Temporal correlations and neural spike train entropy
Biological Physics
2009-11-06 v3 Disordered Systems and Neural Networks
Neurons and Cognition
Abstract
Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight upon the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower RMS error information estimates in comparison to a `brute force' approach.
Cite
@article{arxiv.physics/0001006,
title = {Temporal correlations and neural spike train entropy},
author = {Simon R. Schultz and Stefano Panzeri},
journal= {arXiv preprint arXiv:physics/0001006},
year = {2009}
}
Comments
4 pages, 3 figures; final published version. In press, Physical Review Letters