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: Proceedings of 3rd International Symposium on Adaptive Motion in Animals and Machines (AMAM 2005), 2005-09-25 - 2005-09-30.

Full text not available from this repository.

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
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 14 Jul 2011 08:04
Last Modified: 08 Jul 2019 23:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/22205
DOI:

Actions (login required)

View Item View Item