CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2019)

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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 *
cadyn_new.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 *
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kadistcr.mod *
kap.mod *
Kaxon.mod *
kca.mod *
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kdrcr.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
Naaxon.mod *
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nafcr.mod *
nap.mod *
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nca.mod *
nmda.mod *
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somacar.mod *
STDPE2Syn.mod *
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A synaptic current with two dual exponential function conductances,
representing non-voltage-dependent AMPA and voltage-dependent NMDA
components.  The basic dual exponential conductance is given by:
         g = 0 for t < onset and
         g = gmax*((tau1*tau2)/(tau1-tau2)) * (exp(-(t-onset)/tau1)-exp(-(t-onset)/tau2))
         for t > onset (tau1 and tau2 are fast and slow time constants)
The synaptic current is:
        i = (gA + gN) * (v - e)      i(nanoamps), g(micromhos);
NMDA model taken from Mel, J. Neurophys. 70:1086-1101, 1993
BPG 1-12-00

    RANGE onset, gmax, e, i, g, gA, gN, tau1, tau2, Ntau1, Ntau2, eta, Mg, gamma, Nfrac

    (nA) = (nanoamp)
    (mV) = (millivolt)
    (umho) = (micromho)

    onset=0 (ms)
    tau1=.2 (ms)    <1e-3,1e6>
    tau2=2 (ms)    <1e-3,1e6>
    Ntau1=.66 (ms)    <1e-3,1e6>
    Ntau2=80 (ms)    <1e-3,1e6>
    eta=0.33 (/mM)
    Mg=1 (mM)
    gamma=0.06 (/mV)
    gmax=0  (umho)  <0,1e9>
    e=0 (mV)
    v   (mV)

ASSIGNED { i (nA)  g (umho) gA (umho) gN (umho) Agmax (umho) Ngmax (umho)}

    Agmax = (1-Nfrac)*gmax
    Ngmax = Nfrac*gmax

    gA = Agmax*((tau1*tau2)/(tau1-tau2))*duale((t-onset)/tau1,(t-onset)/tau2)
    gN = Ngmax*((Ntau1*Ntau2)/(Ntau1-Ntau2))*duale((t-onset)/Ntau1,(t-onset)/Ntau2)
    gN = gN / (1 + (eta*Mg*exp(-gamma*v)))
    g = gA + gN
    i = g*(v - e)

FUNCTION duale(x,y) {
    if (x < 0 || y < 0) {
        duale = 0
        duale = exp(-x) - exp(-y)