Overview
The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the Hyper NEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the desi gn of virtual multi-component robots and morphologies and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines. This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.
Full Product Details
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: 2014 ed.
Volume: 557
Dimensions:
Width: 15.50cm
, Height: 1.60cm
, Length: 23.50cm
Weight: 5.325kg
ISBN: 9783642553363
ISBN 10: 3642553362
Pages: 261
Publication Date: 26 June 2014
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
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.” (João Luís 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. (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. (Joao Luis G. Rosa, Computing Reviews, March, 2015)
