Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks
Author:   Taras Kowaliw ,  Nicolas Bredeche ,  René Doursat
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   Softcover reprint of the original 1st ed. 2014
Volume:   557
ISBN:  

9783662509449


Pages:   261
Publication Date:   17 September 2016
Format:   Paperback
Availability:   In Print   Availability explained
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Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks


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Author:   Taras Kowaliw ,  Nicolas Bredeche ,  René Doursat
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 2014
Volume:   557
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   4.161kg
ISBN:  

9783662509449


ISBN 10:   366250944
Pages:   261
Publication Date:   17 September 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Artificial neurogenesis: An introduction and selective review.- A Brief Introduction to Probabilistic Machine Learning and its Relation to Neuroscience.- Evolving culture versus local minima.- Learning sparse features with an auto-associator.- HyperNEAT: the first five years.- Using the GReaNs (Genetic Regulatory evolving artificial Networks) platform for signal processing, animat control, and artificial multicellular development.- Constructing complex systems via activity-driven unsupervised Hebbian self-organization.- Neuro-centric and holocentric approaches to the evolution of developmental neural networks.- Artificial evolution of plastic neural networks: A few key concepts.

Reviews

This book considers the importance of biological plausibility in artificial neural networks (ANNs). ... the book is recommended for those who want to know more about ANNs and their biologically inspired architectures, especially those related to learning. (Joao Luis G. Rosa, Computing Reviews, March, 2015)


“This book considers the importance of biological plausibility in artificial neural networks (ANNs). … the book is recommended for those who want to know more about ANNs and their biologically inspired architectures, especially those related to learning.” (João Luís G. Rosa, Computing Reviews, March, 2015)


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