This is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as 'black boxes'.
Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research.
- The first comprehensive textbook on MATLAB with a focus for its application in neuroscience
- Problem based educational approach with many examples from neuroscience and cognitive psychology using real data
- Authors are award-winning educators with strong teaching experience
- Instructor's website with figurebank, additional problems and examples, solutions, etc.
Pascal Wallisch received his PhD from the University of Chicago, did postdoctoral work at the Center for Neural Science at New York University, and currently serves as a clinical assistant professor of Psychology at New York University. His research interests are at the intersection of Psychology and Neuroscience, specifically Cognitive and Computational Neuroscience. His current work focuses on motion perception, autism and the appraisal of film.
MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB is the first comprehensive teaching resource and textbook for the teaching of MATLAB in the Neurosciences and in Psychology. MATLAB is unique in that it can be used to learn the entire empirical and experimental process, including stimulus generation, experimental control, data collection, data analysis and modeling. Thus a wide variety of computational problems can be addressed in a single programming environment. The idea is to empower advanced undergraduates and beginning graduate students by allowing them to design and implement their own analytical tools. As students advance in their research careers, they will have achieved the fluency required to understand and adapt more specialized tools as opposed to treating them as "e;black boxes"e;. Virtually all computational approaches in the book are covered by using genuine experimental data that are either collected as part of the lab project or were collected in the labs of the authors, providing the casual student with the look and feel of real data. In some cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research. The first comprehensive textbook on MATLAB with a focus for its application in neuroscience Problem based educational approach with many examples from neuroscience and cognitive psychology using real data Authors are award-winning educators with strong teaching experience
Front Cover 1
MATLAB for Neuroscientists 4
Copyright Page 5
Contents 6
Preface 8
About the Authors 12
How to Use This Book 14
Part I: Fundamentals 16
Chapter 1: Introduction 18
Chapter 2: MATLAB Tutorial 22
Goal of This Chapter 22
Basic Concepts 22
Graphics and Visualization 42
Function and Scripts 47
Data Analysis 59
A Word on Function Handles 65
The Function Browser 68
Summary 69
MATLAB Functions, Commands, and Operators Covered in This Chapter 70
Part II: Data collection with matlab 72
Chapter 3: Visual Search and Pop Out 74
Goals of This Chapter 74
Background 74
Exercises 75
Project 82
MATLAB Functions, Commands, and Operators Covered in This Chapter 84
Chapter 4: Attention 86
Goals of This Chapter 86
Background 86
Exercises 87
Project 90
MATLAB Functions, Commands, and Operators Covered in This Chapter 92
Chapter 5: Psychophysics 94
Goals of This Chapter 94
Background 94
Exercises 96
Project 107
MATLAB Functions, Commands, and Operators Covered in This Chapter 111
Chapter 6: Signal Detection Theory 112
Goals of This Chapter 112
Background 112
Exercises 115
Project 127
MATLAB Functions, Commands, and Operators Covered in This Chapter 128
Part III: Data Analysis with MATLAB 130
Chapter 7: Frequency Analysis Part I: Fourier Decomposition 132
Goals of This Chapter 132
Background 132
Exercises 134
Project 138
MATLAB Functions, Commands, and Operators Covered in This Chapter 139
Chapter 8: Frequency Analysis Part II: Nonstationary Signals and Spectrograms 140
Goal of This Chapter 140
Background 140
Exercises 143
Project 145
MATLAB Functions, Commands, and Operators Covered in This Chapter 146
Chapter 9: Wavelets 148
Goals of This Chapter 148
Background 148
Exercises 154
Project 155
MATLAB Functions, Commands, and Operators Covered in This Chapter 155
Chapter 10: Convolution 156
Goals of This Chapter 156
Background 156
Exercises 159
Project 166
MATLAB Functions, Commands, and Operators Covered in This Chapter 167
Chapter 11: Introduction to Phase Plane Analysis 168
Goal of This Chapter 168
Background 168
Exercises 170
Project 176
MATLAB Functions, Commands, and Operators Covered in This Chapter 177
