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Neural Control Engineering: The Emerging Intersection between Control Theory and Neuroscience (Computational Neuroscience Series), by Stev
Download PDF Neural Control Engineering: The Emerging Intersection between Control Theory and Neuroscience (Computational Neuroscience Series), by Stev
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Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience. The revolution in nonlinear control engineering in the late 1990s has made the intersection of control theory and neuroscience possible. In Neural Control Engineering, Steven Schiff seeks to bridge the two fields, examining the application of new methods in nonlinear control engineering to neuroscience. After presenting extensive material on formulating computational neuroscience models in a control environment -- including some fundamentals of the algorithms helpful in crossing the divide from intuition to effective application -- Schiff examines a range of applications, including brain-machine interfaces and neural stimulation. He reports on research that he and his colleagues have undertaken showing that nonlinear control theory methods can be applied to models of single cells, small neuronal networks, and large-scale networks in disease states of Parkinson's disease and epilepsy. With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field. The book will serve as an essential guide for scientists in either biology or engineering and for physicians who wish to gain expertise in these areas.
- Sales Rank: #1236048 in eBooks
- Published on: 2011-11-10
- Released on: 2011-11-10
- Format: Kindle eBook
Review
For many years, Steve Schiff's pioneering work has led the way toward a deeper understanding of the brain as a dynamical system. In this beautifully written and groundbreaking book, he presents a new synthesis of neuroscience, computation, and engineering. Neural Control Engineering will be a welcome resource for all working in this emerging field, and it will guide and inspire the next generation of students. -- Olaf Sporns, Provost Professor, Department of Psychological and Brain Sciences, Indiana University, author of Networks of the Brain
About the Author
Steven J. Schiff, a neurosurgeon, is Brush Chair Professor of Engineering and Director of the Center for Neural Engineering at Pennsylvania State University.
Most helpful customer reviews
12 of 14 people found the following review helpful.
Predicting and Controlling Brain Activity
By Jack Cowan
Niels Bohr has been quoted as saying "the hardest thing to do in science is to predict, especially the future!" This book is concerned, in large part, with this problem, in its application to controlling the dynamics of the brain. The problem involves collecting observations of brain activity, filtering out the effects of noise and measurement errors, and controlling brain dynamics. In many ways this set of problems originated in Gauss's least squares method of minimizing the effects of measurement errors, which is over 200 years old. But it was during the period between World Wars 1 and 2, that the current methods for dealing with noisy data in the exercise of predictive control were really started by Norbert Wiener, and by Andrey Kolmogorov in 1941. Wiener's theory considered data in the form of continuously changing data represented by stationary random processes with Gaussian statistics, and Kolmogorov studied very similar processes generated by data sampled at discrete times. Essentially Wiener and Kolmogorov developed filters to remove the effects of noise and measurement errors from the data, so the theory is often called the Wiener-Kolmogorov filtering theory.
The problem with both theories was that they dealt only with linear, stationary and Gaussian processes. It took another 20 years before Rudy Kálmán (1960) introduced a filter theory to deal with noisy dynamical systems, and another 40 years or so before the Kalman filter techniques were used to estimate the parameters of dynamics generated by various equations describing neural activity. Schiff's book is the first comprehensive account of this development. Schiff is perhaps uniquely qualified to write this account. He is both a practicing neurosurgeon and computational neuroscientist, and a pioneer in the application of control techniques to problems such as chaos control.
The early chapters provide a brisk introduction to least squares minimization and its connection with Bayes Rule, and thence to iterative methods for data assimilation, i.e., the processes that incorporate measurements into various models of neural activity, in particular to the Kalman filter approach for discrete data, and the Kalman-Bucy filter (1961) for continuous data. The first neural examples considered are the Hodgkin-Huxley equations (1952) that model the ionic current flows that trigger current pulses in neurons, and various simplified models such as the Fitzhugh-Nagumo equations (1961). Schiff shows that such simplified models often lead to effective controls of neuronal activity. In keeping with this, the next chapter deals with a population model of large-scale neural activity, the Wilson-Cowan equations (1972, 1973). These are essentially a spatio-temporal extension of Fitzhugh-Nagumo like equations, and Schiff shows how a Kalman filter approach can again be used in an efficient way to control their dynamics. The final two chapters of this sequence deal with the construction of ab initio models and filters based directly on data assimilation, and with the inadequacies of all such models. Chapter 7, the first of these two chapters, studies the utility of such techniques as Principal Components Analysis, and Singular Value Decomposition and other techniques, as ways of abstracting from data sets, a set of uncorrelated linear combinations of parts of the data set, that carry the essential information in the set. The first few such components can then be used in a Kalman filter approach. Examples are described of applications to Image Analysis, both static and dynamic, and to the analysis of Spatiotemporal Brain Activity.
