Feedforward inhibition in pyramidal cells (Ferrante & Ascoli 2015)

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Accession:206378
"Feedforward inhibition (FFI) enables pyramidal cells in area CA1 of the hippocampus (CA1PCs) to remain easily excitable while faithfully representing a broad range of excitatory inputs without quickly saturating. Despite the cortical ubiquity of FFI, its specific function is not completely understood. FFI in CA1PCs is mediated by two physiologically and morphologically distinct GABAergic interneurons: fast-spiking, perisomatic-targeting basket cells and regular-spiking, dendritic-targeting bistratified cells. These two FFI pathways might create layer-specific computational sub-domains within the same CA1PC, but teasing apart their specific contributions remains experimentally challenging. We implemented a biophysically realistic model of CA1PCs using 40 digitally reconstructed morphologies and constraining synaptic numbers, locations, amplitude, and kinetics with available experimental data. ..."
Reference:
1 . Ferrante M, Ascoli GA (2015) Distinct and synergistic feedforward inhibition of pyramidal cells by basket and bistratified interneurons. Front Cell Neurosci 9:439 [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: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell; Hippocampus CA1 bistratified cell; Hippocampus CA1 basket cell;
Channel(s): I K; I A; I h; I Na,t;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Synaptic Integration;
Implementer(s): Ferrante, Michele [mferr133 at bu.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; I Na,t; I A; I K; I h;
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FFI_CA1
readme.html
distr.mod *
h.mod
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
c20465.hoc
ffi.hoc
Fig3F_OrangeCurveSubSampled.hoc
fixnseg.hoc *
mosinit.hoc
n1.txt
regNsyn.hoc *
screenshot.png
                            
/* 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  }
//  forall nseg *= 3
}


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