Connectionists: Fwd: New book: Growing Adaptive Machines

[Nicolas Bredeche]

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Dear all,

Some of you will be interested in our recent book, Growing Adaptive
Machines: Combining Development and Learning in Artificial Neural Networks.

The topic is the generation of low-level neural network architectures using
bio-inspired models and simulations. 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 book includes two broad reviews, summaries of research portfolios from
established researchers, and new research contributions. We believe it will
form a valuable reference for advanced students and practitioners. See
below for the table of contents.

http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-55336-3

http://www.amazon.com/Growing-Adaptive-Machines-Computational-Intelligence/dp/3642553362

Best regards,

Taras Kowaliw, Nicolas Bredeche, René Doursat (Editors)

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Table of Contents

Artificial Neurogenesis: An Introduction and Selective Review.
T. Kowaliw, N. Bredeche, S. Chevallier and R. Doursat

A Brief Introduction to Probabilistic Machine Learning and Its Relation to
Neuroscience.
T. Trappenberg

Evolving Culture Versus Local Minima.
Y. Bengio

Learning Sparse Features with an Auto-Associator.
S. Rebecchi, H. Paugam-Moisy and M. Sebag

HyperNEAT: The First Five Years.
D. D’Ambrosio, J. Gauci and K. Stanley

Using the Genetic Regulatory Evolving Artificial Networks (GReaNs) Platform
for Signal Processing, Animat Control, and Artificial Multicellular
Development.
B. Wróbel and M. Joachimczak

Constructing Complex Systems Via Activity-Driven Unsupervised Hebbian
Self-Organization
J. Bednar

Neuro-Centric and Holocentric Approaches to the Evolution of Developmental
Neural Networks.
J. Miller

Artificial Evolution of Plastic Neural Networks: A Few Key Concepts.
J.-B. Mouret and P. Tonelli
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