Entropy factor for randomness quantification in neuronal data

A novel measure of neural spike train randomness, an entropy factor, is proposed. It is based on the Shannon entropy of the number of spikes in a time window and can be seen as an analogy to the Fano factor. Theoretical properties of the new measure are studied for equilibrium renewal processes and...

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Bibliographic Details
Published in:Neural networks 2017-11, Vol.95, p.57-65
Main Authors: Rajdl, K., Lansky, P., Kostal, L.
Format: Article
Language:English
Subjects:
Action Potentials
Animals
Entropy
Fano factor
Macaca
Models, Neurological
Neurons - physiology
Probability
Renewal process
Shannon entropy
Variability measure
Visual Cortex - physiology
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Summary:A novel measure of neural spike train randomness, an entropy factor, is proposed. It is based on the Shannon entropy of the number of spikes in a time window and can be seen as an analogy to the Fano factor. Theoretical properties of the new measure are studied for equilibrium renewal processes and further illustrated on gamma and inverse Gaussian probability distributions of interspike intervals. Finally, the entropy factor is evaluated from the experimental records of spontaneous activity in macaque primary visual cortex and compared to its theoretical behavior deduced for the renewal process models. Both theoretical and experimental results show substantial differences between the Fano and entropy factors. Rather paradoxically, an increase in the variability of spike count is often accompanied by an increase of its predictability, as evidenced by the entropy factor.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2017.07.016