Volume 118, Number 4, May 2017
|Number of page(s)||7|
|Section||Condensed Matter: Structural, Mechanical and Thermal Properties|
|Published online||20 July 2017|
Fast reversible learning based on neurons functioning as anisotropic multiplex hubs
1 Department of Physics, Bar-Ilan University - Ramat-Gan 52900, Israel
2 Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University - Ramat-Gan 52900, Israel
3 Department of Biochemistry and Molecular Biology, Tel Aviv University - Tel Aviv, Israel
4 Sagol School of Neuroscience, Tel Aviv University - Tel Aviv, Israel
Received: 9 May 2017
Accepted: 2 July 2017
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 brain's functionalities.
PACS: 64.60.aq – Networks / 87.18.Sn – Neural networks and synaptic communication / 87.18.-h – Biological complexity
© EPLA, 2017
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