How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by The Computational Brain. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework.Computer models constrained by neurobiological data can help reveal how -networks of neurons subserve perception and behavior - bow their physical interactions can yield global results in perception and behavior, and how their physical properties are used to code information and compute solutions. The Computational Brain focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system.The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques.Patricia S. Churchland is Professor of Philosophy at the University of California, San Diego, Adjunct Professor at the Salk Institute, and a MacArthur Fellow. Terrence J. Sejnowski is Professor of Biology at the University of California, San Diego, Professor at the Salk Institute, where he is Director of the Computational Neurobiology Laboratory, and an Investigator of the Howard Hughes Medical Institute.

The underlying theme in the book is to explain emergent properties as "high-level" effects that are dependent on "lower-level" phenomena, hence rejecting the thesis that they are "nomologically autonomous", i.e. that such a dependence cannot be done and is outside the domain of science. The science in this book recognizes its historical origins, and it is clear that the authors will not accept explanations of the mind/brain that do not involve scientific experimentation and analysis. Much has been done experimentally in neuroscience since this book was published, especially using the techniques of magnetic resonance imaging (MRI). A brief discussion of MRI is given in the Appendix of the book, but no doubt if the book were updated there would be a lengthy overview of it. The current experimental situation in neuroscience has led some to predict a total "reverse engineering" of the brain in the upcoming decades. This prediction is an optimistic one, but no doubt detailed knowledge of the brain will continue to accelerate, this being a sign of what the authors call "a remarkable time in the history of science".

The authors devote an entire chapter to the computational modeling of the brain, mostly of course dealing with the mathematics of neural networks. The approach in this chapter though is still at a level that would allow a general audience to follow it. Readers with a background in physics, especially statistical physics, will appreciate more the discussion on Hopfield networks and Boltzmann machines. Experimental results are inserted as graphs throughout the book, with detailed explanation. As a whole the discussion of the biology of the brain is purely descriptive, and the line drawings could stand some improvement.

The chapter on neuronal plasticity is the most interesting in the book, the authors viewing the brain as an entity that is continuously undergoing modification. Their stated goal in the chapter is to explain how the "local" property of plasticity can result in the "global" property of learning. Clearly intelligence to the authors is an emergent property, i.e. an object or device may be characterized as intelligent without its components being intelligent. Particularly interesting in this chapter was the discussion of the amnesia of a patient who underwent bilateral resection of mesial temporal lobe structures. The time scales of the patient's memory are striking: he remembered things before the surgery but could not remember things that happened a few minutes or hours ago, but could remember things within a minute in his past. The authors also mention the fascinating work of Antonio Damasio and his collaborators, this research being even more important at the present time. The scientific study of consciousness is just beginning and no doubt this study will give many surprises as it develops throughout the twenty-first century.

9 of 9 found the following review helpful:


A source of stimulation and frustration  Feb 14, 2007 By John Harpur
There is an argument that this is a book of its time. It is nearly fifteen years since it was put together and a great deal of neural water has flowed under the bridge. The thematic enthusiasm for computationalism that dominates the book has not been convincingly proved in the meantime. If anything, the computational properties of models have been shown to entertain many unpleasant complexity results. Moreover, the localisation of brain functions grounded in naive interpretations of lesion effects has come under greater scrutiny due to detailed MRI results. Given twhat was known at the time, it is unsurprising that the book focuses on the visual system - a focus also found in Christof Koch's recent book. Acknowleding all that and more, it would be hard to find a better condensation of science, computationalism, and philosophical speculation than in this book.

Leaving aside downsides arising from recent discoveries that the authors could not have anticipated, the book can be frustrating to read at times. In particular, there is a tendency to introduce technical concepts and descriptors into accounts without prior definition. For example, very early on in a brief account of monkey vision there is mention of V4, MT, etc. The terms are neither defined nor explained. Strangely, in the introduction to networks, the inner product of two vectors is explained while the outer product is not. Small points but the oversight recurs.

The philosophical content in the book is light, but the assumptions driving the work are among the most contentious. There is no point reaming off a list but the book does not shirk supporing the brain-as-a-computer hypothesis.

All in all a stimulating work, if in need of updating.

7 of 9 found the following review helpful:


a great book  Jan 11, 2002 By misi
This extremely interesting book integrates our vast knowledge of neuroscience with computational models of perception, sensori-motor integration, memory etc.
For students of neuroscience, computer science and psychology this book is extremely important, because it gives you the necessary fundamentals of this field(namely computational neuroscience) so you can get to more advanced levels easily.
Understanding the book will need some background in higher mathematics (differential calculus).

8 of 16 found the following review helpful:


Good summary of empirical and experimental neuroscience  Nov 12, 1996
This book is an excellent follow-up to "Engine of Reason, Seat of the Soul". It gets into the details of what's known about computational neuroscience and the working of the human brain (and other animals ' brains and nervous systems. Great summaries of specific "tricks" of brain processing, e.g., how vison works, etc.