Fisher information in Poissonian model neurons

Eitan Z. Gross

Physica A: Statistical Mechanics and its Applications, 2020, vol. 542, issue C

Abstract: Mutual information (MI) is being widely used to analyze the neural code in a variety of stochastic neuronal sensory systems. Unfortunately, MI is analytically tractable only for simple coding problems. One way to address this difficulty is to relate MI to Fisher information which is relatively easier to compute and interpret with regard to neurophysiological parameters. The relationship between the two measures is not always clear and often depends on the probability distribution function that best describes the distribution of the noise. Using Stam’s inequality we show here that deviations from Gaussianity in neuronal response distribution function can result in a large overestimation of MI, even in the small noise regime. This result is especially relevant when studying neural codes represented by Poissonian neurons.

Keywords: Fisher information; Mutual information; Poissonian neurons; Stam’s inequality (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119319235
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119319235

DOI: 10.1016/j.physa.2019.123451

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().