Synchronization and Redundancy: Implications for Robustness of Neural Learning and Decision Making

TL;DR

The paper investigates how the brain uses synchronization and redundancy among neural systems to mitigate errors and improve robustness in learning and decision-making processes, supported by theoretical analysis and simulations.

Contribution

It introduces a quantitative framework linking network topology, synchronization, and noise reduction in neural decision-making models, highlighting the role of the Laplacian spectrum.

Findings

Synchronization strength reduces error and uncertainty.

Network topology influences noise mitigation.

Theoretical bounds align with empirical data.

Abstract

Learning and decision making in the brain are key processes critical to survival, and yet are processes implemented by non-ideal biological building blocks which can impose significant error. We explore quantitatively how the brain might cope with this inherent source of error by taking advantage of two ubiquitous mechanisms, redundancy and synchronization. In particular we consider a neural process whose goal is to learn a decision function by implementing a nonlinear gradient dynamics. The dynamics, however, are assumed to be corrupted by perturbations modeling the error which might be incurred due to limitations of the biology, intrinsic neuronal noise, and imperfect measurements. We show that error, and the associated uncertainty surrounding a learned solution, can be controlled in large part by trading off synchronization strength among multiple redundant neural systems against the noise amplitude. The impact of the coupling between such redundant systems is quantified by the spectrum of the network Laplacian, and we discuss the role of network topology in synchronization and in reducing the effect of noise. A range of situations in which the mechanisms we model arise in brain science are discussed, and we draw attention to experimental evidence suggesting that cortical circuits capable of implementing the computations of interest here can be found on several scales. Finally, simulations comparing theoretical bounds to the relevant empirical quantities show that the theoretical estimates we derive can be tight.