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From Computer to Brain William W. Lytton From Computer to Brain Foundations of Computational Neuroscience With 88 Illustrations Springer William W. Lytton, M.D. Associate Professor, State University of New York, Downstato, Brooklyn, NY Visiting Associate Professor, University of Wisconsin, Madison Visiting Associate Professor, Polytechnic University, Brooklyn, NY Staff Neurologist., Kings County Hospital, Brooklyn, NY SUNY 450 Clarkson Ave., Box 31 Brooklyn, NY 11203 USA Cover illustrationi Roy Wiemann, (2002). Library of Congress Cataloging-in-Publicntion Data Lytton. William W. Prow computer to brain : foundations of computations! netirosrii>nce / Willia.ni W. Lytton. p. nn Includes bibliographical references and index. ISBN 0-367-95528-3 (alk. paper) (cid:151) ISBN 0-387-95526-7 (pbk. : alk. paper) 1. Computational neiirofidence. I. Title. QP357.5 ,L9S 2002 006.3(cid:151)dc2 1 2002070819 ESBN 0-387-95528-3 (Hard cove ) Printed on acid-free paper. ISBN 0-387-95526-7 (Soft cover) ' 2002 Springer-Vurlag New York, Inc. All rights rettervcd. This work may not be translated or copied in whole or in purl without the written permission of the publisher (Springer-Verlag New York, Eno., 175 Fifth Avenue, Ni:w York, NY II1010, USA), except for brief exoerpts in connection with reviews w scholarly analysis. Use in connection with tmy form of in forma Lion storage ati’l retrieval, electronic adaptation, computer ao ft ware, or by similar or dissimilar methodology now know hereaf- ter devclojiod is forbiilil- a Tin- use in tliis pabUcation of trade Dames, trademarks, service rowka and similar tenna, even If they an not identified as snch, is not to be taken as an expression of opinion BH to whether .ir not they are subject to proprietary rights. Printed in the United Slates of Anierica. 9 8 7 6 5 4 3 2 1 SPIN 10883094 {Hard cover) SPIN 1088303G (Soft covctr) Typesetting: Pages created by the author using a Springer UTEJX macro pad www .spri nger-ny .corn Springer-Verlag New York Berlin j; A member of BerteismannSpringer Saenrr+Bu.riw.ss Media GmbH for Jeeyune and Barry Foreword In From Computer to Brain: Foundations of Computational Neuroscience, William Lytton provides a gentle but rigorous introduction to the art of modeling neurons and neural systems. It is an accessible entry to the methods and approaches used to model the brain at many different levels, ranging from synapses and dendrites to neurons and neural circuits. Dif- ferenttypesofquestionsareaskedateachlevelthatrequiredifferenttypes of models. Why learn this? Oneofthereasonswhysomeonemightwanttolearnaboutcomputational neuroscience is tobetter predictthe outcomes ofexperiments. The process of designing an experiment to test a hypothesis involves making predic- tionsaboutwhatthepossibleoutcomesoftheexperimentmightbeandto work out the implications of each possible result. This is a difficult task in most biological systems, especially ones like the brain that involve many interacting parts, some of which are not even known. A model may reveal assumptions about the system that were not fully appreciated. One of the earliest and most successful models is the Hodgkin-Huxley model of the action potential (Chap. 12). For their classic papers on the giant squid axon, they integrated the differential equations on a hand- powered mechanical calculator. Computers today are millions of times faster than those in the 1950s and it is now possible to simulate cortical neurons having thousands of dendritic compartments and dozens of different types of ions channels, and networks with thousands of interacting neurons. The complex dynamics of these networks is exceptionally difficult to predict without computational tools from computer science, and mathematical tools from dynamical systems theory. Butthereisanotherreasontodelveintothisbook.Computationalneuro- sciencealsoprovidesaframeworkforthinkingabouthowbrainmechanisms give rise to behavior. The links between facts and consequences are often subtle. We are told, for example, that lateral inhibition enhances contrast discontinuities, but without a quantitative model, such as that for the Limulus compound eye (Chap. 8), it is not at all obvious how this oc- curs, especially when the signals are varying in time as well as space. The jump from mechanism to behavior becomes even more difficult to understand for the cellular basis of learning and memory, where the memory of a single item can be distributed over millions of synapses in distant parts of the brain. Why read this book? Computational neuroscience is such a young field that ten years ago there were no good books for someone who was getting started. That has now changed and there are now several excellent textbooks available, but most of them focus on one type of model, such as Hodgkin-Huxley style models or abstract neural networks, or presume a high level of mathematical so- phistication.Thisbookgivesabalancedviewofthewiderangeofmodeling techniquesthatareavailable,inawaythatisaccessibletoawideaudience. Anotherreasonforreadingthisbookistoenjoytheplayfulnessthatthe author brings to a subject that can be dry and technical. His imaginative use of examples brings