncount; ++j ) {
if ( matrix[nidx][j] == 1 )
net->addSynapse(nidx, j, 1U, float(-randomParameter()), 0);
}
}
}
return net;
}
/* Used to demonstrate the optimal network for MPI simulations */
nemo::Network* constructSemiRandom(unsigned ncount, unsigned scount, unsigned dmax, bool stdp, unsigned workers, float ratio) {
rng_t rng;
/* Neuron parameters and weights are partially randomised */
urng_t randomParameter(rng, boost::uniform_real<double>(0, 1));
uirng_t randomDelay(rng, boost::uniform_int<>(1, dmax));
nemo::Network* net = new nemo::Network();
/* Firstly, add ncount neurons to the network.
* Properties: 80% excitatory, 20% inhibitory.
*/
for ( unsigned nidx = 0; nidx < ncount; ++nidx ) {
if ( nidx < (ncount * 4) / 5 ) { // excitatory
addExcitatoryNeuron(net, nidx, randomParameter);
}
else { // inhibitory
addInhibitoryNeuron(net, nidx, randomParameter);
}
}
/* Initialize partition counters */
float avgNeurons = ceil(((float) ncount) / ((float) workers));
std::vector<unsigned> counters(workers, avgNeurons);
counters.back() = ncount - (workers - 1) * avgNeurons;
std::vector<std::pair<nidx_t, nidx_t> > ranges;
/* Make index ranges for each partition */
for ( unsigned r = 0; r < workers; r++ ) {
unsigned start = r * avgNeurons;
std::pair<unsigned, unsigned> range(start, start + counters[r] - 1);
ranges.push_back(range);
}
for ( unsigned i = 0; i < workers; i++ ) {
uirng_t randomLocalNeuron(rng, boost::uniform_int<>(ranges[i].first, ranges[i].second));
unsigned partitionSynapses = counters[i] * scount;
unsigned globalSynapses = ratio * partitionSynapses;
unsigned localSynapses = partitionSynapses - globalSynapses;
// std::cout << "partitionSynapses: " << partitionSynapses << std::endl;
// std::cout << "localSynapses: " << localSynapses << std::endl;
// std::cout << "globalSynapses: " << globalSynapses << std::endl;
// std::cout << std::endl;
for ( unsigned j = 0; j < localSynapses; j++ ) {
unsigned source;
unsigned target;
while (true) {
source = randomLocalNeuron();
target = randomLocalNeuron();
if ( source != target )
break;
}
if ( randomLocalNeuron() < (ncount * 4) / 5 )
net->addSynapse(source, target, randomDelay(), 0.5f * float(randomParameter()), stdp);
else
net->addSynapse(source, target, 1U, float(-randomParameter()), stdp);
}
for ( unsigned j = 0; j < globalSynapses; j++ ) {
uirng_t randomWorker(rng, boost::uniform_int<>(0, workers - 1));
unsigned randomNeighbour;
while (true) {
randomNeighbour = randomWorker();
if ( randomNeighbour != i )
break;
}
uirng_t randomGlobalNeuron(rng,
boost::uniform_int<>(ranges[randomNeighbour].first, ranges[randomNeighbour].second));
if ( randomLocalNeuron() < (ncount * 4) / 5 )
net->addSynapse(randomLocalNeuron(), randomGlobalNeuron(), randomDelay(), 0.5f * float(randomParameter()), stdp);
else
net->addSynapse(randomLocalNeuron(), randomGlobalNeuron(), 1U, float(-randomParameter()), stdp);
}
}
// std::cout << "net->neuronCount: " << net->neuronCount() << std::endl;
// std::cout << "net->synapseCount: " << net->synapseCount() << std::endl;
return net;
}
/* Used for debugging */
nemo::Network* simpleNet(unsigned ncount, unsigned dmax, bool stdp) {
rng_t rng;
/* Neuron parameters and weights are partially randomised */
urng_t randomParameter(rng, boost::uniform_real<double>(0, 1));
uirng_t randomDelay(rng, boost::uniform_int<>(1, dmax));
nemo::Network* net = new nemo::Network();
for ( unsigned nidx = 0; nidx < ncount; ++nidx ) {
if ( nidx < (ncount * 4) / 5 ) { // excitatory
nemo::random::addExcitatoryNeuron(net, nidx, randomParameter);
if ( nidx == 0 ) {
net->addSynapse(nidx, 1, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 6, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 11, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(2, nidx, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(3, nidx, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(4, nidx, randomDelay(), 0.5f * float(randomParameter()), stdp);
}
if ( nidx == 8 ) {
net->addSynapse(nidx, 2, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 7, randomDelay(), 0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 14, randomDelay(), 0.5f * float(randomParameter()), stdp);
}
}
else { // inhibitory
nemo::random::addInhibitoryNeuron(net, nidx, randomParameter);
if ( nidx == 13 ) {
net->addSynapse(nidx, 3, randomDelay(), -0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 8, randomDelay(), -0.5f * float(randomParameter()), stdp);
net->addSynapse(nidx, 14, randomDelay(), -0.5f * float(randomParameter()), stdp);
}
}
}
return net;
}
