Provides a unified treatment of analytical methods that have become essential for contemporary researchers
Examples drawn from the literature are included throughout this text, ranging from electrophysiology, neuroimaging and behavior
Recommended prior knowledge is high-school level mathematics
Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).
Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.
Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.
"This is an outstanding book, that fills a real need. Assuming no background in statistics, it covers the data analysis methods neuroscientists need to know, from standard material like hypothesis tests, to specialized methods that have recently found use in our field. It has the detail and insight needed for those developing their own statistical methods. And for the working neurobiologist it has plenty of practical tricks, tips, and examples, coming straight from the experts. This book is a must for anyone serious about quantitative analysis in neuroscience." (Kenneth D. Harris, Professor of Quantitative Neuroscience, University College London)
"Analysis of Neural Data is a thorough, authoritative textbook on the fastest growing statistical field. All relevant topics are covered in depth with examples from the literature and thoughtful comments. Particularly welcome is the discussion of multivariate statistics, time series and Bayesian methods, topics frequently encountered in neuroscience research but infrequently discussed in standard statistics textbooks. A highly readable, useful and commendable textbook!" (Apostolos P. Georgopoulos, Regents Professor of Neuroscience, University of Minnesota)
"This book is a unique and valuable resource for any scientist who wants to approach neural data analysis in a rigorous fashion, or to gain a broad overview of modern statistical concepts and approaches. While the book is an eminently practical guide, it is far from a cookbook. The individual who is willing to invest the time to read it will be deeply rewarded not only with everyday methodological guidance, but also, with a comprehensive understanding of the mathematical foundations of statistics. The first chapter, in which the authors lucidly present a perspective on what statistics has to offer, should be required reading for all neuroscientists – or at least, all who care about data. The authors have met the difficult and competing challenges of creating a book that is both practical and rigorous. To do this, they combine a crisp writing style with a number of helpful strategies, including the use of many carefully-chosen examples from the neuroscience literature, and vivid reminders of the difference between the world of mathematical objects and the world of data. Mathematical concepts that are typically omitted from elementary texts are not avoided, but are discussed in a way that makes their relevance evident...The book is a one-of-a-kind resource that combines practicality, rigor, and accessibility; it is a book that was sorely needed and is an extremely valuable reference." (Jonathan D. Victor, Fred Plum Professor, Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medical College)
"Analysis of Neural Data provides an invaluable guide for neuroscientists seeking to summarize and interpret their data. The authors -- leading statisticians who have developed and applied many of the methods they describe themselves – are also outstanding teachers, and the treatment they provide is at once accessible, authoritative, comprehensive, and up-to-date. The book provides a carefully structured introduction to statistical methods for students at the beginning of their research careers as well as a treatment of several advanced methods that will be of value to practicing researchers." (James L. McClelland, Lucie Stern Professor in the Social Sciences, Director, Center for Mind, Brain and Computation, Stanford University)
"Written by eminent statisticians, this book covers a range of topics from basic mathematics to state-of-the-art statistical analyses of neural data. Researchers conducting experiments will learn the principles of data analysis and will begin analyzing data using the methods provided. Theoreticians will be introduced to more than 100 intriguing experiments that will teach them to form persuasive interpretations. Analysis of Neural Data should become a standard reference for neuroscience research." (Shigeru Shinomoto, Department of Physics, Kyoto University)
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
Introduction.- Exploring Data.- Probability and Random Variables.- Random Vectors.- Important Probability Distributions.- Sequences of Random Variables.- Estimation and Uncertainty.- Estimation in Theory and Practice.- Uncertainty and the Bootstrap.- Statistical Significance.- General Methods for Testing Hypotheses.- Linear Regression.- Analysis of Variance.- Generalized Regression.- Nonparametric Regression.- Bayesian Methods.- Multivariate Analysis.- Time Series.- Point Processes.- Appendix: Mathematical Background.- Example Index.- Index.- Bibliography.
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data.
This book provides a unified review of analytical methods for neural data that have become essential for contemporary researchers. Illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology to neuroimaging to behavior.
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
Introduction.- Exploring Data.- Probability and Random Variables.- Random Vectors.- Important Probability Distributions.- Sequences of Random Variables.- Estimation and Uncertainty.- Estimation in Theory and Practice.- Uncertainty and the Bootstrap.- Statistical Significance.- General Methods for Testing Hypotheses.- Linear Regression.- Analysis of Variance.- Generalized Regression.- Nonparametric Regression.- Bayesian Methods.- Multivariate Analysis.- Time Series.- Point Processes.- Appendix: Mathematical Background.- Example Index.- Index.- Bibliography.
