Item description for Theory of Cortical Plasticity by Leon N. Cooper, Nathan Intrator, Brian S. Blais & Harel Z. Shouval...
In Theory of Cortical Plasticity, Nobel Laureate Leon Cooper and his collaborators present a systematic development of the Bienenstock, Cooper and Munro (BCM) theory of synaptic plasticity, and discuss experiments that test both its assumptions and consequences.
This insightful book provides an elegant analysis of theoretical structure in neuroscience research, and elucidates the role BCM theory has played in guiding research leading to our present understanding of the mechanisms underlying cortical plasticity.
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Studio: World Scientific Publishing Company
Est. Packaging Dimensions: Length: 0.5" Width: 5.75" Height: 8.75" Weight: 1.1 lbs.
Publisher World Scientific Publishing Company
ISBN 9812387919 ISBN13 9789812387912
Availability 0 units.
More About Leon N. Cooper, Nathan Intrator, Brian S. Blais & Harel Z. Shouval
Leon N. Cooper was born in 1922 and has an academic affiliation as follows - Brown University, Rhode Island.
Reviews - What do customers think about Theory of Cortical Plasticity?
Prancing around the elephant in the brain-box May 6, 2010
This is an important and interesting book that reviews some basic ideas about "cortical plasticity", which is the process that actually underlies intelligence and "mind". Cooper and his colleagues have made useful contributions over a long period of time, and most of their key ideas are covered in this book. There is also an admirable attempt to link models to experimental data. But while they provide some important tools and ideas, they are very far from understanding their nominal topic. Partly this reflects their understandable attachment to their own pet idea, the famous "BCM" rule. This was originally intended to solve the standard problem of runaway synapse strengthening in a simple Hebb model (Oja came up with an even more elegant solution around the same time and place). But then they slowly realised that the nonlinear form of their rule also conferred useful sensitivity to input statistics that goes beyond the Oja model, and the book explains this advance quite nicely. But really there are 2 separate problems here: sensitivity to sophisticated statistics, and dynamical stabilisation, and it's not clear that BCM is the uniquely best solution to either. Furthermore, attempts to provide a secure mechanistic basis for the BCM rule have not been very convincing, in part because one really needs to use a framework ("STDP") that is much closer to biology. While the authors focus on pet solutions to relatively easy and obvious problems, they completely miss the elephant in the cortical room: "crosstalk" between synapses means that no real synapses can actually learn from higher-order statistics unless much of cortex is devoted to crosstalk-mitigation. One very attractive possibility is that most cortical circuitry is actually devoted to a "proofreading" operation, that allows the unprecedented synaptic accuracy that underlies powerful computation and learning (see [...]). What matters is not the precise form of the learning rule, but its accuracy.