Chapter 12: Exploring the Fitzhugh-Nagumo Model 178
The Goal of This Chapter 178
Background 178
Exercises 180
Project 184
MATLAB Functions, Commands, and Operators Covered in This Chapter 186
Chapter 13: Neural Data Analysis: Encoding 188
Goals of This Chapter 188
Background 188
Exercises 188
Project 194
MATLAB Functions, Commands, and Operators Covered in This Chapter 196
Chapter 14: Principal Components Analysis 198
Goals of This Chapter 198
Background 198
Exercises 206
Project 206
MATLAB Functions, Commands, and Operators Covered in This Chapter 207
Chapter 15: Information Theory 208
Goals of This Chapter 208
Background 208
Exercises 216
Project 217
MATLAB Functions, Commands, and Operators Covered in This Chapter 217
Chapter 16: Neural Decoding Part I: Discrete Variables 218
Goals of This Chapter 218
Background 218
Exercises 224
Project 224
MATLAB Functions, Commands, and Operators Covered in This Chapter 225
Chapter 17: Neural Decoding Part II: Continuous Variables 226
Goals of This Chapter 226
Background 226
Exercises 232
Project 232
MATLAB Functions, Commands, and Operators Covered in This Chapter 233
Chapter 18: Functional Magnetic Imaging 234
Goals of This Chapter 234
Background 234
Exercises 237
Project 239
MATLAB Functions, Commands, and Operators Covered in This Chapter 240
Part IV: Data modeling with matlab 242
Chapter 19: Voltage-Gated Ion Channels 244
Goal of This Chapter 244
Background 244
Exercises 251
Project 253
MATLAB Functions, Commands, and Operators Covered in This Chapter 253
Chapter 20: Models of a Single Neuron 254
Goal of This Chapter 254
Background 254
Exercises 260
Project 261
MATLAB Functions, Commands, and Operators Covered in This Chapter 261
Chapter 21: Models of the Retina 262
Goal of This Chapter 262
Background 262
Exercises 266
Project 268
MATLAB Functions, Commands, and Operators Covered in This Chapter 268
Chapter 22: Simplified Model of Spiking Neurons 270
Goal of This Chapter 270
Background 270
Exercises 272
Project 275
MATLAB Functions, Commands, and Operators Covered in This Chapter 275
Chapter 23: Fitzhugh-Nagumo Model: Traveling Waves 276
Goals of This Chapter 276
Background 276
Exercises 277
Project 284
MATLAB Functions, Commands, and Operators Covered in This Chapter 289
Chapter 24: Decision Theory 290
Goals of This Chapter 290
Background 290
Exercises 291
Project 296
MATLAB Functions, Commands, and Operators Covered in This Chapter 296
Chapter 25: Markov Models 298
Goal of This Chapter 298
Background 298
Exercises 302
Project 305
MATLAB Functions, Commands, and Operators Covered in This Chapter 305
Chapter 26: Modeling Spike Trains as a Poisson Process 306
Goals of This Chapter 306
Background 306
Exercises 308
Project 312
MATLAB Functions, Commands, and Operators Covered in This Chapter 313
Chapter 27: Synaptic Transmission 314
Goals of This Chapter 314
Background 314
Exercises 315
Project: Combining Vesicular Release with Diffusion 321
MATLAB Functions, Commands, and Operators Covered in This Chapter 321
Chapter 28: Neural Networks Part I: Unsupervised Learning 322
Goals of This Chapter 322
Background 322
Trying out a neural network 328
Project 330
MATLAB Functions, Commands, and Operators Covered in This Chapter 332
Chapter 29: Neural Network Part II: Supervised Learning 334
Goals of this Chapter 334
Background 334
Exercises 337
Project 350
MATLAB Functions, Commands, and Operators Covered in this Chapter 352
Appendix A: Thinking in MATLAB 354
Alternatives to MATLAB 354
A Few Words about Precision 357
Appendix B: Linear Algebra Review 360
Matrix Dimensions 360
Multiplication 360
Addition 361
Transpose 361
Geometrical Interpretation of Matrix Multiplication 361
Determinant 364
Inverse 365
Eigenvalues and Eigenvectors 366
Eigendecomposition of a Matrix 367
Appendix C: Master Equation List 370
Chapter 6 370
Chapter 7 370
Chapter 8 371
Chapter 9 371
Chapter 10 372
Chapter 11 372
Chapter 12 373
Chapter 14 374
Chapter 15 374
Chapter 16 375
Chapter 17 375
Chapter 18 376
Chapter 19 376
Chapter 20 378
Chapter 21 380
Chapter 22 381
Chapter 23 382
Chapter 24 382
Chapter 26 383
Chapter 27 383
Chapter 28 384
Chapter 29 384
References 386
Index 394
Color Plates 400
Introduction
Publisher Summary
