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Published in:
Artificial Neurogenesis: An Introduction and Selective Review Read chapter Read first chapter

2014 | OriginalPaper | Chapter

9. Artificial Evolution of Plastic Neural Networks: A Few Key Concepts

Authors : Jean-Baptiste Mouret, Paul Tonelli

Published in: Growing Adaptive Machines

Publisher: Springer Berlin Heidelberg

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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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previous chapter Neuro-Centric and Holocentric Approaches to the Evolution of Developmental Neural Networks
Footnotes
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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Metadata
Title
Artificial Evolution of Plastic Neural Networks: A Few Key Concepts
Authors
Jean-Baptiste Mouret
Paul Tonelli
Copyright Year
2014
Publisher
Springer Berlin Heidelberg
Book
Growing Adaptive Machines

Print ISBN: 978-3-642-55336-3

Electronic ISBN: 978-3-642-55337-0

Copyright Year: 2014

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

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