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  Representation of Dynamical Stimuli in Populations of Threshold Neurons

Tchumatchenko, T., & Wolf, F. (2011). Representation of Dynamical Stimuli in Populations of Threshold Neurons. PLoS Computational Biology, 7: e1002239. doi:10.1371/journal.pcbi.1002239.

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 Creators:
Tchumatchenko, Tatjana1, Author           
Wolf, Fred1, 2, Author           
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1Research Group Theoretical Neurophysics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063289              
2Department of Nonlinear Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063286              

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 Abstract: Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework.

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Language(s): eng - English
 Dates: 2011-10-20
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 576002
DOI: 10.1371/journal.pcbi.1002239
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Title: PLoS Computational Biology
  Alternative Title : PLoS Comp Biol
Source Genre: Journal
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Pages: - Volume / Issue: 7 Sequence Number: e1002239 Start / End Page: - Identifier: -