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Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks

Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks
Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks
Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems.
Continuous-time recurrent neural network, Homeostatic plasticity, Signal propagation
252-259
Williams, Hywel
805c7100-2f6e-410c-9418-ed0cad29bac7
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764
Williams, Hywel
805c7100-2f6e-410c-9418-ed0cad29bac7
Noble, Jason
440f07ba-dbb8-4d66-b969-36cde4e3b764

Williams, Hywel and Noble, Jason (2007) Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks. Biosystems, 87 (2-3), 252-259.

Record type: Article

Abstract

Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems.

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Published date: February 2007
Keywords: Continuous-time recurrent neural network, Homeostatic plasticity, Signal propagation
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 263481
URI: https://eprints.soton.ac.uk/id/eprint/263481
PURE UUID: 5403df31-0176-4aa8-b7cf-ae3dc630df26

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Date deposited: 18 Feb 2007
Last modified: 16 Sep 2019 18:43

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Contributors

Author: Hywel Williams
Author: Jason Noble

University divisions

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