Synchronization and Noise: A Mechanism for Regularization in Neural Systems

TL;DR

This paper proposes that the combination of synchronization and noise in neural systems acts as a regularization mechanism, helping the brain learn and reason under uncertainty, supported by theoretical, computational, and experimental evidence.

Contribution

It introduces a novel mechanism where synchronization modulates noise-induced regularization, applicable to associative learning and sensory coding, aligning with visual cortex data.

Findings

Noise induces regularization in neural systems.

Synchronization modulates the degree of regularization over time.

The model's behavior matches experimental data from visual cortex.

Abstract

To learn and reason in the presence of uncertainty, the brain must be capable of imposing some form of regularization. Here we suggest, through theoretical and computational arguments, that the combination of noise with synchronization provides a plausible mechanism for regularization in the nervous system. The functional role of regularization is considered in a general context in which coupled computational systems receive inputs corrupted by correlated noise. Noise on the inputs is shown to impose regularization, and when synchronization upstream induces time-varying correlations across noise variables, the degree of regularization can be calibrated over time. The proposed mechanism is explored first in the context of a simple associative learning problem, and then in the context of a hierarchical sensory coding task. The resulting qualitative behavior coincides with experimental data from visual cortex.