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.
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.

Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks
261
Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks
261Paperback(Softcover reprint of the original 1st ed. 2014)
Product Details
ISBN-13: | 9783662509449 |
---|---|
Publisher: | Springer Berlin Heidelberg |
Publication date: | 06/19/2015 |
Series: | Studies in Computational Intelligence , #557 |
Edition description: | Softcover reprint of the original 1st ed. 2014 |
Pages: | 261 |
Product dimensions: | 6.10(w) x 9.25(h) x 0.02(d) |