Spatially Constrained Networks and the Evolution of Modular Control Systems

  • Peter Fine
  • Ezequiel Di Paolo
  • Andrew Philippides
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


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.


Spatial Constraint Plexus Model Modular Architecture Spatial Network Evolutionary Robotic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Fine
    • 1
  • Ezequiel Di Paolo
    • 1
  • Andrew Philippides
    • 1
  1. 1.Centre for Computational Neuroscience and Robotics (CCNR), Department of InformaticsUniversity of SussexBrightonUK

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