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Searching for Emergent Representations in Evolved Dynamical Systems

  • Thomas Hope
  • Ivilin Stoianov
  • Marco Zorzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)

Abstract

This paper reports an experiment in which artificial foraging agents with dynamic, recurrent neural network architectures, are "evolved" within a simulated ecosystem. The resultant agents can compare different food values to "go for more," and display similar comparison performance to that found in biological subjects. We propose and apply a novel methodology for analysing these networks, seeking to recover their quantity representations within an Approximationist framework. We focus on Localist representation, seeking to interpret single units as conveying representative information through their average activities. One unit is identified that passes our "representation test", representing quantity by inverse accumulation.

Keywords

Food Group Hide Unit Food Quantity Numerical Distance Functional Decomposition 
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

  • Thomas Hope
    • 1
  • Ivilin Stoianov
    • 1
  • Marco Zorzi
    • 1
  1. 1.Computational Cognitive Neuroscience LabUniversity of PadovaPadovaItaly

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