The final chapters, 9 through13 are perhaps the most interesting. Chapter 9 covers such topics as Apostolos Georgopoulos' discoveries in 1982-1986, that the direction of a limb movement is uniquely predicted by the activity of a (small) population of neurons in the monkey motor cortex. Essentially each neuron in the population is tuned to respond maximally for a preferred direction of limb movement, and the vector sum of the population activity drives the intended movement. [There is a corresponding effect in the visual cortex: the vector sum of the population activity of a (small) population of neurons in the visual cortex accurately represents the local orientation of the edge of an object in the visual field, as was demonstrated by David Hubel and Torsten Wiesel in 1959-1974.]
These findings were seminal for the development of brain-machine interfaces in the form, for example, of implanted arrays of microelectrodes. The resulting deluge of data required much assimilation via Kalman filters. Chapter 10 provides a very interesting introduction to Parkinson's Disease, and the models thereof developed by David Terman and colleagues (2002-2004). These models are simplified Hodgkin-Huxley-like models, and provide a first attempt to model Parkinson's Disease and to provide insight into the efficacy or otherwise of deep brain stimulation. Chapters 11-12 provide a brief look at the use of electric fields to stimulate the brain, and at recent attempts to understand and control Epileptic Seizures. The final chapter, 13, is more speculative, but raises the possibility that brain themselves implement Kalman filters.
In summary, I found this book extremely interesting and well written. I have only some minor caveats. There is almost nothing about Wiener-Kolmogorov filters. In Chapter 11 the method of reducing the resistive tree structure of a neural dendrite to an equivalent cylinder is introduced with no citation of Wilfrid Rall's introduction of this method (based on impedance matching) in 1957-1960. Apart from these, this book is a gold mine for anyone interested in how to model brain activity, and how to control it.
This review was written at the invitation of Physics Today magazine, a publication of the American Institute of Physics.
3 of 4 people found the following review helpful.
Interdisciplinary Gem
By Thomas S
In terms of what this book is about, I have not much to say beyond what a previous reviewer said. What I want to emphasize is the extraordinary fact that all these chapters are offered by the same author. He has obviously spent many years of his life to learn and practise enough engineering, mathematics, statistics, physics, philosophy - beyond his primary profession as a neurosurgeon trained in physiology - to meaningfully bring together what is needed to advance his field beyond this point.
Why should you read this book - beyond admiring the last polymath?
Case 1: You plan to attend Prof. Schiff's Course - well then it's obvious
Case 2: You plan to teach a course in a related field - then you can take this text as a blueprint for your preparation
Case 3: You plan to do work in neural control engineering - then you will find your learning agenda set in this book. You will probably master one or two of the branches covered, but you need to develop some intuition on the others as well
Case 4: You are just curious what is going on in this area - that's how I came to read it. Then you will understand what it is like to go beyond trial-and-error in a field where full understanding of the inner workings of the system is way out of reach.
The book is a very entertaining demonstration that intuition is of utmost importance in interdisciplinary work - and that intuition comes as the result of hard work.
What I personally enjoyed is that where other authors offer bold statements and hyperbole, Steven Schiff shows courage and hope. That is a major difference.
Hope and courage is needed in particular for the last chapter "Assimilating Minds". The author is well aware that anything achieved so far in neural control engineering looks like mere stamp collecting if you had set out to understand the mind - as many did. But why not take the finest stamps from distant countries as inspiration and encouragement to eventually go to these places yourself!
The volume is staged as a serious text in a specialized field. So color illustrations are offered not for glamour but where it helps to understand a figure. For a high quality hardcover edition of a cutting edge text, I find the book reasonably priced.
I can give my full recommendation.
1 of 1 people found the following review helpful.
A Research Monograph on Neuronal Models and Related Filtering Problems
By Luigi Fatori
This is a very nice interdisciplinary book. It deals with neuronal models and a number of issues related to filtering and model uncertainty. This book will prove helpful for those who need to grasp knowledge about neurons, models and data from both. There is much information on historical development of the field which is an extra benefit. The author succeeds in making a strong point for Kalman filtering (in one of its nonlinear versions, the UKF) as a tool for handling, model, data and uncertainty. The book, which is very well written, reads more like a research monograph than a textbook and the bias is clearly towards neural issues albeit the author's efforts to relate the material to control engineering. Given the "plant" considered by the author, it is fair to say that his efforts have been quite successful.
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