Efficient representation as a design principle for neural coding and computation

TL;DR

This paper explores how the brain may optimize sensory representations for future prediction, using fly vision as a model, and presents experimental evidence supporting an information-theoretic principle of neural coding.

Contribution

It proposes a novel information-theoretic principle for neural coding that emphasizes maximizing predictive information about future sensory inputs given past inputs.

Findings

Preliminary experiments in fly vision support the proposed optimization principle.

Theoretical framework links neural coding to maximizing predictive information.

Results suggest the brain's representations are tuned for future prediction.

Abstract

Does the brain construct an efficient representation of the sensory world? We review progress on this question, focusing on a series of experiments in the last decade which use fly vision as a model system in which theory and experiment can confront each other. Although the idea of efficient representation has been productive, clearly it is incomplete since it doesn't tell us which bits of sensory information are most valuable to the organism. We suggest that an organism which maximizes the (biologically meaningful) adaptive value of its actions given fixed resources should have internal representations of the outside world that are optimal in a very specific information theoretic sense: they maximize the information about the future of sensory inputs at a fixed value of the information about their past. This principle contains as special cases computations which the brain seems to carry out, and it should be possible to test this optimization directly. We return to the fly visual system and report the results of preliminary experiments that are in encouraging agreement with theory.