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• W2313002880 abstract "Event Abstract Back to Event Neural implementation of chaos control improves both speed and reliability Christian Bick1, 2, 3, Christoph Kolodziejski1, 2, 4* and Marc Timme1, 2, 4 1 Max Planck Institute for Dynamics and Self-Organization, Network Dynamics Group, Germany 2 Bernstein Center for Computational Neuroscience Göttingen, Germany 3 Georg-August-Universität Göttingen, Mathematisches Institut, Germany 4 Georg-August-Universität Göttingen, Institute for Physics – Nonlinear Dynamics, Germany Chaos control has applications in many fields [1], for example, it has been demonstrated in neural circuits [2] and we have recently used it in order to control robot behavior [3]. One way to achieve chaos control, i.e., rendering unstable fixed points stable, is by adding control perturbations [4, 5]. In a neural implementation of chaos control the application of the control perturbations are restrained by the underlying neural substrate. Hence, the neural implementation itself poses challenges. At the same time, the chaos control method itself is subject to a serious limitation. Convergence speed of such a mechanism becomes very slow when stabilizing more and more periodic points. This has immediate consequences. Take for example an organism, natural or artificial, with a neurally implemented chaos control mechanism where a specific movement is linked to the period of some periodic orbit. For the organism to react to changing environments, new periodic orbits with different periods have to be stabilized as fast as possible resulting in corresponding reactive movements. Hence, reaction time is linked to the convergence time of the stabilization mechanism. We show that a delay, inevitable due to the neural implementation, improves not only convergence characteristics like speed and reliability but, interestingly, also extends the accessibility of periodic orbits in terms of stabilization. Chaos control methods usually are parameter-dependent and the parameter influences the speed of convergence. A priori, however, the optimal parameter value is unknown. We systematically study the performance of different adaptation schemes [6], including heuristical [3] methods, that can be used to find the optimal parameter values dynamically. The result is an adaptive, neurally implemented chaos control algorithm that may have wide applications in the dynamics of neural systems. References [1] E. Schöll, “Handbook of Chaos Control,” Wiley-VCH, 2007. [2] M. I. Rabinovich and H. D. I. Abarbanel, Neuroscience 87 (1), 1998. [3] S. Steingrube, M. Timme, F. Wörgötter, and P. Manoonpong, Nature Physics 6 (3), 2010. [4] M. de Sousa Vieira and A. J. Lichtenberg, Physical Review E 54 (2), 1996. [5] P. Schmelcher and F. K. Diakonos, Physical Review E 57 (3), 1998. [6] A. L. Fradkov and A. Pogromsky, “Introduction to control of oscillations and chaos,” World Scientific, 1998. Keywords: dynamical systems, networks, Neurons Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: neurons, networks and dynamical systems (please use neurons, networks and dynamical systems as keywords) Citation: Bick C, Kolodziejski C and Timme M (2011). Neural implementation of chaos control improves both speed and reliability. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00047 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Dr. Christoph Kolodziejski, Max Planck Institute for Dynamics and Self-Organization, Network Dynamics Group, Göttingen, Germany, kolo@nld.ds.mpg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Christian Bick Christoph Kolodziejski Marc Timme Google Christian Bick Christoph Kolodziejski Marc Timme Google Scholar Christian Bick Christoph Kolodziejski Marc Timme PubMed Christian Bick Christoph Kolodziejski Marc Timme Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page." @default.
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• W2313002880 title "Neural implementation of chaos control improves both speed and reliability" @default.
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