Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks


Watson, Richard A., Mills, Rob and Buckley, C. L. (2011) Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks. Adaptive Behavior, 19, (4), 227-249.

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Description/Abstract

The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks.

Item Type: Article
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Keywords: Hopfield networks, associative learning, dynamical systems, adaptive networks, constraint optimisation, modularity, nearly-decomposable systems, coarse-graining, self-organisation, canalisation.
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
ePrint ID: 272116
Date Deposited: 24 Mar 2011 10:37
Last Modified: 27 Mar 2014 20:17
Contact Email Address: raw@ecs.soton.ac.uk
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/272116

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