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 121:1125-1142 [PubMed]
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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] ;
NEURON {
	THREADSAFE
	POINT_PROCESS variator
	RANGE t0, t1, a0, a1, a
	POINTER var
}
PARAMETER {
	t0 ()
	t1 ()
	a0 ()
	a1 ()
}
ASSIGNED {
	a ()
	var ()
}
BREAKPOINT {
	if( t < t0 ){
		a = a0
	}else{
		if( t > t1 ){
			a = a1
		}else{
			a = a0 + (a1-a0)*(t-t0)/(t1-t0)
		}
	}
	var = a
}