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16 of 18 people found the following review helpful:
3.0 out of 5 stars
Too qualitative, October 11, 2003
The author introduces the book as one about thoughts, memories, consciousness, creativity, etc., with his goal being to put these subjects in the context of an evolutionary paradigm. The cerebral cortex represents mental images via a Darwinian process, recombining them to create something totally original. When considering my dreams, or the moments of consciousness when I am just falling off to sleep, I can certainly sympathize with the author's thesis. However, throughout the book I wanted to see equations and graphs, discussions on mathematical modeling/simulations and laboratory experiments. Instead the approach is purely descriptive, making the book somewhat of a disappointment. The author though warns the reader early on that he resisted the temptation to utilize computer simulations, citing the need for clarity, and his skepticism of "free-parameter curve-fitting" as the main reasons. But even though the author takes a purely qualitative approach, it is still embedded in a scientific description, and not mere philosophical handwaving. The first two chapters are an overwiew of the author's solution of the representation problem, this problem in his view being which spatio-temporal pattern represents a mental object. The author is clearly influenced by the neurologist D.O.Hebb, and throughout the book he attempts to answer the representational questions that Hebb posed back in the 1940's. Cerebral representations must explain spatial-only and spatiotemporal patterns, their interconversions, redundancy, spatial extent, and imperfections, and how they are linked to associative memory. Arguing for the need for copying, the author shows how it can arise in the neocortex. His (Darwinian) mechanism for copying takes place among the interactions of the superficial pyramidal neurons, due to their physical properties and their geometric layout. Interestingly, the phenomenon of "emergent synchrony", familiar to the physics reader in the motion of the double pendulum, is shown to play a role in the copying mechanism. Indeed the superficial layers of the neocortex are shown to form (ephemeral) triangular arrays interacting via entrainement. The next few chapters are devoted to showing just how the triangular arrays result in successful representations. The stability of the triangular arrays formed by the "hot spots" under perturbation is addressed, the author showing how the six "nearest neighbors" have a correcting influence on the spot if it fires out of sync with them. The minimal Hebbian cell-assembly is thus shown to be a hexagon, and that author shows how they are related to triangular arrays: namely, that two triangular arrays can alter synaptic strengths and create attractors within a hexagon's circuitry that sustain the firing pattern. The author's use of concepts and constructions from dynamical systems in this chapter and the next two is very interesting but made me thirst for more quantitative justification. Indeed chaotic dynamics is brought in to explain the "memorized environment", which for the author is the most difficult problem to explain from the standpoint of his Darwinian shaping-up process. Calling chaos "controlled disorder", the author holds that the EEG patterns in deep sleep are limited-cycle rhythmicity, that Parkinson tremors are the result of fixed-point attractors, and the Necker cube perspective switching is switching in and out of lobes of an attractor. He does admit though that all these are "loose analogies" and goes on to explain in more detail how resonances influence cortical territory by spatio-temporal patterns that arrive by lateral cloning. The Darwinian paradigm via the overlaid hexagons is asserted to be one of the elementary mechanisms for category formation, and thus are able to deal with higher levels of abstraction, such as one finds in advanced mathematics. If the mechanism put forward by the author is correct in explaining such high-level reasoning, this would be a major advance in cognitive science. As if detecting that the reader-scientist may be disenchanted with purely philosophical discussion, the author elaborates on his Darwinian paradigm in the rest of the book and offers a new perspective on the nature of categories in the context of this paradigm. He adheres to the assertion that categories are indispensable for using words in a referential manner, as linguistic symbols do not relate directly to the objects in the world, but to concepts of the classes which the objects belong. A hierarchical network of meanings is essential for this to occur. The author has taken on a problem of enormous difficulty here, but does give explanations that seem plausible. The "hexagons for cerebral codes" are capable he says of handling any level of abstraction or representation. Interestingly, his explanations make use of another concept from physics, that of Brownian motion, to discuss the role and origin of associative memory in his Darwinian paradigm. The role of "recombination" in the Darwinian process is explained as a need for integrating codes that are stored separately in the brain into a "master code" for a particular concept. "Hexagonal cloning competitions" are thought of as processes by which information can be (serially) ordered and missing information can be identified. The author makes his case for the utility of metaphor crystal clear, for without such metaphors he says, without imagination, we will have no mechanisms to mold experience or to discover new things. Consciousness too, deemed the most complex of phenomena to be described by a theory of brain function, is explained in the context of his hexagonal neocortical arrays. Consciousness is a result of the multiple levels of "stratified stability", each of these employing Darwinian processes to enhance quality and create new things. In addition, he discusses practical consequences of his brain theory in psychiatry, rather than in merely explaining the capabilities of the brain. With more experimentation, with more modeling, with more simulations, and with further refinements and clarifications to the physical concepts which he uses, his ideas will become vastly more convincing. However exotic they may appear, his ideas, and others in brain modeling, will require careful elucidation, and future developments are to be greeted with eager anticipation.
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7 of 8 people found the following review helpful:
5.0 out of 5 stars
My review of "The Celebral Code"., December 28, 2001
This review is from: The Cerebral Code: Thinking a Thought in the Mosaics of the Mind (Paperback)
Calvin surprised me in this book. I am the kind of guy interested in intelligence, how it might work biologically, and lastly I was given an advice by a fellow at bionet.neuroscience. The book gave me food for thought, and even as I am studying neurology in much more detail; "Principles of Neural Science" by Kandel et al; the basic idea that Calvin lay down in written form is still influencing me. But if you really want the best usage of this book, you at least have to know SOME basics (which I didn't have to much of), and read the book when you know what corticothalamic pathways mean. 5 stars for the book, well deserved. This applies also for "How Brains Think" which was written before the "The Celebral Code". I urge you to get both books, read first "How Brains Think", and then "The Celebral Code".
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2 of 2 people found the following review helpful:
4.0 out of 5 stars
A Review of The Cerebral Code, December 3, 2006
Calvin contends that brains think by virtue of
being "Darwin Machines", machines that emulate
biological evolution but on a much reduced time
scale. He goes on to suggest how these processes
might occur in biological neural networks.
Unfortunately his ideas have not been developed
to the point of actual algorithms and experiments.
This is what is missing. While recurrent excitation
is known to occur what about Calvin's "triangular
arrays", "lateral cloning", "hot spots", "synchronous
recuitment", "attractor formation", "pattern
competition", "memory recall", etc. etc.? All of
these ideas need to be fleshed out, coded in
artificial neural network software, and sought in
computer simulations. Such experiments are what is
needed to turn speculation into theory. As a happy
biproduct if such experiments prove successful one
would have a working prototype artificial intelligence.
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