Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs

TL;DR

This paper reveals that neurons function as anisotropic multiplex hubs, enabling fast, adaptive information routing and noise elimination, which enhances neural computation and learning within seconds, contrasting with slower synaptic plasticity.

Contribution

It introduces the concept of neurons acting as anisotropic multiplex hubs, demonstrating their role in rapid, adaptive information routing and noise suppression in neural networks.

Findings

Neurons act as independent anisotropic multiplex hubs.

Neurons enable high-frequency transmission of complex signals.

Neurons adaptively eliminate noisy inputs.

Abstract

Neural networks are composed of neurons and synapses, which are responsible for learning in a slow adaptive dynamical process. Here we experimentally show that neurons act like independent anisotropic multiplex hubs, which relay and mute incoming signals following their input directions. Theoretically, the observed information routing enriches the computational capabilities of neurons by allowing, for instance, equalization among different information routes in the network, as well as high-frequency transmission of complex time-dependent signals constructed via several parallel routes. In addition, this kind of hubs adaptively eliminate very noisy neurons from the dynamics of the network, preventing masking of information transmission. The timescales for these features are several seconds at most, as opposed to the imprint of information by the synaptic plasticity, a process which exceeds minutes. Results open the horizon to the understanding of fast and adaptive learning realities in higher cognitive functionalities of the brain.