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]
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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 SK channel (small conductance, calcium-activated potassium channel)
COMMENT

Original Mod File:
Original name 'gskch.mod', gsk granule
Santhakumar et al. (2005)
https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=51781&file=/dentategyrusnet2005/gskch.mod

Current version 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

Changes in current versus original version:
Correction: use of correct dynamics (see rate() lines: 95-101)

Further Mod File history:
- gsk granule
- modified from Aradi I, Holmes WR (1999) J Comput Neurosci 6:215-35

ENDCOMMENT

UNITS {
        (molar) = (1/liter)
        (mM)    = (millimolar)
	(mA)	= (milliamp)
	(mV)	= (millivolt)
}

NEURON {
	SUFFIX sk
	USEION k READ ek WRITE ik 
	USEION nca READ ncai VALENCE 2
	USEION lca READ lcai VALENCE 2
	USEION tca READ tcai VALENCE 2
	RANGE gsk, gskbar, qinf, qtau, ik
}

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

PARAMETER {
	celsius=6.3 (degC)
	v		(mV)
	dt		(ms)
	gskbar  (mho/cm2)
	ek	(mV)
	cai (mM)
	ncai (mM)
	lcai (mM)
	tcai (mM)
}

STATE { q }

ASSIGNED {
	ik (mA/cm2) gsk (mho/cm2) qinf qtau (ms) qexp
}


BREAKPOINT {          :Computes i=g*q^2*(v-ek)
	SOLVE state
        gsk = gskbar * q*q
	ik = gsk * (v-ek)
}

UNITSOFF

INITIAL {
	cai = ncai + lcai + tcai	
	rate(cai)
	q=qinf
	VERBATIM
	ncai = _ion_ncai;
	lcai = _ion_lcai;
	tcai = _ion_tcai;
	ENDVERBATIM
}


PROCEDURE state() {  :Computes state variable q at current v and dt.
	cai = ncai + lcai + tcai
	rate(cai)
	q = q + (qinf-q) * qexp
	VERBATIM
	return 0;
	ENDVERBATIM
}

LOCAL q10
PROCEDURE rate(cai) {  :Computes rate and other constants at current v.
	LOCAL alpha, beta, tinc
	q10 = 3^((celsius - 6.3)/10) : q10=1 for 6.3 celcius
		:"q" activation system

        : this is the correct dynamics <AH>
	alpha = 0.00246/exp((12*log10(cai)+28.48)/-4.5)
	beta = 0.006/exp((12*log10(cai)+60.4)/35)
	qtau = 1 / (alpha + beta)
	qinf = alpha * qtau
	tinc = -dt*q10
	qexp = 1 - exp(tinc/qtau)*q10
}

UNITSON