Data Driven Computational Neuroscience

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Data-Driven Computational Neuroscience

Author : Concha Bielza
Publisher : Cambridge University Press
ISBN 13 : 1108639046
Total Pages : 734 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Data-Driven Computational Neuroscience by : Concha Bielza

Download or read book Data-Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

Advanced Data Analysis in Neuroscience

Author : Daniel Durstewitz
Publisher : Springer
ISBN 13 : 3319599763
Total Pages : 308 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Advanced Data Analysis in Neuroscience by : Daniel Durstewitz

Download or read book Advanced Data Analysis in Neuroscience written by Daniel Durstewitz and published by Springer. This book was released on 2017-09-15 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 explanatory 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

Highly Data-driven Mathematical Description of Single Neuron and Recurrent Network Activity

Author : Loreen Hertäg
Publisher :
ISBN 13 :
Total Pages : 224 pages
Book Rating : 4.:/5 (891 download)

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Book Synopsis Highly Data-driven Mathematical Description of Single Neuron and Recurrent Network Activity by : Loreen Hertäg

Download or read book Highly Data-driven Mathematical Description of Single Neuron and Recurrent Network Activity written by Loreen Hertäg and published by . This book was released on 2014 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data-Driven Modeling & Scientific Computation

Author : Jose Nathan Kutz
Publisher :
ISBN 13 : 0199660336
Total Pages : 657 pages
Book Rating : 4.1/5 (996 download)

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Book Synopsis Data-Driven Modeling & Scientific Computation by : Jose Nathan Kutz

Download or read book Data-Driven Modeling & Scientific Computation written by Jose Nathan Kutz and published by . This book was released on 2013-08-08 with total page 657 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.

Computational and Data-Driven Chemistry Using Artificial Intelligence

Author : Takashiro Akitsu
Publisher : Elsevier
ISBN 13 : 0128232722
Total Pages : 280 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Computational and Data-Driven Chemistry Using Artificial Intelligence by : Takashiro Akitsu

Download or read book Computational and Data-Driven Chemistry Using Artificial Intelligence written by Takashiro Akitsu and published by Elsevier. This book was released on 2021-10-08 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. Provides an accessible introduction to the current state and future possibilities for AI in chemistry Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields

Insights in computational neuroscience

Author : Si Wu
Publisher : Frontiers Media SA
ISBN 13 : 2832520502
Total Pages : 150 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Insights in computational neuroscience by : Si Wu

Download or read book Insights in computational neuroscience written by Si Wu and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computational Science — ICCS 2004

Author : Marian Bubak
Publisher : Springer Science & Business Media
ISBN 13 : 3540221166
Total Pages : 1376 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Computational Science — ICCS 2004 by : Marian Bubak

Download or read book Computational Science — ICCS 2004 written by Marian Bubak and published by Springer Science & Business Media. This book was released on 2004-05-26 with total page 1376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Conference on Computational Science (ICCS 2004) held in Krak ́ ow, Poland, June 6–9, 2004, was a follow-up to the highly successful ICCS 2003 held at two locations, in Melbourne, Australia and St. Petersburg, Russia; ICCS 2002 in Amsterdam, The Netherlands; and ICCS 2001 in San Francisco, USA. As computational science is still evolving in its quest for subjects of inves- gation and e?cient methods, ICCS 2004 was devised as a forum for scientists from mathematics and computer science, as the basic computing disciplines and application areas, interested in advanced computational methods for physics, chemistry, life sciences, engineering, arts and humanities, as well as computer system vendors and software developers. The main objective of this conference was to discuss problems and solutions in all areas, to identify new issues, to shape future directions of research, and to help users apply various advanced computational techniques. The event harvested recent developments in com- tationalgridsandnextgenerationcomputingsystems,tools,advancednumerical methods, data-driven systems, and novel application ?elds, such as complex - stems, ?nance, econo-physics and population evolution.

Computational Neuroscience

Author : Drozdstoy Stoyanov
Publisher : Springer Nature
ISBN 13 : 1071632302
Total Pages : 275 pages
Book Rating : 4.0/5 (716 download)

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Book Synopsis Computational Neuroscience by : Drozdstoy Stoyanov

Download or read book Computational Neuroscience written by Drozdstoy Stoyanov and published by Springer Nature. This book was released on 2023-05-10 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and comprehensive, Computational Neuroscience is a valuable tool for researchers in the psychiatry and mental health fields who want to learn more about ways to incorporate computational approaches into utility and validity of clinical methods.

