Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)

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Accession:118631
Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008.
References:
1 . Koendgen H, Geisler C, Wang XJ, Fusi S, Luescher HR, Giugliano M (2004) The dynamical response of single cells to noisy time-varying currents Soc Neurosci Abstr :640
2 . Köndgen H, Geisler C, Fusi S, Wang XJ, Lüscher HR, Giugliano M (2008) The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex 18:2086-97 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Parameter Fitting; Methods; Rate-coding model neurons;
Implementer(s): Giugliano, Michele [mgiugliano at gmail.com]; Delattre, Vincent;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K;
objectvar g[20]         // max 20 graphs
ngraph = 0

proc addgraph() { local ii  // define subroutine to add a new graph
                // addgraph("variable", minvalue, maxvalue)
    ngraph = ngraph+1
    ii = ngraph-1
    g[ii] = new Graph()
    g[ii].size(tstart,tstop,$2,$3)
    g[ii].xaxis()
    g[ii].yaxis()
    g[ii].addvar($s1,1,0)
    g[ii].save_name("graphList[0].")
    graphList[0].append(g[ii])
}

proc makegraph() { local ii // define subroutine to add a new graph
                // makeraph("variable", xmin,xmax,ymin,ymax)
    ngraph = ngraph+1
    ii = ngraph-1
    g[ii] = new Graph()
    g[ii].size($2,$3,$4,$5)
    g[ii].xaxis()
    g[ii].yaxis()
    g[ii].addvar($s1,1,0)
    g[ii].save_name("graphList[0].")
    graphList[0].append(g[ii])
}


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