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Artificial Evolution of Plastic Neural Networks: A Few Key Concepts

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Growing Adaptive Machines

Part of the book series: Studies in Computational Intelligence ((SCI,volume 557))

Abstract

This chapter introduces a hierarchy of concepts to classify the goals and the methods used in articles that mix neuro-evolution and synaptic plasticity. We propose definitions of “behavioral robustness” and oppose it to “reward-based behavioral changes”; we then distinguish the switch between behaviors and the acquisition of new behaviors. Last, we formalize the concept of “synaptic General Learning Abilities” (sGLA) and that of “synaptic Transitive learning Abilities (sTLA)”. For each concept, we review the literature to identify the main experimental setups and the typical studies.

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Notes

  1. 1.

    We focus our discussion on classic neurons (as used in classic machine learning) and population-based models of neurons (e.g. leaky integrators) because they are the neuron models that are used by most of the community. Spiking neuron models can make use of other plasticity mechanisms (e.g. STDP) that will not be described here.

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Acknowledgments

This work was funded by the EvoNeuro project (ANR-09-EMER-005-01) and the Creadapt project (ANR-12-JS03-0009).

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Authors and Affiliations

  1. Institut des Systèmes Intelligents et de Robotique (ISIR), UMR 7222, Sorbonne Universités, UPMC Univ Paris, 06, F-75005, Paris, France

    Jean-Baptiste Mouret

  2. UMR 7222, ISIR, F-75005, Paris, France

    Paul Tonelli

Authors
  1. Jean-Baptiste Mouret
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  2. Paul Tonelli
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Corresponding author

Correspondence to Jean-Baptiste Mouret .

Editor information

Editors and Affiliations

  1. CNRS, Institut des Systèmes Complexes - Paris Île-de-France, Paris, France

    Taras Kowaliw

  2. Institute of Intelligent Systems and Robotics, CNRS UMR 7222, Université Pierre et Marie Curie, Paris, France

    Nicolas Bredeche

  3. School of Biomedical Engineering, Drexel University, Philadelphia, Pennsylvania, USA

    René Doursat

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Mouret, JB., Tonelli, P. (2014). Artificial Evolution of Plastic Neural Networks: A Few Key Concepts. In: Kowaliw, T., Bredeche, N., Doursat, R. (eds) Growing Adaptive Machines. Studies in Computational Intelligence, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55337-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-55337-0_9

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  • Print ISBN: 978-3-642-55336-3

  • Online ISBN: 978-3-642-55337-0

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