MATLAB for Neuroscientists

MATLAB for Neuroscientists
Author: Pascal Wallisch,Michael E. Lusignan,Marc D. Benayoun,Tanya I. Baker,Adam Seth Dickey,Nicholas G. Hatsopoulos
Publsiher: Academic Press
Total Pages: 570
Release: 2014-01-09
ISBN 10: 0123838371
ISBN 13: 9780123838377
Language: EN, FR, DE, ES & NL

MATLAB for Neuroscientists Book Review:

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

MATLAB for Neuroscientists

MATLAB for Neuroscientists
Author: Pascal Wallisch,Michael E. Lusignan,Marc D. Benayoun,Tanya I. Baker,Adam Seth Dickey,Nicholas G. Hatsopoulos
Publsiher: Academic Press
Total Pages: 400
Release: 2010-07-28
ISBN 10: 9780080923284
ISBN 13: 0080923283
Language: EN, FR, DE, ES & NL

MATLAB for Neuroscientists Book Review:

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

MATLAB for Neuroscientists

MATLAB for Neuroscientists
Author: Anonim
Publsiher: Academic Press
Total Pages: 384
Release: 2009
ISBN 10: 9780123745514
ISBN 13: 0123745519
Language: EN, FR, DE, ES & NL

MATLAB for Neuroscientists Book Review:

Matlab is the accepted standard for scientific computing, used globally in virtually all Neuroscience and Cognitive Psychology laboratories. For instance, SPM, the most used software for the analysis and manipulation of fMRI images in research and clinical practice is fully programmed in matlab, and its use of the possibility to allow for sophisticated software modules to be freely added to the software has established it as the by far dominant software in the field. Many universities now offer, or are beginning to offer matlab introductory courses in their neuroscience and psychology programs. Nevertheless, so far there hasn't been a textbook specific to this market, and the use of the plethora of existing engineering focused Matlab textbooks is notoriously difficult for teaching the package in those environments. 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 rare cases, published data from classical papers are used to illustrate important concepts, giving students a computational understanding of critically important research. The ability to effectively use computers in research is necessary in an academic environment that is increasingly focused on quantitative issues. Matlab represents an ideal language of scientific computing. It is based on powerful linear algebra structures which lend themselves to empirical problems on the one hand, while at the same time allowing the student to make rapid problem-oriented progress (particularly in terms of visualization of data points) without having to lose focus by worrying too much about memory allocation and other "plumbing" minutiae as would be required in other, more low-level programming languages such as C or C++. Currently, there are several books that provide introductions to Matlab that are either too generic and fundamental or too irrelevant for neuroscientists and cognitive psychologists who typically face a very circumscribed range of problems in data collection, data analysis and signal processing. Some non-book tutorials and primers that are in use in the community are typically out of date. Matlab versions are usually not backwards compatible. Many commands and functions used in older tutorials and primers, such as "flops" won't work in current versions of Matlab, necessitating a book that is timely and up-to-date. The complete lack of a relevant resource in this area, combined with a clearly felt need for such a text provided the primary and initial impetus for this project. The authors provide such a dearly needed resource adapting and pooling materials that developed for and used in highly rated courses involving the use of Matlab in Neuroscience at the University of Chicago.

MATLAB for Neuroscientists 2nd Edition

MATLAB for Neuroscientists  2nd Edition
Author: Pascal Wallisch,Michael Lusignan,Marc Benayoun,Tanya Baker,Adam Dickey,Nicholas Hatsopoulos
Publsiher: Anonim
Total Pages: 570
Release: 2014
ISBN 10:
ISBN 13: OCLC:1105766137
Language: EN, FR, DE, ES & NL

MATLAB for Neuroscientists 2nd Edition Book Review:

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.

Signal Processing for Neuroscientists

Signal Processing for Neuroscientists
Author: Wim van Drongelen
Publsiher: Anonim
Total Pages: 308
Release: 2007
ISBN 10: 9780123708670
ISBN 13: 0123708672
Language: EN, FR, DE, ES & NL

Signal Processing for Neuroscientists Book Review:

