Coordinate invariance as a fundamental constraint on the form of stimulus-specific information measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F18%3A00489787" target="_blank" >RIV/67985823:_____/18:00489787 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s00422-017-0729-7" target="_blank" >http://dx.doi.org/10.1007/s00422-017-0729-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00422-017-0729-7" target="_blank" >10.1007/s00422-017-0729-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Coordinate invariance as a fundamental constraint on the form of stimulus-specific information measures

Popis výsledku v původním jazyce

The value of Shannon's mutual information is commonly used to describe the total amount of information that the neural code transfers between the ensemble of stimuli and the ensemble of neural responses. In addition, it is often desirable to know which features of the stimulus or response are most informative. The literature offers several different decompositions of the mutual information into its stimulus or response-specific components, such as the specific surprise or the uncertainty reduction, but the number of mutually distinct measures is in fact infinite. We resolve this ambiguity by requiring the specific information measures to be invariant under invertible coordinate transformations of the stimulus and the response ensembles. We prove that the Kullback-Leibler divergence is then the only suitable measure of the specific information. On a more general level, we discuss the necessity and the fundamental aspects of the coordinate invariance as a selection principle. We believe that our results will encourage further research into invariant statistical methods for the analysis of neural coding.

Název v anglickém jazyce

Coordinate invariance as a fundamental constraint on the form of stimulus-specific information measures

Popis výsledku anglicky

The value of Shannon's mutual information is commonly used to describe the total amount of information that the neural code transfers between the ensemble of stimuli and the ensemble of neural responses. In addition, it is often desirable to know which features of the stimulus or response are most informative. The literature offers several different decompositions of the mutual information into its stimulus or response-specific components, such as the specific surprise or the uncertainty reduction, but the number of mutually distinct measures is in fact infinite. We resolve this ambiguity by requiring the specific information measures to be invariant under invertible coordinate transformations of the stimulus and the response ensembles. We prove that the Kullback-Leibler divergence is then the only suitable measure of the specific information. On a more general level, we discuss the necessity and the fundamental aspects of the coordinate invariance as a selection principle. We believe that our results will encourage further research into invariant statistical methods for the analysis of neural coding.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA17-06943S" target="_blank" >GA17-06943S: Přesnost neuronálního kódování a její adaptace na statistiku stimulu</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Biological Cybernetics

ISSN

0340-1200

e-ISSN

—

Svazek periodika

112

Číslo periodika v rámci svazku

1-2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

11

Strana od-do

13-23

Kód UT WoS článku

000430460400003

EID výsledku v databázi Scopus

2-s2.0-85028607049