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

Hywel Williams, Jason Noble

Research output: Contribution to conferenceOther

19 Citations (Scopus)


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.
Original languageEnglish
Number of pages8
Publication statusPublished - 2005
Event6th International Workshop on Information Processing in Cells and Tissues - York, United Kingdom
Duration: 1 Jan 2005 → …


Conference6th International Workshop on Information Processing in Cells and Tissues
Country/TerritoryUnited Kingdom
Period1/01/05 → …

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