Signal Processing for Neuroscientists introduces analysis techniques primarily aimed at neuroscientists and biomedical engineering students with a reasonable but modest background in mathematics, physics, and computer programming. The focus of this text is on what can be considered the 'golden trio' in the signal processing field: averaging, Fourier analysis, and filtering. Techniques such as convolution, correlation, coherence, and wavelet analysis are considered in the context of time and frequency domain analysis. The whole spectrum of signal analysis is covered, ranging from data acquisition to data processing; and from the mathematical background of the analysis to the practical application of processing algorithms. Overall, the approach to the mathematics is informal with a focus on basic understanding of the methods and their interrelationships rather than detailed proofs or derivations. One of the principle goals is to provide the reader with the background required to understand the principles of commercially available analyses software, and to allow him/her to construct his/her own analysis tools in an environment such as MATLAB®. * Multiple color illustrations are integrated in the text * Includes an introduction to biomedical signals, noise characteristics, and recording techniques * Basics and background for more advanced topics can be found in extensive notes and appendices * A Companion Website hosts the MATLAB scripts and several data files: http://www.elsevierdirect.com/companion.jsp?ISBN=9780123708670

MATLAB for Brain and Cognitive Scientists

MATLAB for Brain and Cognitive Scientists
Author: Mike X Cohen
Publsiher: MIT Press
Total Pages: 576
Release: 2017-05-12
ISBN 10: 0262035820
ISBN 13: 9780262035828
Language: EN, FR, DE, ES & NL

MATLAB for Brain and Cognitive Scientists Book Review:

An introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.

An Introductory Course in Computational Neuroscience

An Introductory Course in Computational Neuroscience
Author: Paul Miller
Publsiher: MIT Press
Total Pages: 408
Release: 2018-10-09
ISBN 10: 0262347563
ISBN 13: 9780262347563
Language: EN, FR, DE, ES & NL

An Introductory Course in Computational Neuroscience Book Review:

A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.

Neural Data Science

Neural Data Science
Author: Erik Lee Nylen,Pascal Wallisch
Publsiher: Academic Press
Total Pages: 368
Release: 2017-02-24
ISBN 10: 012804098X
ISBN 13: 9780128040980
Language: EN, FR, DE, ES & NL

Neural Data Science Book Review:

A Primer with MATLAB® and PythonTM present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility. Includes discussions of both MATLAB and Python in parallel Introduces the canonical data analysis cascade, standardizing the data analysis flow Presents tactics that strategically, tactically, and algorithmically help improve the organization of code

Analyzing Neural Time Series Data

Analyzing Neural Time Series Data
Author: Mike X Cohen
Publsiher: MIT Press
Total Pages: 600
Release: 2014-01-17
ISBN 10: 0262019876
ISBN 13: 9780262019873
Language: EN, FR, DE, ES & NL

Analyzing Neural Time Series Data Book Review:

A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.

Case Studies in Neural Data Analysis

Case Studies in Neural Data Analysis
Author: Mark A. Kramer,Uri T. Eden
Publsiher: MIT Press
Total Pages: 384
Release: 2016-11-04
ISBN 10: 0262529378
ISBN 13: 9780262529372
Language: EN, FR, DE, ES & NL

Case Studies in Neural Data Analysis Book Review:

A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data.

MATLAB for Behavioral Scientists Second Edition

MATLAB for Behavioral Scientists  Second Edition
Author: David A. Rosenbaum,Jonathan Vaughan,Brad Wyble
Publsiher: Routledge
Total Pages: 400
Release: 2014-07-17
ISBN 10: 1136281088
ISBN 13: 9781136281082
Language: EN, FR, DE, ES & NL

MATLAB for Behavioral Scientists Second Edition Book Review:

