Spatially constrained networks and the evolution of modular control systems

Authors:
Peter Fine;Ezequiel Di Paolo;Andrew Philippides
Affiliations:
Centre for Computational Neuroscience and Robotics (CCNR), Department of Informatics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics (CCNR), Department of Informatics, University of Sussex, Brighton, UK;Centre for Computational Neuroscience and Robotics (CCNR), Department of Informatics, University of Sussex, Brighton, UK
Venue:
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Year:
2006

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Abstract

This paper investigates the relationship between spatially embedded neural network models and modularity It is hypothesised that spatial constraints lead to a greater chance of evolving modular structures Firstly, this is tested in a minimally modular task/controller scenario Spatial networks were shown to possess the ability to generate modular controllers which were not found in standard, non-spatial forms of network connectivity We then apply this insight to examine the effect of varying degrees of spatial constraint on the modularity of a controller operating in a more complex, situated and embodied simulated environment We conclude that a bias towards modularity is perhaps not always a desirable property for a control system paradigm to possess.