Neural Coding as a Statistical Testing Problem

TL;DR

This paper models neural coding as a statistical testing problem to compare the discrimination capabilities of place and grid cells, revealing how their different properties affect stimulus discrimination times.

Contribution

It introduces a novel testing perspective to analyze neural coding, specifically comparing place and grid cells' discrimination abilities based on stimulus distance.

Findings

Place cells' discrimination time decreases with stimulus distance.

Grid cells can discriminate stimuli that are closer together.

Place cells may complement grid cells in stimulus discrimination.

Abstract

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.