#include "neuron.h"
Neuron::Neuron(){
//initialize excitatory neurons
Tmem = 25, Tgk = 20, Tth = 15, Th = 0,Th0 = 1;
mTmem = 1/Tmem, mTgk = 1/Tgk, mTh = 1/Tth;
mTCa = 1/270;
dcGk = exp(-mTgk), dcCa = exp(-mTCa);
Ge = 0, Gi = 0, Gk = 1, GiGeRatio = 3;
Vr = -70, Ek = -15, Ei = -15, Ee = 70, E = 0, Th = 0;
c = 0.75, CM = c, CS = 0.0, Ca = 0, B = 20;
//Te1 = 0.3, Te2 = 1.5, Ti1 = 0.3, Ti2 = 1.5;
Te1 = 0.2, Te2 = 10, Ti1 = 0.2, Ti2 = 20;
TauRelease = 1; TauReplenish = 8000; SpontRelease = 0.0001;
glutamate = 0;
S = 0, P = 0;
for(int i=0; i< 151; i++){
Ge_table.push_back( (Te1 * Te2 / (Te1 - Te2)) * (exp(-i/Te1) - exp(-i/Te2)) );
Gi_table.push_back( (Ti1 * Ti2 / (Ti1 - Ti2)) * (exp(-i/Ti1) - exp(-i/Ti2)) );
}
for(int i = 1; i <= 100; i++)
firing_Prob.push_back( exp(-1000*(i/100 - 1)*(i/100 - 1)) );
for(int i = 1; i <= 3; i++)
glutamate_Prob.push_back( exp(-1000*(i/4 - 1)*(i/4 - 1)) );
}
int Neuron::Inhibitory(){
//initialize inhibitory neurons
Tmem = 25, Tgk = 10, Tth = 15, Th = 0,Th0 = 1;
mTmem = 1/Tmem, mTgk = 1/Tgk, mTh = 1/Tth;
mTCa = 1/270;
dcGk = exp(-mTgk), dcCa = exp(-mTCa);
Ge = 0, Gi = 0, Gk = 1;
Vr = -70, Ek = -10, Ei = -15, Ee = 70, E = 0, Th = 0;
c = 0.75, CM = c, CS = 0.0, Ca = 0, B = 10;
//Te1 = 0.3, Te2 = 1.5, Ti1 = 0.3, Ti2 = 1.5;
Te1 = 0.2, Te2 = 20, Ti1 = 0.2, Ti2 = 20;
S = 0;
Ge_table.clear();
Gi_table.clear();
for(int i=0; i< 151; i++){
Ge_table.push_back( (Te1 * Te2 / (Te1 - Te2)) * (exp(-i/Te1) - exp(-i/Te2)) );
Gi_table.push_back( (Ti1 * Ti2 / (Ti1 - Ti2)) * (exp(-i/Ti1) - exp(-i/Ti2)) );
}
return 0;
}
int Neuron::computePotential(vector<Synapse> &preSynapses, double SC, int time){
//compute potential of individual neurons
unsigned int i,j;
Ge = 0, Gi = 0, glutamate = 0, gaba = 0;
double mean = 0, stddev = 1.0, value, dist;
int prob_Index;
default_random_engine generator;
normal_distribution<double> distribution(mean,stddev);
for(i = 0; i < preSynapses.size(); i++){
//synaptic delay
dist = sqrt((row - preSynapses[i].row)*(row - preSynapses[i].row) + (clm - preSynapses[i].clm)*(clm - preSynapses[i].clm));
if(dist > 25)
dist = 25;
j = preSynapses[i].psps.size();
while(j >= 150){
preSynapses[i].psps.erase(preSynapses[i].psps.end()-1);
j = j-1;
}
if(preSynapses[i].excitatory){
for(j = round(dist); j < preSynapses[i].psps.size(); j++){
Ge += Ge_table[j-round(dist)]*preSynapses[i].psps[j];
//to keep track of glutamate vesicles released before the neuron fires
if(preSynapses[i].psps[j])
glutamate += 1;//preSynapses[i].psps[j];
}
}
else{
for(j = round(dist); j < preSynapses[i].psps.size(); j++){
Gi += Gi_table[j - round(dist)]*preSynapses[i].psps[j];
if(preSynapses[i].psps[j])
gaba += 1;//preSynapses[i].psps[j];
}
}
}
Gk = exp(-(double)1/Tgk)*Gk + B*S;
Th = exp(-(double)1/Tth)*Th + (1 - exp(-(double)1/Tth))*(Th0+c*E);
E = exp(-((double)1+Gk+Ge+Gi)/(double)5)*E + (((double)1 - exp(-((double)1+Gk+Ge+Gi)/(double)5))*(SC+(double)7*Ge-(double)1*Gi-(double)1*Gk+distribution(generator)))/((double)1+Gk+Ge+Gi);
if(E < -5)
E = -5;
if(E > 10)
E = 10;
if(S == 1){
if(E < Th)
S = 0;
}
if(S == 0){
prob_Index = ceil(99*E/Th);
if(prob_Index < 0)
prob_Index = 0;
if(prob_Index > 99)
