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

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Accession:185355
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. ...
Reference:
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 googlemail.com]; 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 inward rectifier potassium (Kir) channel

COMMENT

Mod File by A. Hanuschkin <AH, 2011> for:
Yim MY, Hanuschkin A, Wolfart J (2015) Hippocampus 25:297-308.
http://onlinelibrary.wiley.com/doi/10.1002/hipo.22373/abstract

Channel description and parameters from:
Stegen M, Kirchheim F, Hanuschkin A, Staszewski O, Veh R, and Wolfart J. Cerebral Cortex, 22:9, 2087-2101, 2012.

Mod File history:
- tau(V), linf(V) fitted to experimental values of human dentate gyrus granual cells
- ModelDB file adapted from 
  Wolf JA, Moyer JT, Lazarewicz MT, Contreras D, Benoit-Marand M, O'Donnell P, Finkel LH (2005) J Neurosci 25:9080-95
  https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=112834&file=/nacb_msp/kir.mod
- file modified to uses nomoclature of 
  Li X, Ascoli GA (2006) J of Comput Neurosci 21(2):191-209 
  Li X, Ascoli GA (2008) Neural Comput 20:1717-31

A. Hanuschkin(c) 2011,2012

ENDCOMMENT


UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
        (S)  = (siemens)
}

PARAMETER {
	v 		(mV)
	gkbar  = 1.44e-05	(S/cm2) 	: to be fitted     	

	: Boltzman steady state curve	
        vhalfl = -98.92  (mV)    		: fitted to patch data, Stegen et al. 2012
        kl = 10.89       (mV)    		: Stegen et al. 2012

	: tau_infty 
        vhalft=67.0828	 (mV)    		: fitted #100 \muM sens curr 350a,  Stegen et al. 2012
        at=0.00610779	 (/ms)   		: Stegen et al. 2012
	bt=0.0817741	 (/ms)	 		: Note: typo in Stegen et al. 2012

	: Temperature dependence
        celsius         (degC)  		: unused if q10 == 1.
        q10 = 1.                              	: temperature scaling
}



NEURON {
	SUFFIX kir 			
	USEION k READ ek WRITE ik	
        RANGE  ik, gkbar, vhalfl, kl, vhalft, at, bt, q10 
        GLOBAL linf,taul
}


STATE {
        l
}

ASSIGNED {
        ik                              (mA/cm2)
        gk                              (S/cm2)
        ek                              (mV)
        linf      
        taul
}


INITIAL {
	rate(v)
	l=linf
}


BREAKPOINT {
	SOLVE states METHOD cnexp	: solve differential equations in states with method 'cnexp'
	gk = gkbar*l			: use state l to calulate gk
        ik = gk * ( v - ek )		: calculate ik 
}



DERIVATIVE states {     
        rate(v)
        l' =  (linf - l)/taul		: differential equation 
}

PROCEDURE rate(v (mV)) { :callable from hoc
        LOCAL qt
	qt=q10^((celsius-33)/10) 	
        linf = 1/(1 + exp((v-vhalfl)/kl))			: l_steadystate
 	taul = 1/(qt *(at*exp(-v/vhalft) + bt*exp(v/vhalft) ))	
}





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