Adaptive whitening with fast gain modulation and slow synaptic plasticity

TL;DR

This paper presents a multi-timescale model that unifies synaptic plasticity and gain modulation to achieve adaptive whitening of neural responses, improving sensory adaptation in changing environments.

Contribution

It introduces a novel multi-timescale whitening objective that combines synaptic and gain adjustments, providing a unified mechanistic framework for adaptive sensory processing.

Findings

Synapses learn optimal configurations for whitening over long timescales.

Gain modulation enables rapid adaptation to current statistical context.

Model performs well on synthetic and natural datasets.

Abstract

Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation.