# Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs

arXiv: https://arxiv.org/abs/1707.06501 · HTML: https://mlsanity.com/paper/1707.06501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [Brain inspired neuronal silencing mechanism to enable reliable sequence identification](https://mlsanity.com/paper/2203.13028/) (2022)

## Abstract (arXiv)

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.