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  Self-organized chaos through polyhomeostatic optimization

Markovic, D., & Gros, C. (2010). Self-organized chaos through polyhomeostatic optimization. Physical Review Letters, 105(6): 068702. doi:10.1103/PhysRevLett.105.068702.

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 Creators:
Markovic, Dimitrije1, Author           
Gros, Claudius2, Author
Affiliations:
1Biomagnetic Center, Hans Berger Clinic for Neurology, University Hospital Jena, Friedrich-Schiller-University, Jena, 07747, Germany, ou_persistent22              
2Institute for Theorethical Physics, Goethe University, Frankfurt am Main, 60348, Germany, ou_persistent22              

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Free keywords: Artificial neural networks; Nonlinear dynamics; Chaos; Attractors; Neural plasticity
 Abstract: The goal of polyhomeostatic control is to achieve a certain target distribution of behaviors, in contrast to homeostatic regulation, which aims at stabilizing a steady-state dynamical state. We consider polyhomeostasis for individual and networks of firing-rate neurons, adapting to achieve target distributions of firing rates maximizing information entropy. We show that any finite polyhomeostatic adaption rate destroys all attractors in Hopfield-like network setups, leading to intermittently bursting behavior and self-organized chaos. The importance of polyhomeostasis to adapting behavior in general is discussed.

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Language(s): eng - English
 Dates: 2010
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1103/PhysRevLett.105.068702
 Degree: -

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Title: Physical Review Letters
Source Genre: Journal
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Publ. Info: Woodbury, N.Y., etc. : American Physical Society.
Pages: - Volume / Issue: 105 (6) Sequence Number: 068702 Start / End Page: - Identifier: ISSN: 0031-9007
CoNE: https://pure.mpg.de/cone/journals/resource/954925433406_1