0 of 0 people found the following review helpful:
The Paganini of Computational Neuroscience Writes Variations on Do Re Mi, 2008-01-07
Koch's book is a tour de force - a chocolate box of biophysics with coatings of equations and melting illustrative centers. I dip into it whenever I want a sharply phrased insight, or hunger for a fact. I have only 2 quibbles. The book is unfortunately too good - the brilliance lavished on it would perhaps have been even better deployed in a frontal attack on the real problem in the biophysics of single neuron computation: understanding how neurons can "learn" the subtle and complex higher-order correlations in their inputs. And the account of the core issue - how single synapses can be both electrically coupled to the distant spike-initiating zone (perhaps 1 mm away) and yet chemically isolated from other synapses that are less than a micron away - is oversimplified and leans too heavily on the author's own somewhat naive analyses. Koch is a genius who risks frittering away his energies on impressing his (grateful and appreciative) audience, rather than pulling the Excalibur of Mind from the Rock of Matter.
5 of 6 people found the following review helpful:
Single Neuron Computational Modeling, 2004-06-11
This is the main book, the "Bible", on single neuron and ion channel computational modeling. Plenty of theory & rigor here! Professor Koch, with CalTech, models single ion channel function, dendrite, dendrite tree function, cable theory, stocastic theories, integrate-fire model, the Poisson model, and discusses how single neurons work together inside the brain. It is worth owning both as a reference book and to use in the laboratory. Dr. Koch has written many other books, but I think this stands out as his best. Methods in Neuronal Modeling 2nd edition is also very good. Koch's writings are complementary, but are not redundant. One can read this book without a problem if you know Calculus.
13 of 14 people found the following review helpful:
How smart is a neuron?, 2004-04-14
For young scientists who are interested in understanding the dynamics of the human brain this change in collective attitude is of profound significance, to which Koch's book provides an ideal introduction.Written in a precise yet easy style, the 21 chapters of Biophysics of Computation begin at the beginning, introducing the reader to elementary electrical properties of membrane patches, linear cable theory and the properties of passive dendritic trees. These introductory chapters are followed by two on the properties of synapses and the various ways that synapses can interact to perform logic on passive dendritic trees. Then the Hodgkin-Huxley formulation for impulse propagation on a single fibre is discussed in detail, and various simplifying models are presented. As a basis for the Hodgkin-Huxley description the present
understanding of ionic channels is reviewed, emphasizing the importance of calcium currents. Further chapters discuss linearization of the H-H equations for small amplitude behavior; present a careful examination of ionic diffusion processes; and describe electrochemical properties of dendritic spines, synaptic plasticity, simple neural models, stochastic neural models and the properties of bursting cells. Just about every facet of currently available neural knowledge is touched upon, with appropriate references to a carefully selected bibliography that will help the diligent novice delve deeply into whatever aspect of neural information processing he or she chooses.All of the above comprises an extended introduction to Chapters 17 to 19, which: `synthesize the previously learned lessons into a complete account of the events occurring in realistic dendritic trees with all of their attendant nonlinearities'. `We will see', the author writes, `that dendrites can indeed be very powerful, nontraditional computational devices, implementing a number of continuous operations.' Thus Biophysics of Computation offers a definitive statement for the direction in which the neural research of the new century should go. Chapter 20, the penultimate, discusses several speculations for non-neural computation in the brain, ranging from molecular computing below the level of a single neuron to the effects of chemical diffusants (nitric oxide, calcium ions, carbon monoxide, etc.) on large numbers of neurons. Although this entire area has been neglected by most of the neuroscience community, Koch points out that there are no good reasons for doing so. As we enter the new century, neuroscientists should keep their minds open. Finally, in the summary of Chapter 21, seven problems for future research projects are listed, emphasizing that the investigation of information processing in single neurons is very much a work in progress. It is of interest to examine these `strategic questions' as they reveal the author's intuitions about possible directions of future developments. (Note that these are not direct quotes, as I have taken the liberty of summarizing Koch's questions.)
(1) How can the operation of multiplication be implemented at the level of a single neuron?
(2) What are the sources of noise in a neural system and how does this noise influence the logical operation of a single neuron?
