KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)

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The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established.
1 . Ovsepian SV, Steuber V, Le Berre M, O'Hara L, O'Leary VB, Dolly JO (2013) A defined heteromeric KV1 channel stabilizes the intrinsic pacemaking and regulates the output of deep cerebellar nuclear neurons to thalamic targets. J Physiol 591:1771-91 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Channel/Receptor;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I CAN; I_Ks;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): Kv1.1 KCNA1; Kv1.2 KCNA2;
Simulation Environment: NEURON;
Model Concept(s): Bursting; Ion Channel Kinetics; Active Dendrites; Detailed Neuronal Models; Intrinsic plasticity; Rebound firing;
Implementer(s): Steuber, Volker [v.steuber at herts.ac.uk]; Luthman, Johannes [jwluthman at gmail.com];
Search NeuronDB for information about:  AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I CAN; I_Ks;
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DCNsyn.mod *
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DCNsynNMDA.mod *
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h.mod *
Ifluct8.mod *
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pasDCN.mod *
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DCN_mechs1.hoc *
DCN_morph.hoc *
// CN model used in Saak V Ovsepian, Volker Steuber, Marie Le 
// Berre, Liam O'Hara, Valerie B O'Leary, and J. Oliver Dolly 
// (2013). A Defined Heteromeric KV1 Channel Stabilizes the 
// Intrinsic Pacemaking and Regulates the Efferent Code of Deep 
// Cerebellar Nuclear Neurons to Thalamic Targets. Journal of 
// Physiology (epub ahead of print). 
// written by Johannes Luthman, modified by Volker Steuber
// procedures for recording and saving data for the simulations 
// that replicate Figure 9 in Ovsepian et al. (2013)

// Declare objects and strings used for recording and saving data.
objref recT, recV, recTraceT, recTraceV
objref vZerosAndOnes, vSpikeTimes
objref recGgaba
objref traceMatrix, fileTrace, fileSpikeTimes
strdef strFileNameBase

proc InstantiateRecObjects() {

    recT = new Vector(nRecVectElements)
    recV = new Vector(nRecVectElements)

    // Create vectors to record traces.
    if (tTraceStop[0] > 0) {
        recTraceT = new Vector(sizeVectorOfTrace)
        recTraceV = new Vector(sizeVectorOfTrace)
        recGgaba = new Vector(sizeVectorOfTrace)

proc SetupOutputFiles() {

    if (strcmp(strFilePrefix, "") == 0) {
        strFilePrefix = "OutputDCN"
    sprint(strFileNameBase, "%s_soma_%gs", strFilePrefix, int(0.5 + (runTime/1000)))

    fileSpikeTimes = new File()
    sprint(strTemp, "%s_ap.dat", strFileNameBase) //"ap" = action potential
    printf("starting simulation with output name %s\n", strTemp)

proc writeToTimeAndVoltVectors() {
    recT.x[iRecTimeVolt] = t
    recV.x[iRecTimeVolt] = soma.v(0.5)

proc writeToTraceVectors() { local subC, subSumGABAg, subSumGABAi, subSumExci
    recTraceT.x[iRecTrace] = t
    recTraceV.x[iRecTrace] = soma.v(0.5)

    // Record gaba conductance mean of all synapses.
    for (subC=0; subC < INHTOTALSYNAPSES; subC=subC+1) {
        subSumGABAg = subSumGABAg + gaba[subC].g
    recGgaba.x[iRecTrace] = subSumGABAg / INHTOTALSYNAPSES

proc writeSpikeTimesToFile() { local subC, subnIndeces, subnSpikes

    vZerosAndOnes = new Vector()
    vZerosAndOnes.spikebin(recV,-20) //feeding recV to spikebin gives vZerosAndOnes the same size as recV.
            // Some spikes reach just around 0 when excitatory synaptic input rates are high;
            // since not each dt is recorded, it's necessary to set the threshold to lower
            // than 0 to catch those spikes.

    // Run through the spike vector, containing 0s and 1s. For each 1, save
    // the corresponding time from recT to the new vector vSpikeTimes.
    subnIndeces = vZerosAndOnes.size()
    subnSpikes = 0
    vSpikeTimes = new Vector(subnIndeces)
    for(subC = 0; subC < subnIndeces; subC+=1) {
        if (vZerosAndOnes.x[subC] > 0.000001) {
            vSpikeTimes.x[subnSpikes] = recT.x[subC]
    //Remove trailing zeroes from spike vector.
    if (subnSpikes > 0.0000001) {
        vSpikeTimes.printf(fileSpikeTimes, "%g\n")
    if (t>=runTime) {
} // end of writeSpikeTimesToFile()

proc writeTracesToFile() { local subC

    // Before saving, determine number of non-zero entries of the vectors.
    // (some extra lines may have been added)
    subC = sizeVectorOfTrace-1
    while (recTraceT.x[subC] < 0.0000001) {
        subC = subC-1

    // First save time. (time is saved separately to save space,
    // due to its different requirement for the number of decimals)
    sprint(strTemp, "%s_time.dat", strFileNameBase)
    fileTrace = new File()
    recTraceT.resize(subC + 1)
    recTraceT.printf(fileTrace, "%g\n")

    // Work on the traces. First instantiate a matrix to use for saving the data.
    traceMatrix = new Matrix(subC + 1, 2)
    recTraceV.resize(subC + 1)
    traceMatrix.setcol(0, recTraceV)
    recGgaba.resize(subC + 1)
    traceMatrix.setcol(1, recGgaba)

    // Create a file to contain the traces.
    sprint(strTemp, "%s_trace.dat", strFileNameBase)
    fileTrace = new File()
    traceMatrix.fprint(0, fileTrace, "%.2e\t")

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