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Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks

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journal contribution
posted on 02.08.2018 by Andrea Soltoggio, Kenneth O. Stanley, Sebastian Risi
Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both innate properties and the ability to change and learn in response to experiences in different environments and problem domains. EPANNs’ aims include autonomously creating learning systems, bootstrapping learning from scratch, recovering performance in unseen conditions, testing the computational advantages of particular neural components, and deriving hypotheses on the emergence of biological learning. Thus, EPANNs may include a large variety of different neuron types and dynamics, network architectures, plasticity rules, and other factors. While EPANNs have seen considerable progress over the last two decades, current scientific and technological advances in artificial neural networks are setting the conditions for radically new approaches and results. Exploiting the increased availability of computational resources and of simulation environments, the often challenging task of hand-designing learning neural networks could be replaced by more autonomous and creative processes. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and possible developments are presented.

History

School

  • Science

Department

  • Computer Science

Published in

Neural Networks

Volume

108

Pages

48-67

Citation

SOLTOGGIO, A., STANLEY, K.O. and RISI, S., 2018. Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks. Neural Networks, 108, pp. 48-67.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal Neural Networks and the definitive published version is available at https://doi.org/10.1016/j.neunet.2018.07.013

Acceptance date

24/07/2018

Publication date

2018-08-07

ISSN

0893-6080

Language

en

Exports