Homeostatic plasticity improves continuous-time recurrent neural networks as a behavioural substrate

Williams, Hywel (2005) Homeostatic plasticity improves continuous-time recurrent neural networks as a behavioural substrate. In: 3rd International Symposium on Adaptive Motion in Animals and Machines, 2005-09-25 - 2005-09-30, Technische Universität Ilmenau.

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Abstract

Homeostatic plasticity is applied to continuous-time recurrent neural networks. It is observed to make networks more sensitive, improve signal propagation and increase the likelihood of autonomous oscillations. Evolutionary experiments with a simulated robot show that in some circumstances homeostatic plasticity improves evolvability of good control networks, but in others it makes good controllers less easy to evolve.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences
Depositing User:	Vishal Gautam
Date Deposited:	14 Jul 2011 08:04
Last Modified:	16 Feb 2023 16:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22205
DOI:

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