mathematical ideas to life. This is a book that will bring a smile to your face as well as inspire your imagination. Terrence J. Sejnowski Howard Hughes Medical Institute Salk Institute for Biological Studies University of California at San Diego Preface As a college student, I got interested in the big questions of brain science: theoriginoflanguageandthought,thenatureofmemory,theintegrationof sensationandaction,thesourceofconsciousness,andthemind-bodyproblem. Although I worked on the mind-body problem all through sophomore year, I didn’t arrive at a solution. After studying some more psychology, somebiologyandsomephysics,Itookawonderfulcomputersciencecourse. I decided that the way to understand the mind’s brain was to analyze it in detail as a computational device. When I graduated from college and looked at graduate programs. I ex- plained to interviewers that I wanted to apply computers to brain science. No one I spoke to in biomedicine or in computer science knew of anyone doing such things. In retrospect, such people were around, mostly located in departments of mathematics and physics. Because I couldn’t find what Iwaslookingfor,Iputoffmyresearchtrainingforalmostadecade.When Icameback,anewfieldhademergedandwasgainingrecognition.Shortly afterwards, this field, whose various branches and tributaries had gone by a variety of names, was dubbed Computational Neuroscience. AsIgotinvolvedinresearch,Ibegantoappreciatethatmassivetheoreti- calandinformationalframeworkshadtobe,andwerebeing,builtinorder to approach these grandly challenging problems that interested me. So, likemanyofmycolleagues,Ibecameabricklayer,laboriouslyaddingbrick upon brick to the wall, without always looking up or down. Later, given the opportunity to teach an undergraduate course, I was happy to have a chancetoreflectmoreexpansivelyontheproblems,techniques,aspirations and goals of my field. I found that the students asked basic questions that required me to peak out from under the residue of brick-dust and think about the architecture. When I started to teach my course, I reviewed the various textbooks in computational neuroscience, most of them brand new and many excellent. I realized that many of these new texts were committed to one particular angle or theoretical bias, and did not reveal the broad scope and wide interplay of ideas that I found so exciting. Additionally, they generally required too much math for most of the students in the class. In fact, most students came into the course either without strong math background, or without strong biology background, or without either. At first, I was concerned that I wouldbe unableto stuff in enough“remedial” material to bring everyone up to a point where they could understand what I was talking about. However, I found that I could cover a wide swath of topics by only teaching the little pieces that I needed out of each one. Additionally, for students who were not particularly comfortable with math, I made an effort to explain things a lot: first in metaphor, then in pictures, then directly in words, then in equations, and finally with computer simulations that brought it to life interactively. I have included theformersetofapproachesdirectlyinthisbook,andhavemadeavailable programs to allow a student to directly interact with, and alter, all of the figures, at least those that aren’t just pictures of things. As I wrote this book, I was sort of hoping that by the end of it I might finallyfigureouttheanswertothemind-bodyproblem.Alas,nosuchluck. But I’ve tried to convey some of the information that will be required for a solution. The rest is left as an exercise for the reader. Bill Lytton East Flatbush, NY June, 2002 Acknowledgments I first want to thank the folks at University of Wisconsin who helped me organize,launch,andteachthecourseonwhichthisbookisbased:Zoology 400,IntroductiontoComputationalNeuroscience.Inparticularanumberof students helped with the development of the course, the lecture notes, and the software: Kevin Hellman, Stephen Cowan, William Annis (Fig. 10.4), Ben Zhao, Winston Chang, Kim Wallace (Fig. 14.3), Adam Briska, Luke Greeley, Sam Sober, and Sam Williams. Version 1.0 of the software for this book, written in Perl CGI, was supported by the University of Wisconsin Office of Medical Education, Research, and Development. I particularly want to thank Mark Albanese and John Harting for their encouragement, and thank Jeff Hamblin, who wrote the software the first time around. A number of longtime collaborators engaged me in many helpful discus- sions that found their way into this book: Dan Uhlrich, Peter Lipton, Josh Chover,JayRosenbek,LewHaberly,SonnyYamasaki,andPeterVanKan. ManythankstotheenablersofSpringer-Verlag:PaulaCallaghan,Jenny Wolkowicki, and Frank Ganz. They were unfailingly helpful and friendly and gave me plenty of leeway. ThebookwaswritteninLatexusingEmacsrunningunderRedhatLinux. Asafanofsoftware,IparticularlywishtoacknowledgeNeuron,awonder- ful program that was used to produce all of the figures in the book. The softwareavailableforthisbookiswrittenintheNeuronsimulator.Ithank Mike Hines and Ted Carnevale for developing and supporting Neuron, and responding to my requests for language augmentations.