CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2020)

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Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy. To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels.
1 . Shuman T, Aharoni D, Cai DJ, Lee CR, Chavlis S, Page-Harley L, Vetere LM, Feng Y, Yang CY, Mollinedo-Gajate I, Chen L, Pennington ZT, Taxidis J, Flores SE, Cheng K, Javaherian M, Kaba CC, Rao N, La-Vu M, Pandi I, Shtrahman M, Bakhurin KI, Masmanidis SC, Khakh BS, Poirazi P, Silva AJ, Golshani P (2020) Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nat Neurosci 23:229-238 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampal CA1 CR/VIP cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA1 basket cell; Hippocampus CA1 basket cell - CCK/VIP; Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron ; Hippocampus CA1 bistratified cell;
Channel(s): I A; I h; I K,Ca; I K; I CAN; I M; I Sodium; I_AHP; I Calcium;
Gap Junctions:
Receptor(s): AMPA; GabaA; GabaB; NMDA;
Simulation Environment: NEURON; Brian;
Model Concept(s): Spatial Navigation;
Implementer(s): Chavlis, Spyridon [schavlis at]; Pandi, Ioanna ; Poirazi, Panayiota [poirazi at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I A; I K; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I_AHP;
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Sodium current for the soma


1.	Martina, M., Vida, I., and Jonas, P.  Distal initiation and active
	propagation of action potentials in interneuron dendrites,
	Science, 287:295-300, 2000.

			soma	axon-lacking dend	axon-bearing dend
Na+	gmax	    107 ps/um2	   117 ps/um2		   107 ps/um2
	slope 	    10.9 mV/e	   11.2 mV/e		   11.2 mV/e
	V1/2        -37.8 mV       -45.6 mV                -45.6 mV

2.	Marina, M. and Jonas, P.  Functional differences in Na+ channel
	gating between fast-spiking interneurons and principal neurones of rat
	hippocampus, J. Physiol., 505.3:593-603, 1997.

*Note* The interneurons here are basket cells from the dentate gyrus.

Na+	Activation V1/2				-25.1 mV
	slope			 		11.5
	Activation t (-20 mV)	 		0.16 ms
	Deactivation t (-40 mV)	 		0.13 ms
 	Inactivation V1/2			-58.3 mV
	slope			 		6.7
	onset of inactivation t (-20 mV)	1.34 ms
	onset of inactivation t (-55 mV)	18.6 ms
	recovery from inactivation t		2.0 ms
	(30 ms conditioning pulse)
	recovery from inactivation t		2.7 ms
	(300 ms conditioning pulse)

    (mA) = (milliamp)
    (mV) = (millivolt)
    SUFFIX Nasoma
    USEION na READ ena WRITE ina
    RANGE gnasoma, gl, el, ina
    GLOBAL minf, hinf, hexp, mtau, htau
    v                (mV)
    celsius = 24     (degC)
    dt               (ms)
    gnasoma = .0107  (mho/cm2)
    ena     = 90     (mV)
    gl      = .00005 (mho/cm2)
    el      = -70    (mV)
STATE { m h }
    ina (mA/cm2)
    il (mA/cm2)
	mtau (ms)
	htau (ms)
	m = minf
	h = hinf

    SOLVE states
	ina = gnasoma*minf*minf*minf*h*(v - ena)    
    il = gl*(v - el)

PROCEDURE states() {	:exact when v held constant
	h = h + hexp*(hinf - h)
	return 0;
PROCEDURE evaluate_fct(v(mV)) {  :Computes rate and other constants at 
	:current v.
    :Call once from HOC to initialize inf at resting v.
    LOCAL q10, tinc, alpha, beta
    TABLE minf, hinf, hexp, mtau, htau DEPEND dt, celsius FROM -200 TO 100 WITH 300
	:q10 = 3^((celsius - 24)/10)
	q10   = 1	: BPG
	tinc  = -dt*q10
	alpha = 0.1*vtrap(-(v+38),10)
	beta  = 4*exp(-(v+63)/18)
	mtau  = 1/(alpha + beta)
	minf  = alpha*mtau
	alpha = 0.07*Exp(-(v+63)/20)
	beta  = 1/(1+Exp(-(v+33)/10))
	htau  = 1/(alpha + beta)
	hinf  = alpha*htau
	hexp  = 1-Exp(tinc/htau)

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)

	if (x < -100) {
		Exp = 0
		Exp = exp(x)