Literature DB >> 11387048

A unified approach to the study of temporal, correlational, and rate coding.

S Panzeri1, S R Schultz.   

Abstract

We demonstrate that the information contained in the spike occurrence times of a population of neurons can be broken up into a series of terms, each reflecting something about potential coding mechanisms. This is possible in the coding regime in which few spikes are emitted in the relevant time window. This approach allows us to study the additional information contributed by spike timing beyond that present in the spike counts and to examine the contributions to the whole information of different statistical properties of spike trains, such as firing rates and correlation functions. It thus forms the basis for a new quantitative procedure for analyzing simultaneous multiple neuron recordings and provides theoretical constraints on neural coding strategies. We find a transition between two coding regimes, depending on the size of the relevant observation timescale. For time windows shorter than the timescale of the stimulus-induced response fluctuations, there exists a spike count coding phase, in which the purely temporal information is of third order in time. For time windows much longer than the characteristic timescale, there can be additional timing information of first order, leading to a temporal coding phase in which timing information may affect the instantaneous information rate. In this new framework, we study the relative contributions of the dynamic firing rate and correlation variables to the full temporal information, the interaction of signal and noise correlations in temporal coding, synergy between spikes and between cells, and the effect of refractoriness. We illustrate the utility of the technique by analyzing a few cells from the rat barrel cortex.

Entities:  

Mesh:

Year:  2001        PMID: 11387048     DOI: 10.1162/08997660152002870

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  32 in total

1.  Tuning neocortical pyramidal neurons between integrators and coincidence detectors.

Authors:  Michael Rudolph; Alain Destexhe
Journal:  J Comput Neurosci       Date:  2003 May-Jun       Impact factor: 1.621

2.  Decoding neuronal spike trains: how important are correlations?

Authors:  Sheila Nirenberg; Peter E Latham
Journal:  Proc Natl Acad Sci U S A       Date:  2003-05-29       Impact factor: 11.205

3.  Synergy, redundancy, and independence in population codes.

Authors:  Elad Schneidman; William Bialek; Michael J Berry
Journal:  J Neurosci       Date:  2003-12-17       Impact factor: 6.167

4.  The use of decoding to analyze the contribution to the information of the correlations between the firing of simultaneously recorded neurons.

Authors:  Leonardo Franco; Edmund T Rolls; Nikolaos C Aggelopoulos; Alessandro Treves
Journal:  Exp Brain Res       Date:  2004-01-13       Impact factor: 1.972

5.  Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

Authors:  J B M Goense; R Ratnam
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2003-08-14       Impact factor: 1.836

6.  Redundancy and synergy arising from pairwise correlations in neuronal ensembles.

Authors:  Michele Bezzi; Mathew E Diamond; Alessandro Treves
Journal:  J Comput Neurosci       Date:  2002 May-Jun       Impact factor: 1.621

7.  Millisecond encoding precision of auditory cortex neurons.

Authors:  Christoph Kayser; Nikos K Logothetis; Stefano Panzeri
Journal:  Proc Natl Acad Sci U S A       Date:  2010-09-13       Impact factor: 11.205

8.  Encoding stimulus information by spike numbers and mean response time in primary auditory cortex.

Authors:  Israel Nelken; Gal Chechik; Thomas D Mrsic-Flogel; Andrew J King; Jan W H Schnupp
Journal:  J Comput Neurosci       Date:  2005-10       Impact factor: 1.621

Review 9.  Spike train metrics.

Authors:  Jonathan D Victor
Journal:  Curr Opin Neurobiol       Date:  2005-10       Impact factor: 6.627

Review 10.  A Tutorial for Information Theory in Neuroscience.

Authors:  Nicholas M Timme; Christopher Lapish
Journal:  eNeuro       Date:  2018-09-11
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