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.
Model Type: Realistic Network; Axon
Region(s) or Organism(s): Neocortex
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron
Model Concept(s): Parameter Fitting; Methods; Rate-coding model neurons
Simulation Environment: NEURON; MATLAB
Implementer(s): Giugliano, Michele [mgiugliano at gmail.com]; Delattre, Vincent
References:
Wang XJ et al. (2004). The dynamical response of single cells to noisy time-varying currents Soc Neurosci Abstr.
Köndgen H et al. (2008). The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cerebral cortex (New York, N.Y. : 1991). 18 [PubMed]