MATLAB for Neuroscientists

An Introduction to Scientific Computing in MATLAB

Pascal Wallisch, Michael E. Lusignan, Marc D. Benayoun, Tanya I. Baker, Adam Seth Dickey, Nicholas G. Hatsopoulos

• Publisher: Academic Press
• Year: 2014
• eBook ISBN: 9780123838377
• Edition: 2
• Topic: Psychology
• Subtopic: Cognitive Psychology & Cognition
• Index: Psychology

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. - The first complete volume on MATLAB focusing on neuroscience and psychology applications - Problem-based approach with many examples from neuroscience and cognitive psychology using real data - Illustrated in full color throughout - Careful tutorial approach, by authors who are award-winning educators with strong teaching experience

Part I

Fundamentals

Outline

Chapter 1 Introduction

Chapter 2 MATLAB Tutorial

Chapter 3 Mathematics and Statistics Tutorial

Chapter 4 Programming Tutorial

Chapter 5 Visualization and Documentation Tutorial

Chapter 1

Introduction

In the past few decades, the essentially biological nature of the study of neuroscience 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. This development has not remained uncontroversial; some neurobiologists 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. While these concerns are valid to some degree, and while excesses do happen, the authors strongly believe that—all in all—the effect of mathematics in the neurosciences has been very positive, and that neuroscience is and will continue to be a discipline that is computational at its very core.

Keywords

neuroscience; MATLAB; cognitive psychology; cognitive science; mathematics

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 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. The reason for this is that—as pointed out by Konrad Körding (http://www.nature.com/news/neuroscience-solving-the-brain-1.13382)—the human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime. That is a staggering number, given that Hubble has been in operation for well over 23 years and generates more than 100 GB of data each week. Eventually, we will develop experimental methods that will fully tap this wellspring of data. We expect that computational methods to tackle this data will be developed in parallel. Put differently, not only is a computational perspective on neuroscience here to stay, we are likely only at the very beginning of this process. 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 information processing devices that receive inputs and transform these inputs into intermediate representations that 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 circu...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface to the Second Edition
• Preface to the First Edition
• About the Authors
• How to Use this Book
• Part I: Fundamentals
• Part II: Data Collection with MATLAB
• Part III: Data Analysis with MATLAB
• Part IV: Data Modeling with MATLAB
• Appendix A. Creating Publication-Quality Figures
• Appendix B. Relevant Toolboxes
• References
• Index