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.
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