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Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently.
Setting out to "reverse engineer" the brain -- disassembling it to understand it -- Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of "anticipatory regulation"; identify constraints on neural design and the need to "nanofy"; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes "save only what is needed."
Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.
- Sales Rank: #230168 in Books
- Published on: 2015-05-22
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .94" w x 6.00" l, .0 pounds
- Binding: Hardcover
- 568 pages
Review
Time, space, energy, and information: these are the key themes of this fascinating book, which takes the spirit of the Feynman Lectures on Physics and applies it to explain how the brain has been designed, by evolution, to process information efficiently. Unique insights, recounted in the authors' characteristically appealing style, are to be found on every page.
(David Attwell, Jodrell Professor of Physiology, University College London)
This is not your typical neuroscience textbook. Rather than catalogue processes and structures of the central nervous system, Sterling and Laughlin take a unique approach to interpreting the brain. They consider the general principles that have shaped brain design through natural selection, principles that make the human brain 10^5 times more efficient than the most powerful computers. Chapters examine efficient encoding of information, layout of brain circuits, and strategies for learning and memory. This overview of brain function is refreshing and insightful.
(Eric A. Newman, Distinguished McKnight University Professor of Neuroscience, University of Minnesota)
Drawing on their areas of expertise, Sterling and Laughlin have written a beautiful book addressing some simple questions about how the brain works, enriching the reader with many 'aha' moments. This transformative book brings us closer to understanding the logic that nature could be using in its design of neural circuits. And, in fact, perhaps the biggest impression from reading the book is a renewed awe for the incredible work that nature has done with nervous systems, which must be the crowning achievement of evolution. I cannot wait to use it as textbook in my course on neural circuits.
(Rafael Yuste, Professor of Biological Sciences, Columbia University)
The authors have been thinking deeply about the issues discussed and it shows, the neurobiology is right up-to-date, and the writing is artful, clear, and engaging. This book is a wonderful start for what will, I believe, become the standard way for conceptualizing neurobiology.
(Charles F. Stevens Current Biology)
About the Author
Peter Sterling is Professor of Neuroscience at the University of Pennsylvania School of Medicine. Simon Laughlin is Professor of Neurobiology in the Department of Zoology at the University of Cambridge and a Fellow of the Royal Society.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Breathtaking vista
By P. Nelson
I have read a lot of neuroscience books that are heavy on "What" and "How," that is, the mind-blowing anatomy and physiology of the brain. Truly a lot is known, and one can spend a lifetime on just these aspects. But few authors dare, or are qualified, to weigh in on "Why" things are as they are. Perhaps they are afraid of being thrown into the bin of teleologists and scorned. Certainly these are dangerous waters, with so many just-so stories about how things must be as they are.
Sterling and Laughlin have the technical chops to do better. To pack that staggering computational power into a few kilograms, with power dissipation of a just a hundred watts, evolution has had to find and exploit a lot of tricks. Evolution is often portrayed as doing a lot of things at random because it just happened to stumble upon a good-enough kludge here and there. The authors make the case that in contrast, the demands on neural performance are so severe that truly optimal design wins. They lay out a handful of principles, then take us on a tour showing how those principles play out in the same way across many scales, many sensory and processing modalities, and even many species, as diverse as human and fly. Along the way, they also introduce the reader to many amazing organisms (the star-nosed mole!) and equally amazing abilities that even humans possess (single-photon sensitivity!), so every chapter is filled with revelations.
By the end, the authors do not shy from explaining how their viewpoint, painstakingly obtained by looking at specifics, also illuminates the human condition, and by this point, the reader has earned these insights. There is really no other book with these ambitions combined with such expertise and global view.
4 of 4 people found the following review helpful.
Conquering Data Mountain
By Dr. James V Stone
This book is both ambitious and courageous. It is ambitious because it seeks to extract general principles from the accumulating mountain of data about brain function, and it is courageous because the principles discovered are not expressed only in the imprecise realm of mere words. Instead, Sterling and Laughlin interpret physiological findings from a wide range of organisms in terms of a single unifying framework: Shannon’s mathematical theory of information, in which information is a well-defined quantity measured in bits.
From the outset, Sterling and Laughlin make it quite plain that this not a book of speculation about brains, but it is a book which shows how definite facts can be used extract general principles of brain function. In the search for these general principles, Sterling and Laughlin cast their investigative net wide, covering sensory and motor systems in species which represent a diverse range of evolutionary experiments.
