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Rating: 
Summary: Extraordinary analysis of neuroscience & human intelligence
Review: Developments in neuroscience over the last decade will doubtless be compared to the Copernican revolution, totally changing the way educated citizens think of human nature. Churchland provides an invaluable guide to recent research into neural networks, surveying the social, moral, legal and philosophical implications of contemporary neuroscience. While not giving as much emphasis to either the emotional dimension of cognition as Damasio (DESCARTES ERROR) or its social origins and ethical function (see my BEYOND RELATIVISM), Churchland's focus on neural networks has compensating advantages, especially for anyone interested in learning how to think about the way the brain works when we think. Must reading for philosophers and social scientists who are aware that the human brain is neither a blank slate nor a serial computer
Rating: 
Summary: Exciting and Eminently Readable
Review: I can't evaluate the neurobiology in the book since I'm no scientist, but Churchland's entirely accessible discussions of vector coding, feed-forward and recurrent networks, and the general landscape of contemporary neuroscience were exhilarating to read. They made me want to rush out and buy textbooks on the brain--a pretty impressive achievement, as far as I'm concerned.Churchland's philosophical perspective, as anyone familiar with his work will expect, is thoroughly naturalistic. He has very little patience with anti-reductive arguments, and the three he discusses (Nagel's, Jackson's, and Searle's) receive straw-man treatments, though like everything else in the book, each treatment is good-natured and fairly humble. Readers already lacking tolerance for Searle will enjoy Churchland's caricature of The Rediscovery of Mind as a Betty Crocker cookbook. Though his explicit discussion of anti-reductionism is sparse, the rest of Churchland's book serves as a demonstration of how much exciting work can be done if we simply ignore armchair naysaying. So I was more bothered by his lack of engagement with philosophers already on the elimintivist bandwagon. His discussion of Dennett, in particular, was cursory and frustrating. It seems to me that he conflates Dennett's distinct accounts of consciousness and content, needlessly (and in the relevant sense inaccurately) portraying Dennett as being a friend of robust human uniqueness. But quibbles aside, the book is a fantastic read. Its optimistic view of the possibilities of computational neuroscience is infectious. Anyone without ideological blinders on will come away excited about the future of brain research.
Rating:
Summary: Great philosophy, effortless to read
Review: I've not come across a more sensible and lucidly written philosophy book. The author loves and deeply believes in science. He shows to my satisfaction that the hard sciences can answer many humanities questions or make them clearly pointless. The chapters on vector processing are still not quite as scientific as the author would like them to be, but the book overall has significantly improved my understanding and appreciation of human and mammalian minds. Since Amazon doesn't do it, here is the table of contents: (1) The little computer that could: the biological brain, (2) Sensory representation: the incredible power of vector coding, (3) Vector processing: how it works and why it is essential, (4) Artificial neural networks: imitating parts of the brain, (5) Recurrent networks: the conquest of time, (6) The neural representation of the social world, (7) The brain in trouble: cognitive dysfunction and mental illness, (8) The puzzle of consciousness, (9) Could an electronic machine be conscious?, (10) Consequences for language, science, politics and art, (11) Neurotechnology and human life. Looking at the Index at the back, the entry that occurs on the most pages in the book is "prototype" which in this book means pretty much the same as what some other authors call a paradigm.
Rating:
Summary: A Stunning Triumph of Cybernetic Plutocracy
Review: The author convinced me how to explain mind as a process of multidimensional vector coding in a massive parallel biological neural network. Everything is neatly explained and illustrated with lots of examples and data from scientific experiments. He did not convince me that he has a grip on qualia with this explanation. If all there were to pain were knowledge of the "inside path" to the same neural phenomena, why would the victim suffer while the the torturer enjoys the act of torture? Pain is not "inside or outside" knowledge of pain and not only pain behaviour. The same goes for colors and "what is it like to be a bat". The other thing I did not like about the book are the chapters in the second part of the book containing a wild speculative outlook about the social and other developments that might be possible as a consequence of a systematic mapping of mind processes on physical processes in the brain. While I would fully subscribe to the consequences of understanding neural processes for ethics described in chapter 6, I find the phantasies disgusting about how we might in the future be dealing with social offenders. Some of the ideas remind me that there were judges in the US who ordered the sterilization of law offenders because they believed science had proven their behaviour would be genetically transmitted to their offsprings. In all, however, this is a superbly written book, an easy to understand account about quite complicated stuff and I learned many new things by reading it. Thank you Prof. Churchland.
