Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.

Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.

Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

All of the above comprises an extended introduction to Chapters 17 through 19, which in the words of the author: "synthesize the previously learned lessons into a complete account of the events occuring in realistic dendritic trees with all of their attendant nonlinearities. We will see 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, discussed 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 the neuroscience community, Koch points out that there are no good reasons for doing so.

Finally, in the summary of Chapter 21, seven problems for future research projects are listed, emphasizing that the study of information processing in single neurons is very much a work in progress.

15 of 16 found the following review helpful:


brief & comprehensive  Jul 06, 2001 By Jihwan Myung
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.

14 of 15 found the following review helpful:


How smart is a neuron?  Apr 14, 2004 By Alwyn Scott
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/

5 of 6 found the following review helpful:


Single Neuron Computational Modeling  Jun 11, 2004 By Joseph J Grenier "jjg MD PhD"
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.

4 of 6 found the following review helpful:


Current insights into progress and problems  Jul 05, 1999 By pyramidl@trilobyte.net
Koch combines summary of ground work before sparse matrice technique, and includes the current work with them. All examples in book and data can be incorporated into Mathcad 8,(Tridiagonal matrice in Numerical Recipes from Mathsoft was made for this stuff) especially signal processing (includes convolutions) and wavelets. Reviewer read book hoping to bridge gap between actually Biophysics and "black box" models used in psychological models of cognition. Chap. 7 of Koch's book on the non-linear dynamics of the problems involved is worth the price of the book alone.

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