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  Intrinsic adaptation in autonomous recurrent neural networks

Markovic, D., & Gros, C. (2012). Intrinsic adaptation in autonomous recurrent neural networks. Neural Computation, 24(2), 523-540. doi:10.1162/NECO_a_00232.

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 Creators:
Markovic, Dimitrije1, Author              
Gros, Claudius1, Author
Affiliations:
1Institute for Theoretical Physics, Goethe University, Frankfurt, Germany, ou_persistent22              

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Free keywords: Artificial neural networks; Nonlinear dynamcis; Chaos; Attractors; Neural plasticity
 Abstract: A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depend crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting, or chaotic activity patterns. We study the influence of nonsynaptic plasticity on the default dynamical state of recurrent neural networks. The nonsynaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes: a regular synchronized, an overall chaotic, and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interceded by chaotic bursts that respond sensitively to input signals. We discuss these findings in the context of self-organized information processing and critical brain dynamics.

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Language(s): eng - English
 Dates: 2011-12-292012-02
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1162/NECO_a_00232
PMID: 22091667
Other: Epub 2011
 Degree: -

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Title: Neural Computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 24 (2) Sequence Number: - Start / End Page: 523 - 540 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591