Computational Modeling Methods For Neuroscientists

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Computational Modeling Methods for Neuroscientists

Author : Erik De Schutter
Publisher : National Geographic Books
ISBN 13 : 0262013274
Total Pages : 0 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Computational Modeling Methods for Neuroscientists by : Erik De Schutter

Download or read book Computational Modeling Methods for Neuroscientists written by Erik De Schutter and published by National Geographic Books. This book was released on 2009-09-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to computational modeling methods in neuroscience, covering a range of modeling scales from molecular reactions to large neural networks. This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications. Contributors Pablo Achard, Haroon Anwar, Upinder S. Bhalla, Michiel Berends, Nicolas Brunel, Ronald L. Calabrese, Brenda Claiborne, Hugo Cornelis, Erik De Schutter, Alain Destexhe, Bard Ermentrout, Kristen Harris, Sean Hill, John R. Huguenard, William R. Holmes, Gwen Jacobs, Gwendal LeMasson, Henry Markram, Reinoud Maex, Astrid A. Prinz, Imad Riachi, John Rinzel, Arnd Roth, Felix Schürmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils

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

Principles of Computational Modelling in Neuroscience

Author : David Sterratt
Publisher : Cambridge University Press
ISBN 13 : 1139500791
Total Pages : 403 pages
Book Rating : 4.1/5 (395 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 2011-06-30 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications

Author : Alonso, Eduardo
Publisher : IGI Global
ISBN 13 : 1609600231
Total Pages : 396 pages
Book Rating : 4.6/5 (96 download)

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Book Synopsis Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications by : Alonso, Eduardo

Download or read book Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications written by Alonso, Eduardo and published by IGI Global. This book was released on 2010-11-30 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

Computational Neuroscience

Author : Erik De Schutter
Publisher : CRC Press
ISBN 13 : 9780849320682
Total Pages : 368 pages
Book Rating : 4.3/5 (26 download)

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Book Synopsis Computational Neuroscience by : Erik De Schutter

Download or read book Computational Neuroscience written by Erik De Schutter and published by CRC Press. This book was released on 2000-11-22 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the processing of information by neural networks. He avoids theoretical mathematics and provides just enough of the basic math used by experimentalists. What makes this resource unique is the inclusion of a CD-ROM that furnishes interactive modeling examples. It contains tutorials and demos, movies and images, and the simulation scripts necessary to run the full simulation described in the chapter examples. Each chapter covers: the theoretical foundation; parameters needed; appropriate software descriptions; evaluation of the model; future directions expected; examples in text boxes linked to the CD-ROM; and references. The first book to bring you cutting-edge developments in neuronal modeling. It provides an introduction to realistic modeling methods at levels of complexity varying from molecular interactions to neural networks. The book and CD-ROM combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.

Computational Models of Brain and Behavior

Author : Ahmed A. Moustafa
Publisher : John Wiley & Sons
ISBN 13 : 1119159067
Total Pages : 586 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Computational Models of Brain and Behavior by : Ahmed A. Moustafa

Download or read book Computational Models of Brain and Behavior written by Ahmed A. Moustafa and published by John Wiley & Sons. This book was released on 2017-11-13 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Advanced Data Analysis in Neuroscience

Author : Daniel Durstewitz
Publisher : Springer
ISBN 13 : 3319599763
Total Pages : 292 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 292 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

Computational Neuroscience

Author : Hanspeter A Mallot
Publisher : Springer Science & Business Media
ISBN 13 : 3319008617
Total Pages : 135 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Computational Neuroscience by : Hanspeter A Mallot

Download or read book Computational Neuroscience written by Hanspeter A Mallot and published by Springer Science & Business Media. This book was released on 2013-05-23 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in neuroscience with backgrounds in biology, psychology and medicine, takes its readers on a journey through three fundamental domains of computational neuroscience: membrane biophysics, systems theory and artificial neural networks. The required mathematical concepts are kept as intuitive and simple as possible throughout the book, making it fully accessible to readers who are less familiar with mathematics. Overall, Computational Neuroscience - A First Course represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience.

Computational Modelling of the Brain

Author : Michele Giugliano
Publisher : Springer Nature
ISBN 13 : 3030894398
Total Pages : 361 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Computational Modelling of the Brain by : Michele Giugliano

Download or read book Computational Modelling of the Brain written by Michele Giugliano and published by Springer Nature. This book was released on 2022-04-26 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers an up-to-date overview of essential concepts and modern approaches to computational modelling, including the use of experimental techniques related to or directly inspired by them. The book introduces, at increasing levels of complexity and with the non-specialist in mind, state-of-the-art topics ranging from single-cell and molecular descriptions to circuits and networks. Four major themes are covered, including subcellular modelling of ion channels and signalling pathways at the molecular level, single-cell modelling at different levels of spatial complexity, network modelling from local microcircuits to large-scale simulations of entire brain areas and practical examples. Each chapter presents a systematic overview of a specific topic and provides the reader with the fundamental tools needed to understand the computational modelling of neural dynamics. This book is aimed at experimenters and graduate students with little or no prior knowledge of modelling who are interested in learning about computational models from the single molecule to the inter-areal communication of brain structures. The book will appeal to computational neuroscientists, engineers, physicists and mathematicians interested in contributing to the field of neuroscience. Chapters 6, 10 and 11 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Computational Neuroscience and Cognitive Modelling

Author : Britt-Anne Anderson
Publisher :
ISBN 13 : 9781526485809
Total Pages : pages
Book Rating : 4.4/5 (858 download)

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Book Synopsis Computational Neuroscience and Cognitive Modelling by : Britt-Anne Anderson

Download or read book Computational Neuroscience and Cognitive Modelling written by Britt-Anne Anderson and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique, self-contained and accessible textbook provides an introduction to computational modelling in psychology and neuroscience accessible to students with little or no background in computing or mathematics.