Written specifically for those with no prior programming experience and minimal quantitative training, this accessible text walks behavioral science students and researchers through the process of programming using MATLAB. The book explores examples, terms, and programming needs relevant to those in the behavioral sciences and helps readers perform virtually any computational function in solving their research problems. Principles are illustrated with usable code. Each chapter opens with a list of objectives followed by new commands required to accomplish those goals. These objectives also serve as a reference to help readers easily relocate a section of interest. Sample code and output and chapter problems demonstrate how to write a program and explore a model so readers can see the results obtained using different equations and values. A web site provides solutions to selected problems and the book’s program code output and examples so readers can manipulate them as needed. The outputs on the website have color, motion, and sound. Highlights of the new edition include: •Updated to reflect changes in the most recent version of MATLAB, including special tricks and new functions. •More information on debugging and common errors and more basic problems in the rudiments of MATLAB to help novice users get up and running more quickly. •A new chapter on Psychtoolbox, a suite of programs specifically geared to behavioral science research. •A new chapter on Graphical User Interfaces (GUIs) for user-friendly communication. •Increased emphasis on pre-allocation of memory, recursion, handles, and matrix algebra operators. The book opens with an overview of what is to come and tips on how to write clear programs followed by pointers for interacting with MATLAB, including its commands and how to read error messages. The matrices chapter reviews how to store and access data. Chapter 4 examines how to carry out calculations followed by a review of how to perform various actions depending on the conditions. The chapter on input and output demonstrates how to design programs to create dialogs with users (e.g., participants in studies) and read and write data to and from external files. Chapter 7 reviews the data types available in MATLAB. Readers learn how to write a program as a stand-alone module in Chapter 8. In Chapters 9 and 10 readers learn how to create line and bar graphs or reshape images. Readers learn how to create animations and sounds in Chapter 11. The book concludes with tips on how to use MATLAB with applications such as GUIs and Psychtoolbox. Intended as a primary text for Matlab courses for advanced undergraduate and/or graduate students in experimental and cognitive psychology and/or neuroscience as well as a supplementary text for labs in data (statistical) analysis, research methods, and computational modeling (programming), the book also appeals to individual researchers in these disciplines who wish to get up and running in MATLAB.

The Fractal Geometry of the Brain

The Fractal Geometry of the Brain
Author: Antonio Di Ieva
Publsiher: Springer
Total Pages: 585
Release: 2016-08-03
ISBN 10: 1493939955
ISBN 13: 9781493939954
Language: EN, FR, DE, ES & NL

The Fractal Geometry of the Brain Book Review:

Reviews the most intriguing applications of fractal analysis in neuroscience with a focus on current and future potential, limits, advantages, and disadvantages. Will bring an understanding of fractals to clinicians and researchers also if they do not have a mathematical background, and will serve as a good tool for teaching the translational applications of computational models to students and scholars of different disciplines. This comprehensive collection is organized in four parts: (1) Basics of fractal analysis; (2) Applications of fractals to the basic neurosciences; (3) Applications of fractals to the clinical neurosciences; (4) Analysis software, modeling and methodology.

Programming Behavioral Experiments with MATLAB and Psychtoolbox

Programming Behavioral Experiments with MATLAB and Psychtoolbox
Author: Erman Misirlisoy
Publsiher: Taylor & Francis
Total Pages: 70
Release: 2016-11-10
ISBN 10: 1317205634
ISBN 13: 9781317205630
Language: EN, FR, DE, ES & NL

Programming Behavioral Experiments with MATLAB and Psychtoolbox Book Review:

Human behavior is fascinating so it’s no surprise that psychologists and neuroscientists spend their lives designing rigorous experiments to understand it. MATLAB is one of the most widely used pieces of software for designing and running behavioral experiments, and it opens up a world of quick and flexible experiment programming. This book offers a step-by-step guide to using MATLAB with Psychtoolbox to create customisable experiments. Its pocket size and simple language allow you to get straight to the point and help you to learn fast in order to complete your work in great time. In nine simple steps, it guides you all the way from setting parameters for your experiment to analysing the output. Gone are the daunting days of working through hundreds of irrelevant and complicated documents, as in this handy book, Erman Misirlisoy coaxes you in the right direction with his friendly and encouraging tricks and tips. If you want to learn how to develop your own experiments to collect and analyse behavioral data, then this book is a must-read. Whether you are a student in experimental psychology, a researcher in cognitive neuroscience, or simply someone who wants to run behavioral tasks on your friends for fun, this book will offer you the skills to succeed.

Python in Neuroscience

Python in Neuroscience
Author: Eilif Muller,James A. Bednar,Markus Diesmann,Marc-Oliver Gewaltig,Michael Hines,Andrew P. Davison
Publsiher: Frontiers Media SA
Total Pages: 329
Release: 2015-07-23
ISBN 10: 2889196089
ISBN 13: 9782889196081
Language: EN, FR, DE, ES & NL

Python in Neuroscience Book Review:

Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.

Analysis of Neural Data

Analysis of Neural Data
Author: Robert E. Kass,Uri T. Eden,Emery N. Brown
Publsiher: Springer
Total Pages: 648
Release: 2014-07-08
ISBN 10: 1461496020
ISBN 13: 9781461496021
Language: EN, FR, DE, ES & NL

Analysis of Neural Data Book Review:

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.

