//! trigger all UserErrors
// TODO: add more error checking
TEST(Interface, connectDeath) {
::testing::FLAGS_gtest_death_test_style = "threadsafe";
CARLsim* sim = new CARLsim("Interface.connectDeath",CPU_MODE,SILENT,1,42);
int g1=sim->createSpikeGeneratorGroup("excit", 10, EXCITATORY_NEURON);
int g2=sim->createGroup("excit", Grid3D(2,3,4), EXCITATORY_NEURON);
sim->setNeuronParameters(g2, 0.02f, 0.2f,-65.0f,8.0f);
// regular connect call
EXPECT_DEATH(sim->connect(g1,g1,"random",RangeWeight(0.1f),0.1f),""); // g-post cannot be PoissonGroup
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(-0.01f),0.1f),""); // weight cannot be negative
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.01f,0.1f,0.1f),0.1f),""); // wt.min>0
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.0f,0.01f,0.1f),0.1f),""); // SYN_FIXED wt.init!=wt.max
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.0f,0.01f,0.1f),-0.1f),""); // prob<0
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.0f,0.01f,0.1f),2.3f),""); // prob>1
EXPECT_DEATH(sim->connect(g1,g2,"one-to-one",RangeWeight(0.1f),0.1f,RangeDelay(1),RadiusRF(3,0,0)),""); // rad>0
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.1f),0.1f,RangeDelay(1),RadiusRF(-1),SYN_FIXED,-1.0f,0.0f),""); // mulSynFast<0
EXPECT_DEATH(sim->connect(g1,g2,"random",RangeWeight(0.1f),0.1f,RangeDelay(1),RadiusRF(-1),SYN_FIXED,0.0f,-1.0f),""); // mulSynSlow<0
// custom ConnectionGenerator
ConnectionGenerator* CGNULL = NULL;
DummyCG* CG = new DummyCG;
EXPECT_DEATH({sim->connect(g1,g2,CGNULL);},""); // CG=NULL
void MCNeuronSim::setupGroups(){
//int poissonGroup[scenCount];
/*
* (A) first setup groups for somatic scenarios! - connect compartments for MC!
*/
float G_up, G_dn;
for(int k = 0; k < scenCount; k++)
{
//std::string poissonGroupName = "poisson_" + patch::to_string(k);
//poissonGroup[k] = network->createSpikeGeneratorGroup(poissonGroupName, popSize_neuronCount, EXCITATORY_NEURON);
for(int c=0; c<compCount; c++)
{
std::string excGroupName = "exc_" + patch::to_string(k) + patch::to_string(c);
excGroup[k][c] = network->createGroup(excGroupName, popSize_neuronCount, EXCITATORY_NEURON);
network->setNeuronParameters(excGroup[k][c], 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f);
// network->setExternalCurrent(excGroup[k][c], 0);
if(compCount>1){
G_up = 0;
G_dn = 0;
network->setCompartmentParameters(excGroup[k][c], G_up, G_dn);
}
excMonitor[k][c] = network->setSpikeMonitor(excGroup[k][c], "/dev/null");
if(c>0){//connect compartments based on layout
if(compCount>2 && c==1 && connLayout[c]==connLayout[c+1]){ //meaning 2 dendrites (dend 1 and dend2 ) connecting to the same point
network->connectCompartments(excGroup[k][c], excGroup[k][connLayout[c]]);
}else{
network->connectCompartments(excGroup[k][connLayout[c]], excGroup[k][c]);
}
}
}
//????????????
//network->connect(poissonGroup[k], excGroup[k][0],"random", RangeWeight(0.0f), 0.5f, RangeDelay(1));
//????????????
