Temporal pairwise spike correlations fully capture single-neuron information

Amadeus Dettner, Sabrina Münzberg and Tatjana Tchumatchenko ()
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Amadeus Dettner: Theory of Neural Dynamics Group, Max Planck Institute for Brain Research
Sabrina Münzberg: Theory of Neural Dynamics Group, Max Planck Institute for Brain Research
Tatjana Tchumatchenko: Theory of Neural Dynamics Group, Max Planck Institute for Brain Research

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract To crack the neural code and read out the information neural spikes convey, it is essential to understand how the information is coded and how much of it is available for decoding. To this end, it is indispensable to derive from first principles a minimal set of spike features containing the complete information content of a neuron. Here we present such a complete set of coding features. We show that temporal pairwise spike correlations fully determine the information conveyed by a single spiking neuron with finite temporal memory and stationary spike statistics. We reveal that interspike interval temporal correlations, which are often neglected, can significantly change the total information. Our findings provide a conceptual link between numerous disparate observations and recommend shifting the focus of future studies from addressing firing rates to addressing pairwise spike correlation functions as the primary determinants of neural information.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13805

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DOI: 10.1038/ncomms13805

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