I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)

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Accession:152539
Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)
Reference:
1 . Cavallari S, Panzeri S, Mazzoni A (2014) Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks. Front Neural Circuits 8:12 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns;
Implementer(s): Cavallari, Stefano [stefano.cavallari at iit.it];
Search NeuronDB for information about:  GabaA; AMPA;
#include <math.h>
#include "ran1.h"
#include "gasdev.h"


float gasdev(long *idum)
{
	float ran1(long *idum); 
	static int iset=0;
	static float gset;
	float fac,rsq,v1,v2;

	if  (iset == 0) {
		do {
			v1=2.0*ran1(idum)-1.0;
			v2=2.0*ran1(idum)-1.0;
			rsq=v1*v1+v2*v2;
		} while (rsq >= 1.0 || rsq == 0.0);
		fac=sqrt(-2.0*log(rsq)/rsq);
		gset=v1*fac;
		iset=1;
		return v2*fac;
	} else {
		iset=0;
		return gset;
	}
}