Adaptive whitening in neural populations with gain-modulating interneurons

TL;DR

This paper introduces a biologically plausible neural circuit model that adaptively whitens sensory responses by modulating neuron gains, enhancing robustness and enabling local whitening in convolutional populations.

Contribution

It proposes a novel gain-modulation based adaptive whitening algorithm and demonstrates its effectiveness and robustness in neural network models.

Findings

Gain modulation improves robustness to ill-conditioned inputs.

The model achieves local whitening in convolutional neural populations.

The approach offers a biologically plausible alternative to synaptic modification.

Abstract

Statistical whitening transformations play a fundamental role in many computational systems, and may also play an important role in biological sensory systems. Existing neural circuit models of adaptive whitening operate by modifying synaptic interactions; however, such modifications would seem both too slow and insufficiently reversible. Motivated by the extensive neuroscience literature on gain modulation, we propose an alternative model that adaptively whitens its responses by modulating the gains of individual neurons. Starting from a novel whitening objective, we derive an online algorithm that whitens its outputs by adjusting the marginal variances of an overcomplete set of projections. We map the algorithm onto a recurrent neural network with fixed synaptic weights and gain-modulating interneurons. We demonstrate numerically that sign-constraining the gains improves robustness of the network to ill-conditioned inputs, and a generalization of the circuit achieves a form of local whitening in convolutional populations, such as those found throughout the visual or auditory systems.