Channel density variability among CA1 neurons (Migliore et al. 2018)

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Accession:244688
The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.
Reference:
1 . Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Roessert CA, Shi Y, Hagens O, Pezzoli M, Freund TF, Kali S, Muller EB, Schuermann F, Markram H, Migliore M (2018) The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow PLOS Computational Biology
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I h; Ca pump; I K; I K,Ca; I Calcium; I CAN; I M; I Na,t; I A; I_KD; I T low threshold; I L high threshold;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; BluePyOpt ;
Model Concept(s): Activity Patterns; Action Potentials; Detailed Neuronal Models; Methods; Parameter Fitting;
Implementer(s): Migliore, Rosanna [rosanna.migliore at cnr.it]; Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I CAN; I Calcium; I_KD; Ca pump;
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MiglioreEtAl2018PLOSCompBiol2018
morphologies
readme_file
readme.htm
cacumm.mod *
cacummb.mod
cagk.mod *
cal2.mod *
can2.mod *
cat.mod *
h.mod
kadist.mod *
kaprox.mod *
kca.mod
kdb.mod
kdrbca1.mod
kdrca1.mod *
kmb.mod *
na3n.mod
naxn.mod
cell_seed1_0-bac-10.hoc
cell_seed1_0-cnac-04.hoc
cell_seed2_0-bac-06.hoc
cell_seed2_0-cnac-08.hoc
cell_seed3_0-pyr-08.hoc
cell_seed4_0-cac-06.hoc
cell_seed4_0-pyr-04.hoc
cell_seed7_0-cac-04.hoc
fig4A-model.hoc
fig4A-model.ses
mosinit.hoc
                            
TITLE K-A channel from Klee Ficker and Heinemann
: modified to account for Dax A Current ----------
: M.Migliore Jun 1997

UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
}

PARAMETER {
	celsius
        v (mV)
        gkabar=.008 (mho/cm2)
        vhalfn=-1   (mV)
        vhalfl=-56   (mV)
        a0l=0.05      (/ms)
        a0n=.1    (/ms)
        zetan=-1.8    (1)
        zetal=3    (1)
        gmn=0.39   (1)
        gml=1   (1)
        lmin=2  (mS)
        nmin=0.2  (mS)
        pw=-1    (1)
        tq=-40
        qq=5
        q10=5
        qtl=1
	ek
}


NEURON {
        SUFFIX kad
        USEION k READ ek WRITE ik
        RANGE gkabar,gka
        GLOBAL ninf,linf,taul,taun,lmin
}

STATE {
        n
        l
}

ASSIGNED {
        ik (mA/cm2)
        ninf
        linf      
        taul
        taun
        gka
}

BREAKPOINT {
        SOLVE states METHOD cnexp
        gka = gkabar*n*l
        ik = gka*(v-ek)

}

INITIAL {
	rates(v)
	n=ninf
	l=linf
}


FUNCTION alpn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  alpn = exp(1.e-3*zeta*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betn(v(mV)) {
LOCAL zeta
  zeta=zetan+pw/(1+exp((v-tq)/qq))
  betn = exp(1.e-3*zeta*gmn*(v-vhalfn)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION alpl(v(mV)) {
  alpl = exp(1.e-3*zetal*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betl(v(mV)) {
  betl = exp(1.e-3*zetal*gml*(v-vhalfl)*9.648e4/(8.315*(273.16+celsius)))
 
}

DERIVATIVE states {  
        rates(v)
        n' = (ninf - n)/taun
        l' = (linf - l)/taul
}

PROCEDURE rates(v (mV)) { :callable from hoc
        LOCAL a,qt
        qt=q10^((celsius-24)/10)
        a = alpn(v)
        ninf = 1/(1 + a)
        taun = betn(v)/(qt*a0n*(1+a))
        if (taun<nmin) {taun=nmin}
        a = alpl(v)
        linf = 1/(1+ a)
        taul = 0.26*(v+50)/qtl
        if (taul<lmin/qtl) {taul=lmin/qtl}
}


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