Neural Coding as a Statistical Testing Problem

Creators: Ost, Guilherme; Reynaud-Bouret, Patricia

Others:: Universidade Federal do Estado do Rio de Janeiro (UNIRIO); Université Côte D'Azur, CNRS, LJAD (France); interdisciplinary Institute for Modeling in Neuroscience and Cognition (NeuroMod)FAPESP project Research, Innovation and Dis- semination Center for Neuromathematics (grant 2013/07699-0)FAPERJ (grants E-26/201.397/2021 and E-26/211.343/2019)UAR 839 CNRS-Sorbonne Université; ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019); ANR-19-CE40-0024,ChaMaNe,Enjeux mathématiques issus des neurosciences(2019); ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015); ANR-10-LABX-0059,CARMIN,Centers of Hosting and International Mathematical Encounters(2010)

Description

We take the testing perspective to understand what the minimal discrimination time between two stimuli is for different types of rate coding neurons. Our main goal is to describe the testing abilities of two different encoding systems: place cells and grid cells. In particular, we show, through the notion of adaptation, that a fixed place cell system can have a minimum discrimination time that decreases when the stimuli are further away. This could be a considerable advantage for the place cell system that could complement the grid cell system, which is able to discriminate stimuli that are much closer than place cells.

Neural Coding as a Statistical Testing Problem

Description

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