Data-Driven Science and Engineering

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009098489
Total Pages : 615 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Spikes

Author : Fred Rieke
Publisher : MIT Press (MA)
ISBN 13 : 9780262181747
Total Pages : 418 pages
Book Rating : 4.1/5 (817 download)

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Book Synopsis Spikes by : Fred Rieke

Download or read book Spikes written by Fred Rieke and published by MIT Press (MA). This book was released on 1997 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for neurobiologists with an interest in mathematical analysis of neural data as well as the growing number of physicists and mathematicians interested in information processing by "real" nervous systems, Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory.

Principles of Computational Modelling in Neuroscience

Author : David Sterratt
Publisher : Cambridge University Press
ISBN 13 : 1108483143
Total Pages : 553 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis Principles of Computational Modelling in Neuroscience by : David Sterratt

Download or read book Principles of Computational Modelling in Neuroscience written by David Sterratt and published by Cambridge University Press. This book was released on 2023-10-05 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to use computational modelling techniques to understand the nervous system at all levels, from ion channels to networks.

The Computational Brain

Author : Patricia Smith Churchland
Publisher : MIT Press
ISBN 13 : 9780262531207
Total Pages : 564 pages
Book Rating : 4.5/5 (312 download)

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Book Synopsis The Computational Brain by : Patricia Smith Churchland

Download or read book The Computational Brain written by Patricia Smith Churchland and published by MIT Press. This book was released on 1992 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a précis of neurobiological techniques."--Jacket.

Computational Explorations in Cognitive Neuroscience

Author : Randall C. O'Reilly
Publisher : MIT Press
ISBN 13 : 9780262650540
Total Pages : 540 pages
Book Rating : 4.6/5 (55 download)

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Book Synopsis Computational Explorations in Cognitive Neuroscience by : Randall C. O'Reilly

Download or read book Computational Explorations in Cognitive Neuroscience written by Randall C. O'Reilly and published by MIT Press. This book was released on 2000-08-28 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience. The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.

Dynamic Mode Decomposition

Author : J. Nathan Kutz
Publisher : SIAM
ISBN 13 : 1611974496
Total Pages : 241 pages
Book Rating : 4.6/5 (119 download)

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Book Synopsis Dynamic Mode Decomposition by : J. Nathan Kutz

Download or read book Dynamic Mode Decomposition written by J. Nathan Kutz and published by SIAM. This book was released on 2016-11-23 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven dynamical systems is a burgeoning field?it connects how measurements of nonlinear dynamical systems and/or complex systems can be used with well-established methods in dynamical systems theory. This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition (DMD) is an innovative tool for integrating data with dynamical systems theory. The DMD has deep connections with traditional dynamical systems theory and many recent innovations in compressed sensing and machine learning. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems, the first book to address the DMD algorithm, presents a pedagogical and comprehensive approach to all aspects of DMD currently developed or under development; blends theoretical development, example codes, and applications to showcase the theory and its many innovations and uses; highlights the numerous innovations around the DMD algorithm and demonstrates its efficacy using example problems from engineering and the physical and biological sciences; and provides extensive MATLAB code, data for intuitive examples of key methods, and graphical presentations.

Industrial Applications of Machine Learning

Author : Pedro Larrañaga
Publisher : CRC Press
ISBN 13 : 135112837X
Total Pages : 349 pages
Book Rating : 4.3/5 (511 download)

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Book Synopsis Industrial Applications of Machine Learning by : Pedro Larrañaga

Download or read book Industrial Applications of Machine Learning written by Pedro Larrañaga and published by CRC Press. This book was released on 2018-12-12 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka

Analysis of Neural Data

Author : Robert E. Kass
Publisher : Springer
ISBN 13 : 1461496020
Total Pages : 663 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Analysis of Neural Data by : Robert E. Kass

Download or read book Analysis of Neural Data written by Robert E. Kass and published by Springer. This book was released on 2014-07-08 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Case Studies in Neural Data Analysis

Author : Mark A. Kramer
Publisher : MIT Press
ISBN 13 : 0262529378
Total Pages : 385 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Case Studies in Neural Data Analysis by : Mark A. Kramer

Download or read book Case Studies in Neural Data Analysis written by Mark A. Kramer and published by MIT Press. This book was released on 2016-11-04 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website.