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DG adult-born granule cell: nonlinear a5-GABAARs control AP firing (Lodge et al, 2021)

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GABA can depolarize immature neurons close to the action potential (AP) threshold in development and adult neurogenesis. Nevertheless, GABAergic synapses effectively inhibit AP firing in newborn granule cells of the adult hippocampus as early as 2 weeks post mitosis. Parvalbumin and dendrite-targeting somatostatin interneurons activate a5-subunit containing GABAA receptors (a5-GABAARs) in young neurons, which show a voltage dependent conductance profile with increasing conductance around the AP threshold. The present computational models show that the depolarized GABA reversal potential promotes NMDA receptor activation. However, the voltage-dependent conductance of a5-GABAARs in young neurons is crucial for inhibition of AP firing to generate balanced and sparse firing activity.
1 . Lodge M, Hernandez MC, Schulz JM, Bischofberger J (2021) Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent a5-GABAA receptors Cell Reports [PubMed]
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: Dentate gyrus;
Cell Type(s): Dentate gyrus granule GLU cell;
Channel(s): I K; I Krp; I Na,t;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Detailed Neuronal Models; Development; Neurogenesis; Pattern Separation; Synaptic Integration;
Implementer(s): Schulz, Jan M [j.schulz at]; Bischofberger, Josef;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; NMDA; I Na,t; I K; I Krp; Gaba; Glutamate;
/* Sets nseg in each section to an odd value
   so that its segments are no longer than 
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works, 
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.

// these are reasonable values for most models

freq = 1000 //original:100     // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1 //original:0.1

func lambda_f() { local i, x1, x2, d1, d2, lam
        if (n3d() < 2) {
                return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
// above was too inaccurate with large variation in 3d diameter
// so now we use all 3-d points to get a better approximate lambda
        x1 = arc3d(0)
        d1 = diam3d(0)
        lam = 0
        for i=1, n3d()-1 {
                x2 = arc3d(i)
                d2 = diam3d(i)
                lam += (x2 - x1)/sqrt(d1 + d2)
                x1 = x2   d1 = d2
        //  length of the section in units of lambda
        lam *= sqrt(2) * 1e-5*sqrt(4*PI*$1*Ra*cm)

        return L/lam

proc geom_nseg_shared() {
  area(0.5) // make sure diam reflects 3d points
  forall { 
	nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1
	if (debug_mode) {
	 printf("%s: lambda(%d)=%g nseg=%d\n",secname(),freq, lambda_f(freq), nseg)


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