Dynamical Systems in Neuroscience

Author : Eugene M. Izhikevich
Publisher : MIT Press
ISBN 13 : 0262514206
Total Pages : 459 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Dynamical Systems in Neuroscience by : Eugene M. Izhikevich

Download or read book Dynamical Systems in Neuroscience written by Eugene M. Izhikevich and published by MIT Press. This book was released on 2010-01-22 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Introduction to Modeling Cognitive Processes

Author : Tom Verguts
Publisher : MIT Press
ISBN 13 : 0262045362
Total Pages : 265 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Introduction to Modeling Cognitive Processes by : Tom Verguts

Download or read book Introduction to Modeling Cognitive Processes written by Tom Verguts and published by MIT Press. This book was released on 2022-02-01 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.

Computational Neuroscience

Author : Wanpracha Chaovalitwongse
Publisher : Springer Science & Business Media
ISBN 13 : 0387886303
Total Pages : 330 pages
Book Rating : 4.3/5 (878 download)

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Book Synopsis Computational Neuroscience by : Wanpracha Chaovalitwongse

Download or read book Computational Neuroscience written by Wanpracha Chaovalitwongse and published by Springer Science & Business Media. This book was released on 2010-07-03 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.

Validating Neuro-Computational Models of Neurological and Psychiatric Disorders

Author : Basabdatta Sen Bhattacharya
Publisher : Springer
ISBN 13 : 3319200372
Total Pages : 315 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Validating Neuro-Computational Models of Neurological and Psychiatric Disorders by : Basabdatta Sen Bhattacharya

Download or read book Validating Neuro-Computational Models of Neurological and Psychiatric Disorders written by Basabdatta Sen Bhattacharya and published by Springer. This book was released on 2015-10-30 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of articles by leading researchers working at the cutting edge of neuro-computational modelling of neurological and psychiatric disorders. Each article contains model validation techniques used in the context of the specific problem being studied. Validation is essential for neuro-inspired computational models to become useful tools in the understanding and treatment of disease conditions. Currently, the immense diversity in neuro-computational modelling approaches for investigating brain diseases has created the need for a structured and coordinated approach to benchmark and standardise validation methods and techniques in this field of research. This book serves as a step towards a systematic approach to validation of neuro-computational models used for studying brain diseases and should be useful for all neuro-computational modellers.

MATLAB for Neuroscientists

Author : Pascal Wallisch
Publisher : Academic Press
ISBN 13 : 0123838371
Total Pages : 570 pages
Book Rating : 4.1/5 (238 download)

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Book Synopsis MATLAB for Neuroscientists by : Pascal Wallisch

Download or read book MATLAB for Neuroscientists written by Pascal Wallisch and published by Academic Press. This book was released on 2014-01-09 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Analysis and Modeling of Coordinated Multi-neuronal Activity

Author : Masami Tatsuno
Publisher : Springer
ISBN 13 : 1493919695
Total Pages : 354 pages
Book Rating : 4.4/5 (939 download)

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Book Synopsis Analysis and Modeling of Coordinated Multi-neuronal Activity by : Masami Tatsuno

Download or read book Analysis and Modeling of Coordinated Multi-neuronal Activity written by Masami Tatsuno and published by Springer. This book was released on 2014-11-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since information in the brain is processed by the exchange of spikes among neurons, a study of such group dynamics is extremely important in understanding hippocampus dependent memory. These spike patterns and local field potentials (LFPs) have been analyzed by various statistical methods. These studies have led to important findings of memory information processing. For example, memory-trace replay, a reactivation of behaviorally induced neural patterns during subsequent sleep, has been suggested to play an important role in memory consolidation. It has also been suggested that a ripple/sharp wave event (one of the characteristics of LFPs in the hippocampus) and spiking activity in the cortex have a specific relationship that may facilitate the consolidation of hippocampal dependent memory from the hippocampus to the cortex. The book will provide a state-of-the-art finding of memory information processing through the analysis of multi-neuronal data. The first half of the book is devoted to this analysis aspect. Understanding memory information representation and its consolidation, however, cannot be achieved only by analyzing the data. It is extremely important to construct a computational model to seek an underlying mathematical principle. In other words, an entire picture of hippocampus dependent memory system would be elucidated through close collaboration among experiments, data analysis, and computational modeling. Not only does computational modeling benefit the data analysis of multi-electrode recordings, but it also provides useful insight for future experiments and analyses. The second half of the book will be devoted to the computational modeling of hippocampus-dependent memory.

Principles of Computational Modelling in Neuroscience

Author : David Sterratt
Publisher : Cambridge University Press
ISBN 13 : 9780521877954
Total Pages : 401 pages
Book Rating : 4.8/5 (779 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 2011-06-30 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.