Browse by author
Lookup NU author(s): Dr Stefano Panzeri
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
Author(s): Panzeri S, Schultz SR
Publication type: Article
Publication status: Published
Journal: Neural Computation
Year: 2001
Volume: 13
Issue: 6
Pages: 1311-1349
ISSN (print): 0899-7667
ISSN (electronic): 1530-888X
Publisher: MIT Press
URL: http://dx.doi.org/10.1162/08997660152002870
DOI: 10.1162/08997660152002870
PubMed id: 11387048
Altmetrics provided by Altmetric
