PIR gamma oscillations in network of resonators (Tikidji-Hamburyan et al. 2015)

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Accession:183718
" ... The coupled oscillator model implemented with Wang–Buzsaki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo and in vitro do not fire sparsely during gamma,but rather on average every other cycle. We substituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clamp experiments that show autapse-induced firing in entorhinal cortical interneurons support the idea that PIR can serve as a network gamma mechanism. ..."
Reference:
1 . Tikidji-Hamburyan RA, Martínez JJ, White JA, Canavier CC (2015) Resonant Interneurons Can Increase Robustness of Gamma Oscillations. J Neurosci 35:15682-95 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Entorhinal cortex;
Cell Type(s): Wide dynamic range neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Gamma oscillations;
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at gmail.com] ;
TITLE Icellchann
: Cell model from Wang and Buzsaki, J Neurosci 1996
: Programmed by Adriano Tort, CBD, BU, 2008
: With modification by Ruben Tikidji-Hamburyan rth@nisms.krinc.ru
UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
}
 
NEURON {
	SUFFIX BSKCch
	USEION na READ ena WRITE ina
	USEION k  READ ek  WRITE ik
	RANGE  gnabar, gkbar
	: , v0

}
 
PARAMETER {
	v				(mV)
	celsius			(degC)
	gna= 0.035		(mho/cm2)
	ena				(mV)
	gk= 0.009		(mho/cm2)
	ek				(mV)
:		gl= 0.0001		(mho/cm2)
:       el= -65			(mV)
:	v0=-65			(mV)
		}
 
STATE {
	m
	n
	h 
}
 
ASSIGNED {
	ina		(mA/cm2) 
 	minf
	mtau   	(ms)
	hinf
	htau    (ms)
	
	ik		(mA/cm2)
	ninf
	ntau	(ms)	
}
 
BREAKPOINT {
        SOLVE states METHOD cnexp
        ina=gna*minf*minf*minf*h*(v-ena)
		ik=gk*n*n*n*n*(v-ek)
}
 
DERIVATIVE states { 
       rates(v)
	   h'= 5*(hinf- h)/ htau
	   n'= 5*(ninf- n)/ ntau 
}


INITIAL { 
	rates(v)
	:DB>>
	:printf("BSKCch init v=%g\n",v)
	:<<DB
	n=0
	h=1
	}

PROCEDURE rates(v (mV)) {
LOCAL alpha, beta
UNITSOFF 

	alpha = -0.1*(v+35)/(exp(-(v+35)/10)-1)
	beta = 4*exp(-(v+60)/18)
	minf=alpha/(alpha+beta)
	
	alpha = 0.07*exp(-(v+58)/20)
	beta = 1/(exp(-0.1*(v+28))+1)
	htau=1/(alpha+beta)
	hinf=alpha*htau
	
	alpha = 0.01*(v+34)/(1-exp(-0.1*(v+34)))
	beta = 0.125*exp(-(v+44)/80)
	ntau=1/(alpha+beta)
	ninf=alpha*ntau	
	
UNITSON
}

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