Independent Component Analysis

Independent Component Analysis
Author: Dr. James V. Stone
Publsiher: MIT Press
Total Pages: 193
Release: 2004
ISBN 10: 9780262693158
ISBN 13: 0262693151
Language: EN, FR, DE, ES & NL

Independent Component Analysis Book Review:

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation.

Advanced Data Analysis in Neuroscience

Advanced Data Analysis in Neuroscience
Author: Daniel Durstewitz
Publsiher: Springer
Total Pages: 292
Release: 2017-09-15
ISBN 10: 3319599763
ISBN 13: 9783319599762
Language: EN, FR, DE, ES & NL

Advanced Data Analysis in Neuroscience Book Review:

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck

An Introduction to MATLAB for Behavioral Researchers

An Introduction to MATLAB for Behavioral Researchers
Author: Christopher R. Madan
Publsiher: SAGE Publications
Total Pages: 280
Release: 2013-12-18
ISBN 10: 1483323242
ISBN 13: 9781483323244
Language: EN, FR, DE, ES & NL

An Introduction to MATLAB for Behavioral Researchers Book Review:

MATLAB is a powerful data analysis program, but many behavioral science researchers find it too daunting to learn and use. An Introduction to MATLAB for Behavioral Researchers by Christopher R. Madan is an easy-to-understand, hands-on guide for behavioral researchers who have no prior programming experience. Written in a conversational and non-intimidating style, the author walks students—step by step—through analyzing real experimental data. Topics covered include the basics of programming, the implementation of simple behavioral analyses, and how to make publication-ready figures. More advanced topics such as pseudo-randomization of trial sequences to meet specified criteria and working with psycholinguistic data are also covered. Interesting behavioral science examples and datasets from published studies, such as visualizing fixation patterns in eye-tracking studies and animal search behavior in two-dimensional space, help develop an intuition for data analysis, which is essential and can only be developed when working with real research problems and real data.

Network Neuroscience

Network Neuroscience
Author: Flavio Fröhlich
Publsiher: Academic Press
Total Pages: 482
Release: 2016-09-20
ISBN 10: 0128015861
ISBN 13: 9780128015865
Language: EN, FR, DE, ES & NL

Network Neuroscience Book Review:

Studying brain networks has become a truly interdisciplinary endeavor, attracting students and seasoned researchers alike from a wide variety of academic backgrounds. What has been lacking is an introductory textbook that brings together the different fields and provides a gentle introduction to the major concepts and findings in the emerging field of network neuroscience. Network Neuroscience is a one-stop-shop that is of equal use to the neurobiologist, who is interested in understanding the quantitative methods employed in network neuroscience, and to the physicist or engineer, who is interested in neuroscience applications of mathematical and engineering tools. The book spans 27 chapters that cover everything from individual cells all the way to complex network disorders such as depression and autism spectrum disorders. An additional 12 toolboxes provide the necessary background for making network neuroscience accessible independent of the reader’s background. Dr. Flavio Frohlich (www.networkneuroscientist.org) wrote this book based on his experience of mentoring dozens of trainees in the Frohlich Lab, from undergraduate students to senior researchers. The Frohlich lab (www.frohlichlab.org) pursues a unique and integrated vision that combines computer simulations, animal model studies, human studies, and clinical trials with the goal of developing novel brain stimulation treatments for psychiatric disorders. The book is based on a course he teaches at UNC that has attracted trainees from many different departments, including neuroscience, biomedical engineering, psychology, cell biology, physiology, neurology, and psychiatry. Dr. Frohlich has consistently received rave reviews for his teaching. With this book he hopes to make his integrated view of neuroscience available to trainees and researchers on a global scale. His goal is to make the book the training manual for the next generation of (network) neuroscientists, who will be fusing biology, engineering, and medicine to unravel the big questions about the brain and to revolutionize psychiatry and neurology. Easy-to-read, comprehensive introduction to the emerging field of network neuroscience Includes 27 chapters packed with information on topics from single neurons to complex network disorders such as depression and autism Features 12 toolboxes serve as primers to provide essential background knowledge in the fields of biology, mathematics, engineering, and physics

From Neuron to Cognition via Computational Neuroscience

From Neuron to Cognition via Computational Neuroscience
Author: Michael A. Arbib,James J. Bonaiuto
Publsiher: MIT Press
Total Pages: 808
Release: 2016-11-04
ISBN 10: 0262335271
ISBN 13: 9780262335270
Language: EN, FR, DE, ES & NL

From Neuron to Cognition via Computational Neuroscience Book Review:

A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille