gaussianHodgkinHuxley

DGL of gaussianHodgkinHuxley

double alpham = (25.0 - x[0] ) / ( 10.0 * ( exp (( 25 - x[0] )/10.0)-1 ));

double alphah = 0.07 * exp ( -x[0] / 20.0 );

double alphan = (10.0 - x[0]) / ( 100.0 * ( exp (( 10.0 - x[0] ) / 10.0 ) - 1 ) );

double betam = 4.0 * exp (-x[0]/18.0);

double betah =1.0 / ( exp (( 30.0 - x[0]) / 10.0) + 1);

double betan = 0.125 * exp ( - x[0] / 80.0);

dxdt[0] = ( gna() /cm() ) *x[1]*x[1]*x[1]*x[2]* ( ena()-x[0] ) + ( gk() /cm() ) *x[3]*x[3]*x[3]*x[3]* ( ek()-x[0] ) + ( gpas() /cm() ) * ( vpas()-x[0] ) + ( couplingSum() /cm() ) + ( I() /cm() );

dxdt[1] = alpham  * ( 1-x[1] )-betam *x[1];

dxdt[2] = alphah  * ( 1-x[2] )-betah *x[2];

dxdt[3] = alphan  * ( 1-x[3] )-betan *x[3];

s[0] = sigmaNoise();

Parameter of gaussianHodgkinHuxley

  • gaussianHodgkinHuxley_cm = 1.0000000000000000;
  • gaussianHodgkinHuxley_gna = 120.0000000000000000;
  • gaussianHodgkinHuxley_gk = 36.0000000000000000;
  • gaussianHodgkinHuxley_gpas = 0.3000000000000000;
  • gaussianHodgkinHuxley_ena = 110.0000000000000000;
  • gaussianHodgkinHuxley_ek = -12.0000000000000000;
  • gaussianHodgkinHuxley_vpas = 10.6129999999999995;
  • gaussianHodgkinHuxley_I = 100.0000000000000000;
  • gaussianHodgkinHuxley_sigmaNoise = 0.0000000000000000;

Example (python-conedy)

import conedy as ns

net = ns.network()

net.addNode(ns.gaussianHodgkinHuxley())

Example (conedy)

network net;

net.addNode(gaussianHodgkinHuxley());

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