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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; 
create soma                 // This creates a single-compartmental neuron..

// ---> SOMA <--- 
soma {             
 L     = 65          // [um] length of a cilindrical cable
 diam  = 65          // [um] diameter of a cilindrical cable
 nseg  = 1           // number of segments - see the documentation


 insert pas          // inserts passive mechanisms (means leak currents, voltage-indep.)
 g_pas = 1e-4
 e_pas = -67.        // [mV]      - Leak currents, reversal potential
 cm    = 1.          // [uF/cm^2] - specific membrane capacitance
 Ra    = 35.4        // [ohm cm]  - axial/cytosolic resistivity - useless in a single-compartmental model

insert wb
gnabar  = .007  //
gkbar   = .009  //

} // end soma

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