}
/*
* (B) secondly, setup groups for MC scenarios! - connect compartments depending on scenarios as below
* - 1. rheo group : decoupled
* - 2. IR: decoupled
* - 3. spike prop: Coupled
* - 4. syn ampl: Coupled
*/
for(int k = scenCount; k < scenCount+mcScenCount; k++)
{
for(int c=0; c<compCount; c++)
{
std::string excGroupName = "mcc_exc_" + patch::to_string(k) + patch::to_string(c);
excGroup[k][c] = network->createGroup(excGroupName, popSize_neuronCount, EXCITATORY_NEURON);
network->setNeuronParameters(excGroup[k][c], 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f, 0.1f);
if(k>=scenCount+2){// coupled scenarios for mcc
G_up = 0;
G_dn = 0;
network->setCompartmentParameters(excGroup[k][c], G_up, G_dn);
}
excMonitor[k][c] = network->setSpikeMonitor(excGroup[k][c], "/dev/null");
if(k>=scenCount+2){
if(c>0){//connect compartments based on layout
if(compCount>2 && c==1 && connLayout[c]==connLayout[c+1]){ //meaning 2 dendrites (dend 1 and dend2 ) connecting to the same point
network->connectCompartments(excGroup[k][c], excGroup[k][connLayout[c]]);
}else{
network->connectCompartments(excGroup[k][connLayout[c]], excGroup[k][c]);
}
}
}
}
}
//single neuron single compartment for EPSP stimulation
for(int pssg=0; pssg<nPreSynSpikerGroups;pssg++){
excGroup[scenCount+mcScenCount][pssg] = network->createGroup("spiker_"+patch::to_string(pssg), 1, EXCITATORY_NEURON);
network->setNeuronParameters(excGroup[scenCount+mcScenCount][pssg], 121.0f, 0.5343182f,-61.20764f,-40.00114f,
0.0336908f, 6.5490165f, 35.965454f,-41.31163f, 7.0f);
}
float connProb=0.3f;
if(writeAllVs){
connProb=1.0f;
}
const int epspGrpRowStartIdx = scenCount + 2+ (compCount-1);
float _weight = 1.0f;
for(int kc=0; kc<(compCount-1); kc++){
/*
* new addition for CA1 pyramidal dendrites analysis
*/
if(compCount==4 && kc==0)
_weight = 1.0f;
if(compCount==4 && kc==1)
_weight = 1.0f/3.0f;//1.0f/6.0f;
if(compCount==4 && kc==2)
_weight = 2.5f*(1.0f/3.0f); //3.0f*(1.0f/3.0f);
/*if(compCount==4 && kc==2)
_weight = 1.0f;
*/
/*
* new addition ends
*/
for(int pssg=0; pssg<nPreSynSpikerGroups;pssg++){
network->connect(excGroup[scenCount+mcScenCount][pssg], excGroup[epspGrpRowStartIdx + kc][kc+1],
"random", RangeWeight(_weight), connProb,
RangeDelay(1), RadiusRF(-1), SYN_FIXED, 1.0f, 0.0f);
}
}
//multi neuron single compartment spikers for new spike prop implementation via synaptic stimulation
for(int pssg=nPreSynSpikerGroups; pssg<nPreSynSpikerGroups*2;pssg++){
//excGroup[scenCount+mcScenCount][pssg] = network->createGroup("spiker_"+ patch::to_string(pssg), 16, EXCITATORY_NEURON);
excGroup[scenCount+mcScenCount][pssg] = network->createGroup("spiker_"+ patch::to_string(pssg), 85, EXCITATORY_NEURON);
network->setNeuronParameters(excGroup[scenCount+mcScenCount][pssg], 121.0f, 0.5343182f,-61.20764f,-40.00114f,
0.0336908f, 6.5490165f, 35.965454f,-41.31163f, 7.0f);
}
const int propRateGrpRowStartIdx = scenCount + 2;
//_weight =33.0f;
_weight =2.0f;
for(int kc=0; kc<(compCount-1); kc++){
//if(kc+1>1)
// _weight = 10.0f;
if(compCount==4 && kc==0)
_weight = 1.0f;
if(compCount==4 && kc==1)
_weight = 1.0f; //_weight = 0; //0, cuz this is not a check for prop in SR. By setting this weight 0, SR only has the I(=700?, {see determine prop rate function})- this is a baseline to test conditional spike propagation
if(compCount==4 && kc==2)
_weight = 2.0f;
for(int pssg=nPreSynSpikerGroups; pssg<nPreSynSpikerGroups*2;pssg++){
network->connect(excGroup[scenCount+mcScenCount][pssg], excGroup[propRateGrpRowStartIdx + kc][kc+1],
"random", RangeWeight(_weight), connProb,
RangeDelay(1,10), RadiusRF(-1), SYN_FIXED, 1.0f, 0.0f );
}
}
if(customI==1){
std::cout<<"pre - in";
custom_grp_in=network->createSpikeGeneratorGroup("custom_input_0", 1, EXCITATORY_NEURON);
std::vector<int> spkTimes = {150, 250, 400, 600};
SpikeGeneratorFromVector SGV(spkTimes);
network->setSpikeGenerator(custom_grp_in, &SGV);
int compartment_to_stimulate = 1;
//create poisson group!
network->connect(custom_grp_in, excGroup[0][compartment_to_stimulate], "full", RangeWeight(10), 1, RangeDelay(1),RadiusRF(-1),SYN_FIXED);
std::cout<<"custom_input EXIT "; //
}
//
/*
* (C) Now, setup network!
*/
network->setConductances(true);
network->setIntegrationMethod(RUNGE_KUTTA4, time_step);
network->setupNetwork();
}