By comparing these systems in the bacterium Escherichia coli, the single celled Paramecium caudatum, the nematode worm Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and a variety of mammals (including humans), the authors show that:
1) even though the biochemical or neuronal signalling systems vary greatly within each species according to the particular demands placed on each sensory system (e.g. vision versus olfaction),
2) the processing within each type of sensory system (e.g. vision) is similar when considered across different species.
For example, they show that the information processing steps in neural networks that support vision are different from those that support audition within the human brain, but the information processing steps in neural networks that support vision in humans are very similar to those that support vision in flies.
Rather than noting this as an intriguing evolutionary coincidence, Sterling and Laughlin ar- gue convincingly that these findings are the result of an interaction between universal physical constraints and species-specific constraints on information processing. These constraints dictate that the energy cost of each additional bit of information rises extremely steeply, resulting in rapidly diminishing informational returns on each extra Joule of energy expended. And because energy cost translates fairly directly into Darwinian fitness, the importance of these constraints is self-evidently paramount.
The principles discovered are made clear at the outset, and these principles are re-discovered many times within the book as different species and sensory systems are examined. In this regard, the book resembles Darwin’s great work, On the Origin of Species. Sterling and Laughlin do not just propose a vague but plausible idea in the hope that others might believe it. They propose a series of testable hypotheses (principles), formulated in mathematically precise language, and support them with a detailed analysis of hard evidence drawn from a virtual archipelago of diverse sources. This book deserves to have an enormous impact on neuroscience (and the various ’brain sciences’), because (in the best traditions of science) it provides a framework for condensing mountains of physiological data into a neat theoretical (information-shaped) molehill.
Sterling and Laughlin do not claim that the set of 10 principles they discover are either complete nor infallible, but they make a convincing case that these principles deserve to be taken seriously. These principles are not listed here because they require substantial context for their worth to be appreciated. The fact that the principles cannot be stated in isolation reflects the authors’ ability to simplify, but not to over-simplify, what is essentially a complex problem.
Like most books on the brain, this one explains how particular mechanisms execute particular functions, but, unlike most books, it also makes frequent use of the word "why". According to Sterling and Laughlin, it is not enough (for example) to understand the physical mechanisms which tells us how a photon changes the voltage of a photoreceptor by some amount. A complete theory of vision should also tell us why the mechanism is the way it is, why its voltage changed by that amount, and why not twice nor half that amount.
This emphasis on why the brain operates as it does has a strong tradition uniquely associated with the computational approach to brain function espoused in books by Marr (Vision, 1981), Rieke et al (Spikes, 1997), Land and Nilsson (Animal Eyes, 2003), Dayan and Abbott (Theoretical Neuroscience, 2005), Bialek (Biophysics: Searching for Principles, 2012). So the approach is not new, but it is rarely adopted, nor expressed as cogently as it is in this book; a book which will still be read long after books which describe only how neurophysiological mechanisms work have been forgotten.
Whilst the authors’ ambition is laudable, some aspects of the execution could be better. This book relies critically on the reader having a firm grasp of Shannon’s formal definition of information. Even though a few pages are dedicated to this, a tutorial account would allow less numerate readers to appreciate the many results which depend on understanding information theory. On a similar note, certain key technical terms are not explained (e.g. Nyquist limit, Fourier transform, and point spread function). Addressing these problems would have greatly improved the book’s accessibility. Having said that, the effort involved in researching topics via Google (as readers are enjoined to do in the Preface) would be well rewarded with a clearer understanding of the book.
In conclusion, the authors clearly believe neuroscience suffers from two related problems: too much data and too little theory. They claim that, “the best we can do with Data Mountain really is just to set a few pitons up the south face”. But I think they have achieved much more than this. Sterling and Laughlin have firmly established a base camp, and have hewn a path which will allow scientists of sufficient skill and fortitude to conquer Data Mountain.
--
Note: In order that you can gauge if I am qualified to comment on this book, my name is
Dr JV Stone, a lecturer in the Department of Psychology, University of Sheffield, England.
I have published books and papers on vision, computational neuroscience and information theory.
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0 of 0 people found the following review helpful.
New context for neuroscience topics
By Amazon Customer
I like this book a lot. "Academic" neuroscience books all cover the same set of subjects and usually from similar perspectives. By putting traditional neuroscience topics in a unique context, this book give you the opportunity to cover these topics in a new way, reinforcing what you've already learned while giving you new insight to some of them.
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