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//#include "model_switch.h"
#include <stdio.h>
#include "neuron.h"
Neuron::Neuron() {
// neurons are initialized before the Global objects is; therefore this call to init() is preliminary
// and has to be repeated after the Global object has been loaded
init();
}
Neuron::init() {
// general neuron params
voltage = global.base_voltage;
refractoryTime = 0.0;
// IP
fac_voltage_tau = 1.0;
fax_current = 1.0;
ip_R = 1.0;
ip_C = 1.0;
ip_est_mom1 = global.ip_dst_mom1;
ip_est_mom2 = global.ip_dst_mom2;
ip_dst_mom1 = global.ip_dst_mom1;
ip_dst_mom2 = global.ip_dst_mom2;
// clock
lastEvent = 0.0;
lastSpike = - INFINITY;
// WARN: synapse lists not initialized (!) (well .. they have a constructor)
}
// evolve the neuron's state time [seconds]
// RETURN: the time difference between the this and the last update
double Neuron::evolve(double time) {
// update internal clock
double dt = time - lastEvent;
lastEvent = time;
// voltage decay
voltage -= (voltage - global.voltage_base) * (1.0 - exp( - td / (global.voltage_tau * fac_voltage_tau)));
return dt;
}
// apply an incoming spike of current to this neurons state giving it the current time [s] of the simulation; returns the time of the next spike to occur
double Neuron::processCurrent(double time, double current) {
// process the model until the current time
evolve(time);
// add spike
if (time > refractoryTime)
voltage += fac_current * current;
// return when the neuron should fire the next time (0.0 is an option, too)
return predictSpike(time);
}
// generate a spike wich has been predicted earlier to occur at time [s]
// RETURN when the next spike is expected to occur and the current of the spike
double Neuron::generateSpike(double time, bool &doesOccur) {
// update the model (to the after-firing-state)
evolve(time);
// check if a spike occurs (the spike date might be outdated)
doesOccur = (voltage >= global.fire_threshold) && (time >= refractoryTime);
if (doesOccur) {
// 0. update internal state (instantaneously)
voltage = global.voltage_base;
refractoryTime = time + global.absoluteRefractoryTime;
// 1. do intrinsic plasticity
intrinsicPlasticity(time - lastSpike);
// 2. do stdp related accounting in the incoming synapses
// (check each synapse for event(s))
for (SynapseSrcList::iterator i = sin.begin(); i != sin.end(); i++) {
// do not touch constant synapses (calc'ing the values below is a waste of time)
if (i->constant)
continue;
// check if pre-neuron has fired since we have fired the last time
// this info is stored in the synapse between pre- and this neuron
if ((lastSpike != -INFINITY) && (i->firstSpike != -INFINITY)) {
// depression
i->evolve(i->firstSpike);
i->stdp(lastSpike - i->firstSpike);
// facilitation
i->evolve(time);
i->stdp(time - i->lastSpike);
// reset firstFired to allow the next max calculation
i->firstSpike = -INFINITY;
}
}
// 3. update the last time this neuron has fired
lastSpike = time;
}
return predictSpike(time);
}
void Neuron::predictSpike(double time) {
if (voltage >= global.voltage_threshold) {
return fmax(time, refractoryTime);
}else{
return INFINITY;
}
}
void Neuron::intrinsicPlasticity(double td) {
if (!ip)
return;
// update rate estimation
double edt = exp( td / g.ip_sliding_avg_tau );
double freq = 1.0 / td; // HINT: td=0 is not possible (because of an absolute refractory period)
ip_est_mom1 = edt * ip_est_mom1 + (1 - edt) * freq;
ip_est_mom2 = edt * ip_est_mom2 + (1 - edt) * freq * freq;
// modify internal representation
ip_R += g.ip_lambda_R * (ip_est_mom1 - ip_dst_mom1) * td;
double m2d = g.ip_lambda_C * (ip_est_mom2 - ip_dst_mom2);
ip_C += pow(ip_C, 2) * m2d * td
/ ( 1.0 + ip_C * m2d * td );
// update coefficients
fac_voltage_tau = ip_R * ip_C;
fac_current = ip_R;
}
template<class Action>
bool Neuron::reflect<Action>(Action &a) {
return
a(voltage, "voltage")
&& a(refractoryTime, "refractoryTime")
&& a(ip, "ip")
&& a(ip_est_mom1, "ip_est_mom1")
&& a(ip_est_mom2, "ip_est_mom2")
&& a(ip_dst_mom1, "ip_dst_mom1")
&& a(ip_dst_mom2, "ip_dst_mom2")
&& a(ip_R, "ip_R")
&& a(ip_C, "ip_C")
&& a(fac_voltage_tau, "fac_voltage_tau")
&& a(fac_current, "fac_current")
&& a(lastEvent, "lastEvent")
&& a(lastSpike, "lastSpike");
}
static id_type Neuron::numElements() {
return s.numNeurons;
}
static Neuron * Neuron::singleton(id_type id) {
return &(s.neurons[id]);
}
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