Temporal Correlations and Neural Spike Train Entropy
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 to 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 {open_quotes}brute force{close_quotes} approach.
- Authors:
- Publication Date:
- Sponsoring Org.:
- (US)
- OSTI Identifier:
- 40204699
- Resource Type:
- Journal Article
- Journal Name:
- Physical Review Letters
- Additional Journal Information:
- Journal Volume: 86; Journal Issue: 25; Other Information: DOI: 10.1103/PhysRevLett.86.5823; Othernumber: PRLTAO000086000025005823000001; 032126PRL; PBD: 18 Jun 2001; Journal ID: ISSN 0031-9007
- Publisher:
- The American Physical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ENTROPY; MONKEYS; SAMPLING
Citation Formats
Schultz, Simon R, and Panzeri, Stefano. Temporal Correlations and Neural Spike Train Entropy. United States: N. p., 2001.
Web. doi:10.1103/PhysRevLett.86.5823.
Schultz, Simon R, & Panzeri, Stefano. Temporal Correlations and Neural Spike Train Entropy. United States. https://doi.org/10.1103/PhysRevLett.86.5823
Schultz, Simon R, and Panzeri, Stefano. 2001.
"Temporal Correlations and Neural Spike Train Entropy". United States. https://doi.org/10.1103/PhysRevLett.86.5823.
@article{osti_40204699,
title = {Temporal Correlations and Neural Spike Train Entropy},
author = {Schultz, Simon R and Panzeri, Stefano},
abstractNote = {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 to 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 {open_quotes}brute force{close_quotes} approach.},
doi = {10.1103/PhysRevLett.86.5823},
url = {https://www.osti.gov/biblio/40204699},
journal = {Physical Review Letters},
issn = {0031-9007},
number = 25,
volume = 86,
place = {United States},
year = {Mon Jun 18 00:00:00 EDT 2001},
month = {Mon Jun 18 00:00:00 EDT 2001}
}
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