/*
* network.h
* stnetwork
*
* Created by Honi Sanders on 8/30/12.
* Copyright 2012 Brandeis University. All rights reserved.
*
*/
#ifndef NEWNETWORK_H
#ifndef STNETWORK_H
#ifndef NETWORK_H
#define NETWORK_H
#include <cmath>
#include <valarray>
#include <vector>
#include <iostream>
#include <fstream>
#include <sstream>
#include <iomanip>
#include <string>
#include <cstring>
#include <ctime>
#include <map>
// // if not debugging:
// #define NDEBUG
#include <assert.h>
using namespace std;
class Neuron {
private:
char celltype[4]; // exc or inh
double V_s; // Somatic voltage
double V_d; // Dendritic voltage
int spike; // did it spike? 1 or 0
double AMPA; // postsynaptic activation of AMPA receptors
double NMDA; // postsynaptic activation of NMDA receptors
double GABAA; // postsynaptic activation of GABAA receptors
double GABAB; // postsynaptic activation of GABAB receptors
double T,R,D,G,B; // [GABA], activated & desensitized GABABR, [G protein] for GIRK
double g_refr; // refractory conductance
bool inact; // inactivation of Na+ channels
char input; // are input synapses onto dendrite 'd' or soma 's'
char inhibition; // are inhibitory synapses onto dendrite 'd' or soma 's'
double gating[4]; // m, h, n for HH equations and x for synaptic gating
void spiking();
double vdepend(double g, char type); // type is 'N' for NMDA or 'G' for GABAB
double compartmentstep(char compartment, double V, valarray<double> g,
double g_in, double V_coupling, double I_ext, double g_PING);
void EIFstep(double g_AMPA, double g_NMDA, double g_GABAA, double g_GABAB,
double g_in, double I_ext, double g_PING);
void HHstep(double g_AMPA, double g_NMDA, double g_GABAA, double g_GABAB,
double g_in, double I_ext, double g_PING);
public:
Neuron(); // default constructor
Neuron(const char ty[], const char inputset, const char inhibitionset); // constructor
void initialize(const char ty[], const char inputset='s', const char inhibitionset='s');
void fillwstats(double arr[7]); // fills arr w/ V_s, V_d, spike, AMPA, NMDA, GABAA, GABAB
void step(double g_AMPA, double g_NMDA, double g_GABAA, double g_GABAB,
double g_in, double I_ext=0, double g_PING=0);
};
class Network {
private:
size_t N; // # of neurons in network
size_t N_E; // # of excitatory neurons
size_t N_I; // # of inhibitory neurons
size_t N_in; // # of incoming axons
valarray<double> w_AMPA; // weights of local excitatory synapses
valarray<double> w_NMDA; // weights of local excitatory synapses
valarray<double> w_GABAA; // weights of local inhibitory synapses
valarray<double> w_GABAB; // weights of local inhibitory synapses
valarray<double> w_in; // weights of input synapses
valarray<double> w_ffAMPA; // weights of feedforward (buffer-buffer) synapses
valarray<double> w_ffNMDA; // weights of feedforward (buffer-buffer) synapses
public:
Network(); // default constructor
Network(const size_t N_A, const size_t N_input, const double connectivity,
const char inputset, const char inhibitionset); // constructor
Network(const size_t N_A, const valarray<double>& w_input, const double connectivity,
const char inputset, const char inhibitionset); // constructor
void initialize(const size_t N_A, const size_t N_input, const double connectivity,
const char inputset, const char inhibitionset);
void initialize(const size_t N_A, const valarray<double>& w_input, const double connectivity,
const char inputset, const char inhibitionset);
void fillwsize(size_t arr[]);
Neuron* allneurons; // array with all of the neurons in the network
void step(const valarray<double> syn_in, const double I_exts[]);
void step(const valarray<double>& syn_in, const valarray<double>& syn_ff,
const double I_exts[], const double g_PING=0);
};
// time in ms, voltage in mV
#define Cm 10 //Membrane Capacitance in nF/mm2
#define g_L 1 //Leak Conductance in uS/mm2
#define g_refr_max 50 //Refractory Conductance "
#define g_c 1 //changed from 10 //Soma-Dendrite Coupling Conductance "
//#define p 0.5 //directionality of Soma-Dendrite Coupling
#define g_Na 450 // changed from 450 // Sodium Conductance for HH
#define g_K 180 // changed from 180 // Potassium Conductance for HH
#define Delta 3.5 //Constant for EIF
#define kf 12 // synaptic opening variable in kHz (AMPA and GABAA)
#define kr 1 // synaptic closing variable in kHz (AMPA)
#define beta 0.1 // synaptic closing variable in kHz (GABAA)
#define E_L -80 // leak reversal potential in mV
#define E_g_E 0 // excitatory " "
#define E_g_I -70 // inhibitory " "
#define E_Na 55 // Sodium " " (for HH)
#define E_K -90 // Potassium " " " and refractory reversal for EIF
#define Phi_m 2.5 // Temperature Factors "
#define Phi_h 2.5
#define Phi_n 5
#define tau_AMPA 2 // time constant of AMPA channel closing in ms
#define tau_NMDA 100 // " of NMDA " "
#define tau_GABAA 10 // " of GABAA " "
#define tau_GABAB 250 // " of GABAB " "
#define tau_refrI 1 // " of refractory current in I-cells
#define tau_refrE 2 // " " in E-cells
#define thresh -55 // spiking threshold
#define reset -70 // reset potential after spike
#define peak 50 // spike peak potential
#define alpha 0.5 // ratio of unactivated receptors activated by spike
#define alpha_GABAB 0.03 // " " GABAB " " "
#define Mg 0.5 //[Mg2+] in mM
#define V_init -70 // initial voltage
#define s_init 0 // initial synaptic activation
#define dt 0.025 //length of time step in ms
#define w_noise 0.5 // variance of noise over a millisecond (uS/mm2)
#define scale g_L
#define ratio 4 //ratio:1::excitatory:inhibitory neurons
#define eps 0.000000000000001
#define pi 3.141592653589793
double doubrand(double minimum=0, double maximum=1); // random double between minimum and maximum
double normrand(double mean=0, double std=1); // normally distributed random double
double rectify(double x); // =x if x>0, =0 if x<0
valarray<double> connect(valarray<double>& w, double connection);
// changes valarray to a connection matrix with connection probability of "connection"
void outputvalarray(valarray<double> arr, size_t Nrows, size_t Ncols,
string filename = "./results/array.dsv");
void outputvalarray(valarray<int> arr, size_t Nrows, size_t Ncols,
string filename = "./results/array.dsv");
void outputvalarray(valarray<bool> arr, size_t Nrows, size_t Ncols,
string filename = "./results/array.dsv");
valarray<double> loadvalarray(string filename, size_t Nrows, size_t Ncols);
valarray<double> valarraysort(valarray<double> &arr);
#endif
#endif
#endif