prob_Index = 99;
value = drand48();
if(value < firing_Prob[prob_Index] && P < 0){
S = 1;
P = 4;
}
}
P = P-1;
return 0;
}
int Neuron::glutamateinitialize(vector<Synapse> &preSynapses,Neuron **NeuronLayer){
//initialize the number of releasable glutamate vesicles
//the mean and stddev variables determine the initial glutamate distribution parameters
double i, j, k;
double p1, pr, nr;
if(distribution_max == 0)
distribution_max = 1;
for(i = 0; i < preSynapses.size();i++){
if(preSynapses[i].excitatory){
preSynapses[i].NR = preSynapses[i].NRmax*NeuronLayer[preSynapses[i].row][preSynapses[i].clm].glutamate_pr/distribution_max;
if(preSynapses[i].NR >= preSynapses[i].NRmax)
preSynapses[i].NR = preSynapses[i].NRmax;
if(preSynapses[i].NR < 0)
preSynapses[i].NR = 0;
}
else
continue;
}
return 0;
}
int Neuron::glutamaterelease(vector<Synapse> &preSynapses,Neuron **NeuronLayer,int iterations){
int i,j,P_index;
double p1, p2, value, Glu_Rel;
p2 = exp(-(double)1/TauRelease);
//glutamate = 0;
for(i = 0; i < preSynapses.size();i++){
preSynapses[i].psps.insert(preSynapses[i].psps.begin(),0);
if(preSynapses[i].excitatory){
p1 = exp(-(double)1/TauReplenish);//(preSynapses[i].tau);
preSynapses[i].NR = preSynapses[i].NR*p1 + preSynapses[i].NRmax*(1 - p1);
//glutamate += preSynapses[i].NR/preSynapses[i].NRmax ;
preSynapses[i].tau += 1;
if(preSynapses[i].NR >= preSynapses[i].NRmax)
preSynapses[i].NR = preSynapses[i].NRmax;
if(preSynapses[i].NR < 0)
preSynapses[i].NR = 0;
//activity dependent glutamate release
if(NeuronLayer[preSynapses[i].row][preSynapses[i].clm].S){
value = drand48();
if( value <= 1 ){
//glutamate release
Glu_Rel = round(preSynapses[i].NR*pow(p2,4 - NeuronLayer[preSynapses[i].row][preSynapses[i].clm].P));
preSynapses[i].NR = preSynapses[i].NR - Glu_Rel;
//glutamate = glutamate + Glu_Rel;
for(j = 0; j < Glu_Rel; j++){
value = preSynapses[i].weight;
preSynapses[i].psps[0] = value;
if(j != Glu_Rel)
preSynapses[i].psps.insert(preSynapses[i].psps.begin(),0);
}
preSynapses[i].tau = 0;
if(preSynapses[i].NR < 0)
preSynapses[i].NR = 0;
}
}
//spontaneous release
else{
value = drand48();
if(value < SpontRelease){
value = drand48();
if( value <= 1 ){
Glu_Rel = round(preSynapses[i].NR*p2);
preSynapses[i].NR = preSynapses[i].NR - Glu_Rel;
//glutamate = glutamate + Glu_Rel;
for(j = 0; j < Glu_Rel; j++){
value = preSynapses[i].weight;
preSynapses[i].psps[0] = value;
if(j != Glu_Rel)
preSynapses[i].psps.insert(preSynapses[i].psps.begin(),0);
}
preSynapses[i].tau = 0;
if(preSynapses[i].NR < 0)
preSynapses[i].NR = 0;
}
}
}
}
else{
if(NeuronLayer[preSynapses[i].row][preSynapses[i].clm].S){
value = drand48();
if( value < 1 ){
//GABA release
//cout << "psp add" << " " << value << endl;
value = preSynapses[i].weight;//drand48();
//preSynapses[i].psps.insert(preSynapses[i].psps.begin(),value);
preSynapses[i].psps[0] = value;
}
}
else{
value = drand48();
if( value < SpontRelease){
//GABA spontaneous release
//cout << "psp add" << " " << value << endl;
//value = preSynapses[i].weight;//drand48();
//preSynapses[i].psps.insert(preSynapses[i].psps.begin(),value);
//preSynapses[i].psps[0] = value;
}
}
}
}
//exit(0);
return 0;
}