CA1 pyramidal neuron (Combe et al 2018)

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Accession:244416
"Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase-locked to activity in area CA3 and presumably driven by the Schaffer collaterals. We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to Schaffer collateral stimulation selectively at slow gamma frequencies, and to identify the mechanisms involved. Both approaches demonstrated that in response to temporally precise input at Schaffer collaterals, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase-locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. ..."
Reference:
1 . Combe CL, Canavier CC, Gasparini S (2018) Intrinsic Mechanisms of Frequency Selectivity in the Proximal Dendrites of CA1 Pyramidal Neurons. J Neurosci 38:8110-8127 [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 GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Gamma oscillations;
Implementer(s): Canavier, CC;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium;
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CombeEtAl2018
experiment
lib
pc2b
template
readme.html
cad.mod
cagk.mod
cal.mod *
calH.mod
car.mod
cat.mod
d3.mod *
exp2i.mod *
h.mod
kadist.mod
kaprox.mod
kca.mod
kcasimple.mod
kdr.mod
km.mod
na3.mod
na3dend.mod
na3notrunk.mod
nap.mod
nax.mod
netstims.mod
nmdanet.mod
somacar.mod
stim2.mod *
cell-setup.hoc
fixnseg.hoc
init.hoc
mosinit.hoc *
multisyn.hoc
print.ses
screenshot1.png
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simplestim.hoc
trunk.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  }

}



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