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;
COMMENT

Original Mod Files:
Santhakumar V, Aradi I, Soltesz I (2005) J Neurophysiol 93:437-53 
https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=51781&file=/dentategyrusnet2005/ccanl.mod
Morgan RJ, Soltesz I (2008) Proc Natl Acad Sci U S A 105:6179-84
Morgan RJ, Santhakumar V, Soltesz I (2007) Prog Brain Res 163:639-58
Dyhrfjeld-Johnsen J, Santhakumar V, Morgan RJ, Huerta R, Tsimring L, Soltesz I (2007) J Neurophysiol 97:1566-87 https://senselab.med.yale.edu/modeldb/showModel.cshtml?model=124513&file=/dentate_gyrus/ccanl.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

Mod File history:
calcium accumulation into a volume of area*depth next to the membrane with a decay (time constant tau) to resting level
given by caiinf	updating ECa for CaN.mod, CaT.mod and CaL.mod <ah 2011>

Warning by Ted Carnevale 2015:
The expression that this mechanism uses to calculate the contribution of ica to the rate of change of calcium concentration in the shell is 
-ica*(1e7)/(depth*FARADAY)
but it should really be
-ica*(1e7)/(depth*2*FARADAY)
because the valence of ca is 2.  The result of this omission is that the mechanism behaves as if the shell is only 1/2 as thick as the value specified by the depth parameter.

ENDCOMMENT

NEURON {
	SUFFIX ccanl
USEION nca READ ncai, inca, enca WRITE enca, ncai VALENCE 2
USEION lca READ lcai, ilca, elca WRITE elca, lcai VALENCE 2
USEION tca READ tcai, itca, etca WRITE etca, tcai VALENCE 2
RANGE caiinf, catau, cai, ncai, lcai,tcai, eca, elca, enca, etca
}

UNITS {
        (mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (milli/liter)
	(mA) = (milliamp)
	FARADAY = 96520 (coul)
	R = 8.3134	(joule/degC)
}

INDEPENDENT {t FROM 0 TO 100 WITH 100 (ms)}

PARAMETER {
        celsius = 6.3 (degC)
	depth = 200 (nm)	: assume volume = area*depth
	catau = 9 (ms)
	caiinf = 50.e-6 (mM)	: takes precedence over cai0_ca_ion
			: Do not forget to initialize in hoc if different
			: from this default.
	cao = 2 (mM)
	ica (mA/cm2)
	inca (mA/cm2)
	ilca (mA/cm2)
	itca (mA/cm2)
	cai= 50.e-6 (mM)
}

ASSIGNED {
	enca (mV)
	elca (mV)
	etca (mV)
	eca (mV)
}

STATE {
	ncai (mM)
	lcai (mM)
	tcai (mM)
}

INITIAL {
	VERBATIM
	ncai = _ion_ncai;
	lcai = _ion_lcai;
	tcai = _ion_tcai; 
	ENDVERBATIM
	ncai=caiinf/3
	lcai=caiinf/3
	tcai=caiinf/3
	cai = caiinf	
	eca = ktf() * log(cao/caiinf)	
	enca = eca
	elca = eca
	etca = eca
}


BREAKPOINT {
	SOLVE integrate METHOD derivimplicit
	cai = ncai+lcai+tcai 	
	eca = ktf() * log(cao/cai)	
	enca = eca : communicate new Ca reversal potential
	elca = eca : communicate new Ca reversal potential
	etca = eca : communicate new Ca reversal potential
}

DERIVATIVE integrate {
ncai' = -(inca)/depth/FARADAY * (1e7) + (caiinf/3 - ncai)/catau
lcai' = -(ilca)/depth/FARADAY * (1e7) + (caiinf/3 - lcai)/catau
tcai' = -(itca)/depth/FARADAY * (1e7) + (caiinf/3 - tcai)/catau
}

FUNCTION ktf() (mV) {
	ktf = (1000)*R*(celsius +273.15)/(2*FARADAY)
} 

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