(3) How is the style of neural computation influenced by metabolic considerations?
(4) What is the function of the apical dendrite, which is a typical cortical structure?
(5) How and where does learning actually take place in a neural system?
(6) What are the functions of the dendritic trees, the forms of which vary so widely from neuron to neuron?
(7) How can we construct neural models that are sufficiently realistic to capture the essential functions of real neurons yet simple enough to allow large-scale computations of brain dynamics?
As these questions indicate, Koch is not merely concerned with understanding
what unusual behaviours the neuron does or might exhibit. His broad aim is to comprehend the relation between this behavioural ability and the computational tasks that the neuron is called upon to perform. In his words:
``Thinking about brain style computation requires a certain frame of mind, related to but distinctly different from that of the biophysicist. For instance, how should we think of a chemical synapse? In terms of complicated pre- and post-synaptic elements? Ionic channels? Calcium binding proteins? Or as a non-reciprocal and stochastic switching device that transmits a binary signal rapidly between two neurons and remembers its history of usage? The answer is that we must be concerned with both aspects, with biophysics as well as computation.''
This excellent book is evidently a labour of love, stemming from the author's 1982 doctoral thesis on information processing in dendritic trees. As far as I can tell all relevant aspects of neural processing are considered, with what seem to me to be just the proper amounts of emphasis. The writing style is precise and rigorous without being stuffy, and the many references to a fifty-page bibliography will be of enormous value to young researchers starting out in this field.
In addition to its obvious value for those engaged in experimental, theoretical or numerical studies of neuronal behaviour Biophysics of Computation would also work well as the text for an introductory course in neural dynamics, perhaps as part of a neuroscience program.
Alwyn Scott
http://personal.riverusers.com/~rover/
13 of 14 people found the following review helpful:
brief & comprehensive, 2001-07-06
This book attempts to integrate bits from papers & other textbooks. Incorporated in the book are all but the most oft-discussed topics in neurophysics.
We don't know much about biological neurons. We don't really understand how they perform computation. Yet we have some models, approximations of the models, and theories of how the model neurons get organized to do computation. These are summarized in this book in a breif & comprehensive manner.
Some notes: 1) Portions of the book may be found in greater detail elsewhere. 2) The book is more about biophysics than compuation.
18 of 29 people found the following review helpful:
Excellent, a good place to start, 2000-04-30
This is a fine comprehensive book. However, it might be helpful to bear in mind, as you study it, that although it was published in late 1998, a few fundamental principles presented in the early chapters were originally developed a long time ago. In particular, the Hodgkin Huxley Katz picture of the neuron was developed in the heroic period of the 1950s after the introduction of the voltage clamp. It is a good model but it may not be the whole story, and could change in important ways as we learn more about the molecular structure of ion channels.
The possibility exists that the neuron is a multichannel device, a cable rather than a wire. The model is attractive because a multichannel nerve would enable us to think as fast as we do. Because nerve impulses are so very slow moving, each successive impulse might, (contrary to everything we thought we knew) be rich in information. A multichannel neuron has the power to convey, with each single all-or-nothing impulse, graded information. For example, to 20 discrete channels, one can assign 20 distinct tiers of meaning, and each channel can thus "mean" a level of intensity between 1 and 20. The phenomenon can easily escape detection because such a neuron appears, to conventional instruments, to convey only the classically blank, binary impulse that is so confidently presented to us on the first page of every neurobiology text, and in summary in this book as well.
To create a continuous longitudinal information channel running the full length of an axon membrane, one would simply link each ion portal to its next door neighbor. A conformation change in one portal induces a conformation change in the next in line. A domino effect more intuitively satisfying, perhaps, than the familiar waveguide or cable models of membrane depolarization reiterated here.
One can visualize many parallel tracks, a corduroy membrane. Possibly linear, possibly helical. Linked receptors are commonplace. The molecular structure of the potassium channel has been published recently, and so we are now finally working at the level where a multichannel membrane can be detected. It is a theoretical construct but if each single impulse carries information, then the computational burden on the nervous system is vastly reduced, and the physiological meaning of intensively studied structures like the synapse suddenly changes. The meaning of several of the models presented in this book also changes, often in quite intriguing ways.