CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:126776
The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.
Reference:
1 . Ascoli GA, Gasparini S, Medinilla V, Migliore M (2010) Local Control of Post-Inhibitory Rebound Spiking in CA1 Pyramidal Neuron Dendrites J.Neurosci. 30(18):6434-42 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I Na,t; I A; I h; I Potassium;
Gap Junctions:
Receptor(s): Gaba;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Activity Patterns; Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Action Potentials; Intrinsic plasticity; Synaptic Integration;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; Gaba; I Na,t; I A; I h; I Potassium; Gaba;
/
rebound
readme.html
h.mod
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
4-ap.jpg
4-ap_plus_zd.jpg
control.jpg
fixnseg.hoc *
mod_func.c
mosinit.hoc
pc2b.hoc *
rebound.hoc
rebound.ses
                            
/* 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 = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 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() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }
}