This chapter defines the challenge at the computational level, which is to determine what computational problem a neuron, neural circuit, or part of the brain is solving. The algorithmic level identifies the inputs, the outputs, their representational format, and the algorithm that takes the input representation and transforms it into an output representation. Finally, the implementational level identifies the neural “hardware” and biophysical mechanisms that underlie the algorithm which solves the problem. Today, this perspective has permeated not only cognitive neuroscience but also systems, cellular, and even molecular neuroscience. The chapter also describes the recent advances in software, as well as hardware, have instantiated scientific computing within the framework of a unified computational environment. One of these environments is provided by the MATLAB®software. MATLAB fulfills the requirements that are necessary to meet and overcome the challenges. In addition—and partly for these reasons—MATLAB has become the de facto standard of scientific computing in our field.
Neuroscience is at a critical juncture. In the past few decades, the essentially biological nature of the field has been infused by the tools provided by mathematics. At first, the use of mathematics was mostly methodological in nature—primarily aiding the analysis of data. Soon, this influence turned conceptual, framing the very issues that characterize modern neuroscience today. Naturally, this development has not remained uncontroversial. Some neurobiologists of yore resent what they perceive to be a hostile takeover of the field, as many quantitative methods applied to neurobiology were pioneered by nonbiologists with a background in physics, engineering, mathematics, statistics, and computer science. Their concerns are not entirely without merit. For example, Hubel and Wiesel (2004) warn of the faddish nature that the idol of “computation” has taken on, even likening it to a dangerous disease that has befallen the field and that we should overcome quickly in order to restore its health.
While these concerns are valid to some degree and while excesses do happen, we strongly believe that—all in all—the effect of mathematics in the neurosciences has been very positive. Moreover, we believe that our science is and will continue to be one that is computational at its very core. Historically, this notion stems in part from the influence that cognitive psychology has had in the study of the mind. Cognitive psychology and cognitive science, more generally, posited that the mind and, by extension, the brain should be viewed as an information processing device that receives inputs and transforms these inputs into intermediate representations which ultimately generate observable outputs. At the same time that cognitive science was taking hold in psychology in the 1950s and 1960s, computer science was developing beyond mere number crunching and considering the possibility that intelligence could be modeled computationally, leading to the birth of artificial intelligence. The information processing perspective, in turn, ultimately influenced the study of the brain and is best exemplified by an influential book by David Marr titled Vision, published in 1982. In that book, Marr proposed that vision and, more generally, the brain should be studied at three levels of analysis: the computational, algorithmic, and implementational levels. The challenge at the computational level is to determine what computational problem a neuron, neural circuit, or part of the brain is solving. The algorithmic level identifies the inputs, the outputs, their representational format, and the algorithm that takes the input representation and transforms it into an output representation. Finally, the implementational level identifies the neural “hardware” and biophysical mechanisms that underlie the algorithm which solves the problem. Today, this perspective has permeated not only cognitive neuroscience but also systems, cellular, and even molecular neuroscience.
Importantly, such a conceptualization of our field places chief importance on the issues surrounding scientific computing. For someone to participate in or even appreciate state of the art debates in modern neuroscience, that person has to be well versed in the language of computation. Of course, it is the task of education—if it is to be truly liberal—to enable students to do so. Yet, this poses a quite formidable challenge.
For most students interested in neuroscience, mathematics amounts to what is essentially a foreign language. Similarly, the language of scientific computing is typically as foreign to students as it is powerful. The prospects of learning both at the same time can be daunting and—at times—overwhelming. So what is a student or educator to do?
