The Computational Brain

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.

This book can be viewed as one of the first attempts to use results from psychology, neuroscience, computer science, and philosophy with the intent of gaining an understanding of how the mind/brain works, but all of this is done within the "computational mind" paradigm. The approach taken by the authors is one of the most honest of those in the literature, for throughout the book they are careful to note just how much evidence there is to support their position(s), and to what extent further work is to done. Philosophically speaking, the authors are clearly in the materialist camp, believing that Cartesian dualism does not cohere with current scientific knowledge. But they state that materialism is not an established fact, allowing the possibility, but not the probability, that dualism may in fact be true. They reject early on though any "arguments from ignorance" in their assertion that just because neuroscience does not have an explanation of consciousness, that such an explanation is impossible. The authors call the failure to be able to think of consciousness in terms of neuronal activity "intuition dissonance", and reject completely its efficacy in establishing the truth of the nature of the mind/brain. 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

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).

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.