Spatially Constrained Networks and the Evolution of Modular Control Systems
Lecture notes in computer sciencePublished 1 January 2006
Peter Fine, Ezequiel A. Di Paolo, Andrew Philippides
Citations13
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper investigates the relationship between spatially embedded neural network models and modularity and concludes that a bias towards modularity is perhaps not always a desirable property for a control system paradigm to possess.
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.
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