Neural Coding as a Statistical Testing Problem

Guilherme Ost (1) , Patricia Reynaud-Bouret (2)

1 UNIRIO - Universidade Federal do Estado do Rio de Janeiro
2 LJAD - Laboratoire Jean Alexandre Dieudonné

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.

Domaines: Mathématiques [math]; Sciences cognitives; Sciences du Vivant [q-bio]; Statistiques [stat]

Guilherme Ost, Patricia Reynaud-Bouret. Neural Coding as a Statistical Testing Problem. 2022. ⟨hal-03773258⟩