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;
ANsyn.mod *
bgka.mod *
burststim2.mod *
cadyn.mod *
cagk.mod *
cal.mod *
calH.mod *
cancr.mod *
car.mod *
cat.mod *
ccanl.mod *
gskch.mod *
h.mod *
hha_old.mod *
hha2.mod *
hNa.mod *
IA.mod *
iccr.mod *
ichan2.mod *
ichan2aa.mod *
ichan2bc.mod *
ichan2bs.mod *
ichan2vip.mod *
Ih.mod *
Ihvip.mod *
ikscr.mod *
kad.mod *
kadistcr.mod *
kap.mod *
Kaxon.mod *
kca.mod *
Kdend.mod *
kdrcr.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
Naaxon.mod *
Nadend.mod *
nafcr.mod *
nap.mod *
Nasoma.mod *
nca.mod *
nmda.mod *
regn_stim.mod *
somacar.mod *
STDPE2Syn.mod *
vecstim.mod *
TITLE HH channel that includes both a sodium and a delayed rectifier channel 
: and accounts for sodium conductance attenuation
: Bartlett Mel-modified Hodgkin - Huxley conductances (after Ojvind et al.)
: Terrence Brannon-added attenuation 
: Yiota Poirazi-modified Kdr and Na threshold and time constants to make it more stable
: Yiota Poirazi-modified threshold for soma/axon spike initiation (threshold about -57 mV),
: USC Los Angeles 2000,
: This file is used only in soma and axon sections

	SUFFIX hha2
	RANGE gnabar, gkbar, gl, el
	RANGE ar2, vhalfs
	RANGE inf, fac, tau
	RANGE taus
	GLOBAL taumin

	(mA) = (milliamp)
	(mV) = (millivolt)


: parameters that can be entered when function is called in cell-setup
a0r   = 0.0003 (ms)
b0r   = 0.0003 (ms)
zetar = 12    
zetas = 12   
gmr   = 0.2   
ar2   = 1.0 :initialized parameter for location-dependent

: Na-conductance attenuation, "s", (ar=1 -> zero attenuation)
taumin = 3      (ms)       :min activation time for "s" attenuation system
vvs    = 2      (mV)       :slope for "s" attenuation system
vhalfr = -60    (mV)       :half potential for "s" attenuation system
W      = 0.016  (/mV)      :this 1/61.5 mV
: gnabar = 0.2 (mho/cm2)     :suggested conductance values
: gkbar  = 0.12 (mho/cm2)
: gl     = 0.0001  (mho/cm2)
gnabar  = 0       (mho/cm2)  :initialized conductances
gkbar   = 0       (mho/cm2)  :actual values set in cell-setup.hoc
gl      = 0       (mho/cm2)
ena     = 60      (mV)       :Na reversal potential (also reset in
ek      = -77     (mV)       :K reversal potential  cell-setup.hoc)
el      = -70.0   (mV)       :steady state 
celsius = 34      (degC)
v                 (mV)

STATE {				:the unknown parameters to be solved in the DEs
	m h n s

ASSIGNED {			:parameters needed to solve DE
	ina (mA/cm2)
	ik (mA/cm2)
	il (mA/cm2)

	SOLVE states
	ina = gnabar*m*m*h*s*(v - ena)  :Sodium current
	ik  = gkbar*n*n*(v - ek)        :Potassium current
	il  = gl*(v - el)               :leak current

INITIAL {			:initialize the following parameter using states()
	s   = 1
	ina = gnabar*m*m*h*s*(v - ena)
	ik  = gkbar*n*n*(v - ek)
	il  = gl*(v - el)

PROCEDURE calcg() {
	m = m + fac[0]*(inf[0] - m)  :Na activation variable
	h = h + fac[1]*(inf[1] - h)  :Na inactivation variable
	n = n + fac[2]*(inf[2] - n)  :K activation variable
	s = s + fac[3]*(inf[3] - s)  :Na attenuation variable

PROCEDURE states() {	: exact when v held constant
	return 0;

FUNCTION varss(v, i) { :steady state values
	if (i==0) {
        varss = 1 / (1 + exp((v + 42.5)/(-3)))    : Na activation
	else if (i==1) {
        varss = 1 / (1 + exp((v + 49)/(3.5)))     : Na inactivation 
	else if (i==2) {	
        varss = 1 / (1 + exp((v + 46.3)/(-3)))    : K activation

	} else {
        :"s" activation system for spike attenuation - Migliore 96 model
		varss = alpv(v,vhalfr)

FUNCTION alpv(v(mV),vh) {    :used in "s" activation system infinity calculation
  alpv = (1+ar2*exp((v-vh)/vvs))/(1+exp((v-vh)/vvs))

FUNCTION alpr(v(mV)) {       :used in "s" activation system tau
  alpr = exp(1.e-3*zetar*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 

FUNCTION betr(v(mV)) {       :used in "s" activation system tau
  betr = exp(1.e-3*zetar*gmr*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 

FUNCTION vartau(v, i) { :estimate tau values
	LOCAL tmp

	if (i==0) {
	    vartau = 0.05  :Na activation tau
	else if (i==1) {
            vartau = 1     :Na inactivation tau
	else if (i==2) {
            vartau = 3.5   :K activation
    } else {
		tmp = betr(v)/(a0r+b0r*alpr(v))
		if (tmp<taumin) {tmp=taumin}
	    vartau = tmp   :s activation tau

PROCEDURE mhn(v) {LOCAL a, b :rest = -70
:       TABLE infinity, tau, fac DEPEND dt, celsius FROM -100 TO 100 WITH 200
	FROM i=0 TO 3 {
		tau[i] = vartau(v,i)
		inf[i] = varss(v,i)
		fac[i] = (1 - exp(-dt/tau[i]))