Information-Theoretic Tools to Understand Distributed Source Coding in Neuroscience

Abstract

This paper brings together topics of two of Berger’s main contributions to information theory: distributed source coding, and living information theory. Our goal is to understand which information theory techniques can be helpful in understanding a distributed source coding strategy used by the natural world. Towards this goal, we study the example of the encoding of location of an animal by grid cells in its brain. We use information measures of partial information decomposition (PID) to assess the unique, redundant, and synergistic information carried by multiple grid cells, first for simulated grid cells utilizing known encodings, and subsequently for data from real grid cells. In all cases, we make simplifying assumptions so we can assess the consistency of specific PID definitions with intuition. Our results suggest that the measure of PID proposed by Bertschinger et al. (Entropy, 2014) provides intuitive insights on distributed source coding by grid cells, and can be used for subsequent studies for understanding grid-cell encoding as well as broadly in neuroscience.