NDLI: Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia

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Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia

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Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia

Content Provider	ACM Digital Library
Author	Doncieux, Stéphane Mouret, Jean-Baptiste Girard, Benoît
Abstract	Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the "toolbox" used by neuroscientists to create their models, this paper argues two main points: (1) neural maps (spatially-organized identical neurons) should be the building blocks to evolve neural networks able to perform cognitive functions and (2) well-identified modules of the brain for which there exists computational neuroscience models provide well-defined benchmarks for neuro-evolution. To support these claims, a method to evolve networks of neural maps is introduced then applied to evolve neural networks with a similar functionality to basal ganglia in animals (i.e. action selection). Results show that: (1) the map-based encoding easily achieves this task while a direct encoding never solves it; (2) this encoding is independent of the size of maps and can therefore be used to evolve large and brain-like neural networks; (3) the failure of direct encoding to solve the task validates the relevance of action selection as a benchmark for neuro-evolution.
Starting Page	587
Ending Page	594
Page Count	8
File Format	PDF
ISBN	9781450300728
DOI	10.1145/1830483.1830592
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Basal ganglia Neuro-evolution Computational neuroscience Neural networks Evolutionary algorithms
Content Type	Text
Resource Type	Article

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