This is an outstanding book, that fills a real need. Assuming no background in statistics, it covers the data analysis methods neuroscientists need to know, from standard material like hypothesis tests, to specialized methods that have recently found use in our field. It has the detail and insight needed for those developing their own statistical methods. And for the working neurobiologist it has plenty of practical tricks, tips, and examples, coming straight from the experts. This book is a must for anyone serious about quantitative analysis in neuroscience.
Kenneth D. Harris , Professor of Quantitative Neuroscience, University College London
Analysis of Neural Data is a thorough, authoritative textbook on the fastest growing statistical field. All relevant topics are covered in depth with examples from the literature and thoughtful comments. Particularly welcome is the discussion of multivariate statistics, time series and Bayesian methods, topics frequently encountered in neuroscience research but infrequently discussed in standard statistics textbooks. A highly readable, useful and commendable textbook!
Apostolos P. Georgopoulos , Regents Professor of Neuroscience, University of Minnesota
This book is a unique and valuable resource for any scientist who wants to approach neural data analysis in a rigorous fashion, or to gain a broad overview of modern statistical concepts and approaches. While the book is an eminently practical guide, it is far from a cookbook. The individual who is willing to invest the time to read it will be deeply rewarded not only with everyday methodological guidance, but also, with a comprehensive understanding of the mathematical foundations of statistics. The first chapter, in which the authors lucidly present a perspective on what statistics has to offer, should be required reading for all neuroscientists or at least, all who care about data. The authors have met the difficult and competing challenges of creating a book that is both practical and rigorous. To do this, they combine a crisp writing style with a number of helpful strategies, including the use of many carefully-chosen examples from the neuroscience literature, and vivid reminders of the difference between the world of mathematical objects and the world of data. Mathematical concepts that are typically omitted from elementary texts are not avoided, but are discussed in a way that makes their relevance evident...The book is a one-of-a-kind resource that combines practicality, rigor, and accessibility; it is a book that was sorely needed and is an extremely valuable reference.
Jonathan D. Victor , Fred Plum Professor, Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medical College
Analysis of Neural Data provides an invaluable guide for neuroscientists seeking to summarize and interpret their data. The authors -- leading statisticians who have developed and applied many of the methods they describe themselves are also outstanding teachers, and the treatment they provide is at once accessible, authoritative, comprehensive, and up-to-date. The book provides a carefully structured introduction to statistical methods for students at the beginning of their research careers as well as a treatment of several advanced methods that will be of value to practicing researchers.
James L. McClelland , Lucie Stern Professor in the Social Sciences, Director, Center for Mind, Brain and Computation, Stanford University
Written by eminent statisticians, this book covers a range of topics from basic mathematics to state-of-the-art statistical analyses of neural data. Researchers conducting experiments will learn the principles of data analysis and will begin analyzing data using the methods provided. Theoreticians will be introduced to more than 100 intriguing experiments that will teach them to form persuasive interpretations. Analysis of Neural Data should become a standard reference for neuroscience research.
Shiger
Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).
Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.
Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.
Über den Autor
Robert E. (Rob) Kass is Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition at Carnegie Mellon University. Since 2001 his research has been devoted to statistical methods in neuroscience. Together with Emery Brown he has organized the highly successful series of international meetings, Statistical Analysis of Neural Data (SAND).
Uri T. Eden is Associate Professor in the Department of Mathematics and Statistics at Boston University. He received his Ph.D. in the Harvard/MIT Medical Engineering and Medical Physics program in the Health Sciences and Technology Department. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, using methods related to model identi cation, statistical inference, signal processing, and stochastic estimation and control.
Emery N. Brown is Edward Hood Taplin Professor of Medical Engineering, Professor of Computational Neuroscience, and Associate Director of the Institute of Medical Engineering and Science at MIT; he is also the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital. He is both a statistician and an anesthesiologist. Since 1998 his research has focused on neural information processing, and his experimental work characterizes the way anesthetic drugs act in the brain to create the state of general anesthesia.
Inhaltsverzeichnis
Introduction.- Exploring Data.- Probability and Random Variables.- Random Vectors.- Important Probability Distributions.- Sequences of Random Variables.- Estimation and Uncertainty.- Estimation in Theory and Practice.- Uncertainty and the Bootstrap.- Statistical Significance.- General Methods for Testing Hypotheses.- Linear Regression.- Analysis of Variance.- Generalized Regression.- Nonparametric Regression.- Bayesian Methods.- Multivariate Analysis.- Time Series.- Point Processes.- Appendix: Mathematical Background.- Example Index.- Index.- Bibliography.
Klappentext
Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.
Provides a unified treatment of analytical methods that have become essential for contemporary researchers
Examples drawn from the literature are included throughout this text, ranging from electrophysiology, neuroimaging and behavior
Recommended prior knowledge is high-school level mathematics
