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Binless strategies for estimation of information from neural data
Abstract
We present an approach to estimate information carried by experimentally observed neural spike trains elicited by known stimuli. This approach makes use of an embedding of the observed spike trains into a set of vector spaces, and entropy estimates based on the nearest-neighbor Euclidean distances within these vector spaces [L. F. Kozachenko and N. N. Leonenko, Probl. Peredachi Inf. 23, 9 (1987)]. Using numerical examples, we show that this approach can be dramatically more efficient than standard bin-based approaches such as the "direct" method [S. P. Strong, R. Koberle, R. R. de Ruyter van Steveninck, and W. Bialek, Phys. Rev. Lett. 80, 197 (1998)] for amounts of data typically available from laboratory experiments.
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Gray, C. M., König, P., Engel, A. K., & Singer, W. (1989) · Nature · doi:10.1038/338334a0 · Cited by 4,183
Cited by
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512
Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Transfer entropy—a model-free measure of effective connectivity for the neurosciences
Vicente, R., Wibral, M., Lindner, M., & Pipa, G. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-010-0262-3 · Cited by 1,014
Causality detection based on information-theoretic approaches in time series analysis
HLAVACKOVASCHINDLER, K., PALUS, M., VEJMELKA, M., & BHATTACHARYA, J. (2007) · Physics Reports · doi:10.1016/j.physrep.2006.12.004 · Cited by 800
Correcting for the sampling bias problem in spike train information measures
Panzeri, S., Senatore, R., Montemurro, M. A., & Petersen, R. S. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00559.2007 · Cited by 378
Metadata
- Authors
- Victor, J. D.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2002-11-11
- Volume
- 66
- Issue
- 5
- Pages
- 051903-051903
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W2119868454
- Cited by
- 238
- References
- 1
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- closed
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