English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Neural networks are composed of neurons and synapses, which are responsiblefor learning in a slow adaptive dynamical process. Here we experimentally showthat neurons act like independent anisotropic multiplex hubs, which relay andmute incoming signals following their input directions. Theoretically, theobserved information routing enriches the computational capabilities of neuronsby allowing, for instance, equalization among different information routes inthe network, as well as high-frequency transmission of complex time-dependentsignals constructed via several parallel routes. In addition, this kind of hubsadaptively eliminate very noisy neurons from the dynamics of the network,preventing masking of information transmission. The timescales for thesefeatures are several seconds at most, as opposed to the imprint of informationby the synaptic plasticity, a process which exceeds minutes. Results open thehorizon to the understanding of fast and adaptive learning realities in highercognitive functionalities of the brain.

Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs