Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)

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The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
1 . Yim MY, Hanuschkin A, Wolfart J (2015) Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability. Hippocampus 25:297-308 [PubMed]
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: Dentate gyrus;
Cell Type(s): Dentate gyrus granule GLU cell; Dentate gyrus mossy cell; Dentate gyrus basket cell; Dentate gyrus hilar cell; Dentate gyrus MOPP cell;
Channel(s): I Chloride; I K,leak; I Cl, leak; Kir; Kir2 leak;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s): IRK; Kir2.1 KCNJ2; Kir2.2 KCNJ12; Kir2.3 KCNJ4; Kir2.4 KCNJ14;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Intrinsic plasticity; Pathophysiology; Epilepsy; Homeostasis; Pattern Separation;
Implementer(s): Yim, Man Yi [manyi.yim at]; Hanuschkin, Alexander ; Wolfart, Jakob ;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; I Chloride; I K,leak; I Cl, leak; Kir; Kir2 leak; Gaba; Glutamate;
TITLE CaT.mod T-type Cav channel

Mod File by A. Hanuschkin <AH, 2011> for:
Yim MY, Hanuschkin A, Wolfart J (2015) Hippocampus 25:297-308.

Mod File history:
- fitted H-H parameter N-Ca from Jaffe DB, Ross WN, Lisman JE,  Lasser-Ross N, Miyakawa H, Johnston D (1994) Journal of Neurophysiology, Vol. 71 no. 3, 1065-1077
- Ca ion & L/T/N-Ca channels model of  Aradi I, Holmes WR (1999) J Comput Neurosci 6:215-35
- Note that eCa is calculated during simulation by ccanl.mod. ecat, ecal values set in Santhakumar are not used in our model scripts.

        (mA) =		(milliamp)
        (mV) =		(millivolt)
        (uF) = 		(microfarad)
	(molar) = 	(1/liter)
	(nA) = 		(nanoamp)
	(mM) = 		(millimolar)
	(um) = 		(micron)
	FARADAY = 96520 (coul)
	R = 8.3134	(joule/degC)
RANGE gtca
RANGE gcatbar
RANGE ainf, atau, binf, btau, itca

INDEPENDENT {t FROM 0 TO 100 WITH 100 (ms)}
        v (mV) 
        celsius = 6.3 (degC)
        dt (ms) 
	gcatbar (mho/cm2)
	a b
        gtca (mho/cm2)
	itca (mA/cm2)
	etca (mV)

	ainf binf
	atau (ms) btau (ms) 
	aexp bexp      

	SOLVE states
        gtca = gcatbar*a*a*b
	itca = gtca*(v-etca)
	a = ainf
	b = binf

PROCEDURE states() {	:Computes state variables a and b 
        trates(v)	:      at the current v and dt.
	a = a + aexp*(ainf-a) : i.e. a_{t+1} = a_t*exp(-dt/atau)+ainf*(1-exp(-dt/atau)); da/dt = 1/atau*(ainf-a)
	b = b + bexp*(binf-b)
        return 0;

PROCEDURE rates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
        LOCAL  alpha, beta, sum
        q10 = 3^((celsius - 6.3)/10) : q10 = 1 for 6.3 celsius
                :"a" TCa activation system
        alpha = -0.2*vtrap(v-19.26,-10)		
	beta = 0.009*exp(-v/22.03)		
	sum = alpha+beta        
	atau = 1/sum      ainf = alpha/sum
                :"b" TCa inactivation system
	alpha = 1e-6*exp(-v/16.26)		
	beta = 1/(exp((29.79-v)/10)+1)		
	sum = alpha+beta        
	btau = 1/sum      binf = alpha/sum
PROCEDURE trates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
	LOCAL tinc
        TABLE  ainf, aexp, binf, bexp, atau, btau
	DEPEND dt, celsius FROM -100 TO 100 WITH 200
	rates(v)	: not consistently executed from here if usetable_hh == 1
		: so don't expect the tau values to be tracking along with
		: the inf values in hoc

	       tinc = -dt * q10
	aexp = 1 - exp(tinc/atau)
	bexp = 1 - exp(tinc/btau)
FUNCTION vtrap(x,y) {  :Traps for 0 in denominator of rate eqns.
        if (fabs(x/y) < 1e-6) {
                vtrap = y*(1 - x/y/2)
                vtrap = x/(exp(x/y) - 1)

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