int main(){
Rate rate_ext = 0;
RateFunctor<double> input(Inp);
RateFunctor<double> openlock(Opl);
RateAlgorithm<double> alg_ext(rate_ext);
WilsonCowanParameter par_wc;
par_wc._f_bias = 0;
par_wc._f_noise = 1.0;
par_wc._rate_maximum = 50;
par_wc._time_membrane = 50e-3;
WilsonCowanAlgorithm alg(par_wc);
Network network;
NodeId id_input = network.addNode(input, EXCITATORY_DIRECT);
NodeId id_gate = network.addNode(alg, EXCITATORY_DIRECT);
NodeId id_output = network.addNode(alg, EXCITATORY_DIRECT);
NodeId id_lock = network.addNode(alg, INHIBITORY_DIRECT);
NodeId id_openlock = network.addNode(openlock ,INHIBITORY_DIRECT);
double weight = 1.0;
network.makeFirstInputOfSecond(id_input, id_gate, weight);
network.makeFirstInputOfSecond(id_input, id_lock, weight);
network.makeFirstInputOfSecond(id_gate, id_output, weight);
network.makeFirstInputOfSecond(id_lock, id_gate, -1*weight);
network.makeFirstInputOfSecond(id_openlock, id_lock, -2*weight);
MPILib::CanvasParameter par_canvas;
par_canvas._state_min = -0.020;
par_canvas._state_max = 0.020;
par_canvas._t_min = 0.0;
par_canvas._t_max = 2.0;
par_canvas._f_min = 0.0;
par_canvas._f_max = 50.0;
par_canvas._dense_min = 0.0;
par_canvas._dense_max = 200.0;
MPILib::report::handler::RootReportHandler handler("singlepoptest", true, true, par_canvas );
handler.addNodeToCanvas(id_input);
handler.addNodeToCanvas(id_gate);
handler.addNodeToCanvas(id_output);
handler.addNodeToCanvas(id_lock);
handler.addNodeToCanvas(id_openlock);
const SimulationRunParameter
par_run
(
handler,
10000000,
0.0,
2.0,
1e-3,
1e-3,
"wilsom.log"
);
network.configureSimulation(par_run);
network.evolve();
return 0;
}
int main(){
cout << "Demonstrating Omurtag et al. (2000)" << endl;
Number n_bins = 500;
Potential V_min = 0.0;
NeuronParameter
par_neuron
(
1.0,
0.0,
0.0,
0.0,
50e-3
);
OdeParameter
par_ode
(
n_bins,
V_min,
par_neuron,
InitialDensityParameter(0.0,0.0)
);
double min_bin = 0.01;
LifNeuralDynamics dyn(par_ode,min_bin);
LeakingOdeSystem sys(dyn);
GeomParameter par_geom(sys);
GeomDelayAlg alg(par_geom);
Rate rate_ext = 800.0;
RateAlgorithm<MPILib::DelayedConnection> alg_ext(rate_ext);
Network network;
NodeId id_rate = network.addNode(alg_ext,EXCITATORY_DIRECT);
NodeId id_alg = network.addNode(alg, EXCITATORY_DIRECT);
MPILib::DelayedConnection con(1,0.03,0.0);
network.makeFirstInputOfSecond(id_rate,id_alg,con);
MPILib::report::handler::RootReportHandler handler("singlepoptest", true, true);
handler.addNodeToCanvas(id_alg);
const SimulationRunParameter
par_run
(
handler,
10000000,
0.0,
0.5,
1e-3,
1e-4,
"singlepoptest.log"
);
network.configureSimulation(par_run);
network.evolve();
return 0;
}
int main(){
cout << "Demonstrating Quadratic-Integrate-and-Fire under jump response" << endl;
Number n_bins = 1000;
Potential V_min = -10.0;
NeuronParameter
par_neuron
(
10.0,
-10.0,
-10.0,
0.0,
10e-3
);
OdeParameter
par_ode
(
n_bins,
V_min,
par_neuron,
InitialDensityParameter(0.0,0.0)
);
GeomLib::QifParameter
par_qif
(
0.5, //
0.5 // default gamma sys
);
DiffusionParameter par_diffusion(0.03,0.03);
CurrentCompensationParameter par_current(0.0,0.0);
SpikingQifNeuralDynamics dyn(par_ode,par_qif);
QifOdeSystem sys(dyn);
GeomDelayAlg alg_qif(GeomParameter(sys, par_diffusion, par_current));
// Reproduce mu = 0.6, sigma = 0.7
Rate rate_ext = 6000;
Efficacy h = -8e-3;
RateAlgorithm<MPILib::DelayedConnection> alg_ext(rate_ext);
Network network;
NodeId id_rate = network.addNode(alg_ext, MPILib::INHIBITORY_GAUSSIAN);
NodeId id_alg = network.addNode(alg_qif, MPILib::EXCITATORY_GAUSSIAN);
MPILib::DelayedConnection con(1,h,0.0);
network.makeFirstInputOfSecond(id_rate,id_alg,con);
MPILib::CanvasParameter par_canvas;
par_canvas._state_min = -10.0;
par_canvas._state_max = 10.0;
par_canvas._t_min = 0.0;
par_canvas._t_max = 0.5;
par_canvas._f_min = 0.0;
par_canvas._f_max = 10.0;
par_canvas._dense_min = 0.0;
par_canvas._dense_max = 5.0;
MPILib::report::handler::RootReportHandler handler("twopopcanvas.root", true, true, par_canvas);
handler.addNodeToCanvas(id_alg);
const SimulationRunParameter
par_run
(
handler,
10000000,
0.0,
0.5,
2e-3,
1e-4,
"singlepoptest.log"
);
network.configureSimulation(par_run);
network.evolve();
return 0;
}