SORN: a self-organizing recurrent neural network

Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effe
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success. Keywords: synaptic plasticity, intrinsic plasticity, recurrent neural networks, reservoir computing, time series prediction
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Metadaten
Author:Andreea Lazar, Gordon Pipa, Jochen Triesch
URN:urn:nbn:de:hebis:30-83842
Document Type:Article
Language:English
Date of Publication (online):2010/10/26
Year of first Publication:2009
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/10/26
Tag:intrinsic plasticity ; recurrent neural networks ; reservoir computing ; synaptic plasticity ; time series prediction
Note:
Copyright © 2009 Lazar, Pipa and Triesch. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Source:Frontiers in computational neuroscience 2009, 3:23 ; doi: 10.3389/neuro.10.023.2009
HeBIS PPN:229856195
Institutes:Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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