Phase response theory in sparsely + strongly connected inhibitory NNs (Tikidji-Hamburyan et al 2019)

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Accession:239177

Reference:
1 . Tikidji-Hamburyan RA, Leonik CA, Canavier CC (2019) Phase Response Theory Explains Cluster Formation in Sparsely but Strongly Connected Inhibitory Neural Networks and Effects of Jitter due to Sparse Connectivity. J Neurophysiol [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract single compartment conductance based cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] ;
:  Vector stream of events

NEURON {
	ARTIFICIAL_CELL VecStim
}

ASSIGNED {
	index
	etime (ms)
	space
}

INITIAL {
	index = 0
	element()
	if (index > 0) {
		net_send(etime - t, 1)
	}
}

NET_RECEIVE (w) {
	if (flag == 1) {
		net_event(t)
		element()
		if (index > 0) {
			net_send(etime - t, 1)
		}
	}
}

VERBATIM
extern double* vector_vec();
extern int vector_capacity();
extern void* vector_arg();
ENDVERBATIM

PROCEDURE element() {
VERBATIM	
  { void* vv; int i, size; double* px;
	i = (int)index;
	if (i >= 0) {
		vv = *((void**)(&space));
		if (vv) {
			size = vector_capacity(vv);
			px = vector_vec(vv);
			if (i < size) {
				etime = px[i];
				index += 1.;
			}else{
				index = -1.;
			}
		}else{
			index = -1.;
		}
	}
  }
ENDVERBATIM
}

PROCEDURE play() {
VERBATIM
	void** vv;
	vv = (void**)(&space);
	*vv = (void*)0;
	if (ifarg(1)) {
		*vv = vector_arg(1);
	}
ENDVERBATIM
}
        


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