No subject

For years, researchers have used the theoretical tools of engineering to
understand neural systems, but much of this work has been conducted in
relative isolation. In Neural Engineering, Chris Eliasmith and Charles
Anderson provide a synthesis of the disparate approaches current in
computational neuroscience, incorporating ideas from neural coding, neural
computation, physiology, communications theory, control theory, dynamics,
and probability theory. This synthesis, they argue, enables novel
theoretical and practical insights into the functioning of neural systems.
Such insights are pertinent to experimental and computational
neuroscientists and to engineers, physicists, and computer scientists
interested in how their quantitative tools relate to the brain.

The authors present three principles of neural engineering based on the
representation of signals by neural ensembles, transformations of these
representations through neuronal coupling weights, and the integration of
control theory and neural dynamics. Through detailed examples and in-depth
discussion, they make the case that these guiding principles constitute a
useful theory for generating large-scale models of neurobiological function.
A software package written in MatLab for use with their methodology, as well
as examples, course notes, exercises, documentation, and other material, are
available on the Web.

"In this brilliant volume, Eliasmith and Anderson present a novel
theoretical framework for understanding the functional organization
and operation of nervous systems, from the cellular level to the
level of large-scale networks" John P. Miller, Center for Computational
Biology, University of Montana

"This book represents a significant advance in computational
neuroscience. Eliasmith and Anderson have developed an elegant
framework for understanding representation, computation, and
dynamics in neurobiological systems. The book is beautifully
written, and it should be accessible to a wide variety of
readers." Bruno A. Olshausen, Center for Neuroscience, University
of California, Davis

"From principle component analysis to Kalman filters, information
theory to attractor dynamics, this book is a brilliant introduction
to the mathematical and engineering methods used to analyze neural
function." Leif Finkel, Neuroengineering Research Laboratories,
University of Pennsylvania

http://mitpress.mit.edu/catalog/item/default.asp?sid=29DD45EE-EFE7-4C7F-BCF3
-40C91D6B2635&ttype=2&tid=9538