The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analytical estimates of limited sampling biases in different information measures
Abstract
Measuring the information carried by neuronal activity is made difficult, particularly when recording from mammalian cells, by the limited amount of data usually available, which results in a systematic error. While empirical ad hoc procedures have been used to correct for such error, we have recently proposed a direct procedure consisting of the analytical calculation of the average error, its estimation (up to subleading terms) from the data, and its subtraction from raw information measures to yield unbiased measures. We calculate here the leading correction terms for both the average transmitted information and the conditional information and, since usually one must first regularize the data, we specify the expressions appropriate to different regularizations. Computer simulations indicate a broad range of validity of the analytical results, suggest the effectiveness of regularizing by simple binning and illustrate the advantage of this over the previously used 'bootstrap' procedure.
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In the citing paper
“For the discrete case, exact expressions for the bias are not known. However, approximate expressions are available in the large-N limit.24”
From Sampling bias corrections for discrete and Gaussian partial information decompositions (2026) · cites this paper as [24]
In the citing paper
“The decrease of the bias with the information level can also be explained from these analytical calculations,24 because the bias increases with the number of discrete responses that are possible (i.e., with non-zero probability). Low information levels correspond to less concentrated stimulus-specific distributions and, thus, have a larger number of possible responses and are more biased.”
From Sampling bias corrections for discrete and Gaussian partial information decompositions (2026) · cites this paper as [24]
In the citing paper
“Earlier applications of information theory to neuroscience have recognized that Shannon information measures from neural activity suffer from a prominent limited-sampling bias.24,25,26”
From Sampling bias corrections for discrete and Gaussian partial information decompositions (2026) · cites this paper as [24]
In the citing paper
“The information-theoretic neuroscience literature has long recognized the importance of correcting for the limited-sampling bias and has provided effective tools for correcting the Shannon information sampling bias.24,25,26,27,28”
From Sampling bias corrections for discrete and Gaussian partial information decompositions (2026) · cites this paper as [24]
Citations
Cites
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Cited by
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Paninski, L. (2003) · Neural Computation · doi:10.1162/089976603321780272 · Cited by 1,442
Hippocampus leads ventral striatum in replay of place-reward information
Lansink, C. S., Goltstein, P. M., Lankelma, J. V., McNaughton, B. L., & Pennartz, C. M. A. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000173 · Cited by 400
Wirth, S., Baraduc, P., Planté, A., Pinède, S., & Duhamel, J.-R. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2001045 · Cited by 129
Sampling bias corrections for discrete and Gaussian partial information decompositions
Lorenz, G. M., Engel, N. M., Koçillari, L., Celotto, M., Orsenigo, D., Curreli, S., Malerba, S. B., Engel, A. K., Kayser, C., Fellin, T., et al. (2026) · Patterns · doi:10.1016/j.patter.2026.101619 · Cited by 9
Self-citation
Metadata
- Authors
- Panzeri, S., & Treves, A.
- Affiliations
- Biophysics, SISSA, via Beirut 2–4, 34013 Trieste, Italy
- Venue
- Network: Computation in Neural Systems
- Publisher
- Informa UK Limited
- Published
- 1996-01
- Volume
- 7
- Issue
- 1
- Pages
- 87-107
- ISSN
- 0954-898X
- 1361-6536
- Cited by
- 138
- References
- 1
- Language
- en
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- Panzeri, S., & Treves, A. (1996). Analytical estimates of limited sampling biases in different information measures. Network: Computation in Neural Systems. https://doi.org/10.1080/0954898x.1996.11978656
