A Single Model Explains both Visual and Auditory Precortical Coding

TL;DR

This paper introduces a unified model based on information preservation, wiring minimization, and response equalization that explains both visual and auditory precortical coding, capturing known neural features across modalities.

Contribution

The study proposes a novel sparse PCA model that accounts for visual and auditory precortical encoding, demonstrating a unifying principle of efficient coding across sensory systems.

Findings

Model captures retinal features like center-surround fields and color opponency.

Model learns auditory receptive fields similar to gammatone filters.

Efficient coding may unify precortical sensory encoding principles.

Abstract

Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate. We present a model of retinal coding that is based on three constraints: information preservation, minimization of the neural wiring, and response equalization. The resulting novel version of sparse principal components analysis successfully captures a number of known characteristics of the retinal coding system, such as center-surround receptive fields, color opponency channels, and spatiotemporal responses that correspond to magnocellular and parvocellular pathways. Furthermore, when trained on auditory data, the same model learns receptive fields well fit by gammatone filters, commonly used to model precortical auditory coding. This suggests that efficient coding may be a unifying principle of precortical encoding across modalities.