Information-theoretic interpretation of tuning curves for multiple motion directions

TL;DR

This paper introduces an information-maximization approach to determine optimal tuning curves for sensory neurons, revealing diverse shapes that may represent an efficient encoding strategy for multiple motion directions in the visual cortex.

Contribution

It presents a novel computational method for optimizing tuning curves, demonstrating that diverse neuronal response shapes can be optimal for encoding complex visual motion information.

Findings

Diverse tuning curve shapes resemble those observed in the visual cortex.

Heterogeneous tuning curves may be an optimal population coding strategy.

The method efficiently computes optimal neural response profiles.

Abstract

We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble what have been found in the visual cortex. Our result suggests that the diversity or heterogeneity of tuning curve shapes as observed in neurophysiological experiment might actually constitute an optimal population representation of visual motions with multiple components.