Entropy factor for randomness quantification in neuronal data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F17%3A00479627" target="_blank" >RIV/67985823:_____/17:00479627 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.neunet.2017.07.016" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2017.07.016</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neunet.2017.07.016" target="_blank" >10.1016/j.neunet.2017.07.016</a>

Result language

angličtina

Original language name

Entropy factor for randomness quantification in neuronal data

Original language description

A novel measure of neural spike train randomness, an entropy factor, is proposed. It is based on the Shannon entropy of the number of spikes in a time window and can be seen as an analogy to the Fano factor. Theoretical properties of the new measure are studied for equilibrium renewal processes and further illustrated on gamma and inverse Gaussian probability distributions of interspike intervals. Finally, the entropy factor is evaluated from the experimental records of spontaneous activity in macaque primary visual cortex and compared to its theoretical behavior deduced for the renewal process models. Both theoretical and experimental results show substantial differences between the Fano and entropy factors. Rather paradoxically, an increase in the variability of spike count is often accompanied by an increase of its predictability, as evidenced by the entropy factor.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Project

<a href="/en/project/GA15-08066S" target="_blank" >GA15-08066S: Efficiency of information transfer and the role of energetic constraints in neuronal systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Networks

ISSN

0893-6080

e-ISSN

—

Volume of the periodical

95

Issue of the periodical within the volume

Nov 17

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

57-65

UT code for WoS article

000411895600006

EID of the result in the Scopus database

2-s2.0-85028766949