Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks

Williams, Hywel and Noble, Jason (2005) Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks. In: 6th International Workshop on Information Processing in Cells and Tissues, 2005-01-01.

Full text not available from this repository.

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.

Item Type:	Conference or Workshop Item (Other)
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	EPrints Services
Date Deposited:	01 Oct 2010 13:41
Last Modified:	06 Feb 2025 01:05
URI:	https://ueaeprints.uea.ac.uk/id/eprint/3108
DOI:	10.1016/j.biosystems.2006.09.020

Actions (login required)

View Item