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The Upward Bias in Measures of Information Derived from Limited Data Samples
Abstract
Extracting information measures from limited experimental samples, such as those normally available when using data recorded in vivo from mammalian cortical neurons, is known to be plagued by a systematic error, which tends to bias the estimate upward. We calculate here the average of the bias, under certain conditions, as an asymptotic expansion in the inverse of the size of the data sample. The result agrees with numerical simulations, and is applicable, as an additive correction term, to measurements obtained under such conditions. Moreover, we discuss the implications for measurements obtained through other usual procedures.
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Edwards, S. F., & Anderson, P. W. (1975) · Journal of Physics F: Metal Physics · doi:10.1088/0305-4608/5/5/017 · Cited by 3,991
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Estimation of entropy and mutual information
Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
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
Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L., Rogov, P., Feizi, S., Gnirke, A., Callan, C. G., Kinney, J. B., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2137 · Cited by 711
Entropy and information in neural spike trains
Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R., & Bialek, W. (1998) · Physical Review Letters · doi:10.1103/physrevlett.80.197 · Cited by 791
Metadata
- Authors
- Treves, A., & Panzeri, S.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1995-03
- Volume
- 7
- Issue
- 2
- Pages
- 399-407
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2151387592
- Cited by
- 358
- References
- 1
- FWCI
- 6.43
- Type
- journal article
- Language
- en
- Open access
- closed
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