Immersion has been shown to be a powerful way to learn foreign languages (Genesee, 1985). Hence, it is imperative that students are using these languages as often as possible when facing a problem in the field. For immersion to work, the learning experience has to be positive, yielding useful results that solve some real or perceived problem. Unfortunately, the inherent complexity as well as the seemingly arcane formalisms that characterize both are usually very off-putting to students, requiring much effort with little tangible yield, reducing the likelihood of further voluntary immersion.
To break this catch-22, the utility of learning these languages has to be drastically increased while making the learning process more accessible and manageable at the same time, even during the learning process itself. As we alluded to previously, this is a tall order. Fortunately, there is a way out of this conundrum. Recent advances in software, as well as hardware, have instantiated scientific computing within the framework of a unified computational environment. One of these environments is provided by the MATLAB® software. For reasons that will become readily apparent in this book, MATLAB fulfills the requirements that are necessary to meet and overcome the challenges outlined earlier. In addition—and partly for these reasons—MATLAB has become the de facto standard of scientific computing in our field. More strongly, MATLAB really has become the lingua franca that all serious students of neuroscience are expected to understand in the very near future, if not already today.
This, in turn, introduces a new—albeit more tractable—problem. How does one teach MATLAB to a useful level of proficiency without making the study of MATLAB itself an additional problem and simply another chore for students? Overcoming this problem as a key to reaching the deeper goals of fluency in mathematics and scientific computing is a crucial goal of this book. We reason that a gentle introduction to MATLAB with a special emphasis on immediate results will computationally empower you to such a degree that the practice of MATLAB becomes self-sustaining by the end of the book. We carefully picked the content such that the result constitutes a confluence of ease (gradually increasing sophistication and complexity) and relevance. We are confident that at the end of the book you will be at a level where you will be able to venture out on your own, convinced of the utility of MATLAB as a tool as well as your abilities to harness this power henceforth. We have tested the various parts of the contents of this book on our students and believe that our approach has been successful. It is our sincere wish and hope that the material contained will be as beneficial to you as it was to those students.
With this in mind, we would like to outline two additional specific goals of this book. First, the material covered in the chapters to follow gives a MATLAB perspective on many topics within computational neuroscience across multiple levels of neuroscientific inquiry from decision-making and attentional mechanisms to retinal circuits and ion channels. It is well known that an active engagement with new material facilitates both understanding and long-time retention of said material. The secondary aim of this book is to acquire proficiency in programming using MATLAB while going through the chapters. If you are already proficient in MATLAB, you can go right to the chapters following the tutorial. For the rest, the tutorial chapter will provide a gentle introduction to the empowering qualities that the mastery of a language of scientific computing affords.
We take a project-based approach in each chapter so that you will be encouraged to write a MATLAB program that implements the ideas introduced in the chapter. Each chapter begins with background information related to a particular neuroscientific or psychological problem, followed by an introduction to the MATLAB concepts necessary to address that problem with sample code and output included in the text. You are invited to modify, expand, and improvise on these examples in a set of exercises. Finally, the project assignment introduced at the end of the chapter requires integrating the exercises. Most of the projects will involve genuine experimental data that are either collected as part of the project or were collected through experiments in research labs. In some rare cases, we use published data from classical papers to illustrate important concepts, giving you a computational understanding of critically important research.
In addition, solutions to exercises as well as executable code can be found in the online repository accompanying this book.
Finally, we would like to point out that we are well aware that there is more than one way to...
Erscheint lt. Verlag | 28.7.2010 |
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Sprache | englisch |
Themenwelt | Geisteswissenschaften ► Psychologie ► Allgemeine Psychologie |
Geisteswissenschaften ► Psychologie ► Biopsychologie / Neurowissenschaften | |
Geisteswissenschaften ► Psychologie ► Verhaltenstherapie | |
Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
Mathematik / Informatik ► Informatik ► Software Entwicklung | |
Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
Medizin / Pharmazie ► Medizinische Fachgebiete ► Neurologie | |
Naturwissenschaften ► Biologie ► Humanbiologie | |
Naturwissenschaften ► Biologie ► Zoologie | |
ISBN-10 | 0-08-092328-3 / 0080923283 |
ISBN-13 | 978-0-08-092328-4 / 9780080923284 |
Haben Sie eine Frage zum Produkt? |
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