Rating:
Summary: Good Intro to Neural Nets and Its Consequences
Review: The book comes in two parts. The part one, which takes up more than a half of the whole book, explains what recurrent neural networks are and how those can be used to explain our own cognitive functions. This is generally a good introduction, I think. His style is casual, and we see certain smugness you normally expect at a college lecture, e.g., introducing certain authorities as his friends and presenting the picture his own daughter and the medial and lateral brain stereographs of his wife (Patricia Churchland). Like other popular science books, however, his description of neural nets is far from precise but let's not expect too much from a book of this kind. Unlike what some of our reviewers below suggested, he minimizes the use of scientific jargons and when he use such jargons he explains what those are. The first part was overall very much enjoyable to read. You cannot expect it to be a fully philosophical book, though. His new epistemological framework arises from this newest perspective the theory of neural networks has created. To know what neural nets are is immensely important. Let's remind ourselves of a classic work in cognitive science and neurobiology. It's David Marr's _Vision_. There Marr expresses the view that physical (hardware) implementation is quite irrelevant. Now we know this is not true. To understand why this is so one may have to consult the part one. The problem area is the part two. The chapter 11 was full of hopes and lots of blah-blah-blah's that bore you to hell. What's interesting, and makes you slightly angry, is his explanation of consciousness. Perhaps that is because Churchland's argument seem amazingly simple. But, to think about it, it has to be simple. Otherwise it cannot be a reduction. If you want to argue against reductionism, you need to bring up some form of dualism. In fact, this is what Searle does. Searle's arguments are not directed agains neural networks. His favorate scapegoat is symbolic computation. But this is something researchers have done away with a long ago. I personally think Searle never really understood what neural nets are. What's not really satisfactory are these: Some will find he never really defeated Nagel and Jackson. I should agree with those who think so. If ever he did, his argument lacked logical clearity or I am very dumb. He is not successful in constructing a model of consciousness, either. The problem is, he thinks he is. Like Newton did, and Euclid earlier, he tries to create a set of descriptive axioms to come to grip with consciousness. But unlike Euclid, Netwon, and Einstein (remember his two postulates), some of his axioms require a first-person perspective. (ref. pp. 213-214) For example, to verify that consciousness disappears in deep sleep, somebody obviously has to go to bed. However imprecise, MEG maybe used to detect conscious activities in a live brain. But there exists no 3rd-person method to verify consciousness is a single unified experience. Churchland has been successful in explaining a lot but I think we still have a long way to go. And his descriptive theory is not adequate. Plus, there is a misprint in page 230 of the softcover edition. The "o%cial" should be read "official".
Rating:
Summary: Good Intro to Neural Nets and Its Consequences
Review: The book comes in two parts. The part one, which takes up more than a half of the whole book, explains what recurrent neural networks are and how those can be used to explain our own cognitive functions. This is generally a good introduction, I think. His style is casual, and we see certain smugness you normally expect at a college lecture, e.g., introducing certain authorities as his friends and presenting the picture his own daughter and the medial and lateral brain stereographs of his wife (Patricia Churchland). Like other popular science books, however, his description of neural nets is far from precise but let's not expect too much from a book of this kind. Unlike what some of our reviewers below suggested, he minimizes the use of scientific jargons and when he use such jargons he explains what those are. The first part was overall very much enjoyable to read. You cannot expect it to be a fully philosophical book, though. His new epistemological framework arises from this newest perspective the theory of neural networks has created. To know what neural nets are is immensely important. Let's remind ourselves of a classic work in cognitive science and neurobiology. It's David Marr's _Vision_. There Marr expresses the view that physical (hardware) implementation is quite irrelevant. Now we know this is not true. To understand why this is so one may have to consult the part one. The problem area is the part two. The chapter 11 was full of hopes and lots of blah-blah-blah's that bore you to hell. What's interesting, and makes you slightly angry, is his explanation of consciousness. Perhaps that is because Churchland's argument seem amazingly simple. But, to think about it, it has to be simple. Otherwise it cannot be a reduction. If you want to argue against reductionism, you need to bring up some form of dualism. In fact, this is what Searle does. Searle's arguments are not directed agains neural networks. His favorate scapegoat is symbolic computation. But this is something researchers have done away with a long ago. I personally think Searle never really understood what neural nets are. What's not really satisfactory are these: Some will find he never really defeated Nagel and Jackson. I should agree with those who think so. If ever he did, his argument lacked logical clearity or I am very dumb. He is not successful in constructing a model of consciousness, either. The problem is, he thinks he is. Like Newton did, and Euclid earlier, he tries to create a set of descriptive axioms to come to grip with consciousness. But unlike Euclid, Netwon, and Einstein (remember his two postulates), some of his axioms require a first-person perspective. (ref. pp. 213-214) For example, to verify that consciousness disappears in deep sleep, somebody obviously has to go to bed. However imprecise, MEG maybe used to detect conscious activities in a live brain. But there exists no 3rd-person method to verify consciousness is a single unified experience. Churchland has been successful in explaining a lot but I think we still have a long way to go. And his descriptive theory is not adequate. Plus, there is a misprint in page 230 of the softcover edition. The "o%cial" should be read "official".
Rating: 
Summary: The connectionist dream
Review: This book is the hallmark of the connectionist dream -the belief that all aspects of mind, brain and consciousness can be explained by calling up neural network models-. Now the basic premise behind all this I will not contest. The brain is a large parallel distribuited processing network of neurons. But there is another big step from this to the statement that everything the mind is is a vector coding of a neural network. This is far too siplistic. Churchland of course realizes this, but continues to talk of connectionist models like neurosciences messiah.
This is perhaps only one aspect of Churchlands book, however. Overall, the book attempts to reconcile philosophy of mind with neuroscience, and it succeeds to an extent. In many parts the discussion falls into vector coding talk, but in many others it stellarily accounts for deep problems. It is a good introducion to neuroscience, neural networks and philosophy. Churchland does not present his own strong theories, but he does well in staying away from controversy. The best part of the book is in my opinion, the attempt to build a framework of the impacts neuroscience has in social and philosophical domains. This is not done often enough, and if it is, rarely with such lucidity and clarity.
Now I would have ceritanly liked much more speculation when it commes to consciousness, given the Churchland's contribuition to the literature. But he refrains from this and merely describes some other models, like Llinas thalamic oscillations, and is content in stating that it is at leas possible to see what an explanation for consciousness would look like from a neuroscience context.
The book is a grat read, and students of philosophy, neuroscience and cognitive science should enjoy it.
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