Embodied neuromechanical chaos through homeostatic regulation

Shim, Yoonsik and Husbands, Phil (2019) Embodied neuromechanical chaos through homeostatic regulation. Chaos, 29 (3). a033123 1- 17. ISSN 1054-1500

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In this paper, we present detailed analyses of the dynamics of a number of embodied neuromechanical systems of a class that has been shown to efficiently exploit chaos in the development and learning of motor behaviors for bodies of arbitrary morphology. This class of systems has been successfully used in robotics, as well as to model biological systems. At the heart of these systems are neural central pattern generating (CPG) units connected to actuators which return proprioceptive information via an adaptive homeostatic mechanism. Detailed dynamical analyses of example systems, using high resolution largest Lyapunov exponent maps, demonstrate the existence of chaotic regimes within a particular region of parameter space, as well as the striking similarity of the maps for systems of varying size. Thanks to the homeostatic sensory mechanisms, any single CPG “views” the whole of the rest of the system as if it was another CPG in a two coupled system, allowing a scale invariant conceptualization of such embodied neuromechanical systems. The analysis reveals chaos at all levels of the systems; the entire brain-body-environment system exhibits chaotic dynamics which can be exploited to power an exploration of possible motor behaviors. The crucial influence of the adaptive homeostatic mechanisms on the system dynamics is examined in detail, revealing chaotic behavior characterized by mixed mode oscillations (MMOs). An analysis of the mechanism of the MMO concludes that they stems from dynamic Hopf bifurcation, where a number of slow variables act as “moving” bifurcation parameters for the remaining part of the system.

Item Type: Article
Keywords: chaotic neuromechanical dynamics, motor behavior, embodied dynamics, homeostatic regulation, mixed mode oscillations, dynamic Hopf bifurcation, embodied AI, computational neuroscience, robotics
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
Subjects: Q Science > QA Mathematics > QA0299 Analysis. Including analytical methods connected with physical problems
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
Depositing User: Phil Husbands
Date Deposited: 06 Mar 2019 14:01
Last Modified: 23 Nov 2020 16:30
URI: http://sro.sussex.ac.uk/id/eprint/82317

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