Flexible Couplings

Diffusing Neuromodulators and Adaptive Robotics

Andy Philippides1, Phil Husbands1, Tom Smith2 and Michael O'Shea2
Centre for Computational Neuroscience and Robotics (CCNR)
1Department of Informatics, 2Department of Biology
University of Sussex
Falmer, Brighton, BN1 9QH, U.K.

Citation: A. Philippides, P. Husbands, T. Smith and M.l O'Shea [2005]. Artificial Life. Vol. 11, Issues 1-2, pp. 139 - 160 - Winter-Spring 2005. Special Issue on Embodied and Situated Cognition. Preprint available in pdf format.

Abstract: Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions—here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one "chemical" and one "electrical."

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Last Modified: May 27, 2005