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Fundamentals Of Computational Neuroscience
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Book Synopsis Fundamentals of Computational Neuroscience by : Thomas Trappenberg
Download or read book Fundamentals of Computational Neuroscience written by Thomas Trappenberg and published by Oxford University Press. This book was released on 2010 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.
Book Synopsis Fundamentals of Computational Neuroscience by : Thomas Trappenberg
Download or read book Fundamentals of Computational Neuroscience written by Thomas Trappenberg and published by Oxford University Press. This book was released on 2023-03-08 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic. The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the previous editions. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies. Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book will be the essential text for anyone in the brain sciences who wants to get to grips with this topic.
Book Synopsis An Introductory Course in Computational Neuroscience by : Paul Miller
Download or read book An Introductory Course in Computational Neuroscience written by Paul Miller and published by MIT Press. This book was released on 2018-10-02 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
Author :Thomas P. Trappenberg Publisher :Oxford : Oxford University Press ISBN 13 :9780198515821 Total Pages :338 pages Book Rating :4.5/5 (158 download)
Book Synopsis Fundamentals of Computational Neuroscience by : Thomas P. Trappenberg
Download or read book Fundamentals of Computational Neuroscience written by Thomas P. Trappenberg and published by Oxford : Oxford University Press. This book was released on 2002 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title includes the following features: An accessible introduction to the field of computational neuroscience; Aimed at graduate/postgraduates upwards in the cognitive and brain sciences; Accompanying webpage with MATLAB programmes to download; Affordable
Book Synopsis Fundamentals of Neural Network Modeling by : Randolph W. Parks
Download or read book Fundamentals of Neural Network Modeling written by Randolph W. Parks and published by MIT Press. This book was released on 1998 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble
Book Synopsis Fundamentals of Brain Network Analysis by : Alex Fornito
Download or read book Fundamentals of Brain Network Analysis written by Alex Fornito and published by Academic Press. This book was released on 2016-03-04 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
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.
Book Synopsis Encyclopedia of Computational Neuroscience by : Dieter Jäger
Download or read book Encyclopedia of Computational Neuroscience written by Dieter Jäger and published by Springer. This book was released on 2022-04-26 with total page 3663 pages. Available in PDF, EPUB and Kindle. Book excerpt: The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks started permeating the methods and ideas of experimental neuroscientists. Although experimental neurophysiological approaches provided many advances, it became increasingly evident that mathematical and computational techniques would be required to achieve a comprehensive and quantitative understanding of neural system function. “Computational Neuroscience” emerged to complement experimental neurophysiology. The Encyclopedia of Computational Neuroscience, published in conjunction with the Organization for Computational Neuroscience, will be an extensive reference work consultable by both researchers and graduate level students. It will be a dynamic, living reference, updatable and containing linkouts and multimedia content whenever relevant.
Book Synopsis Neural Control Engineering by : Steven J. Schiff
Download or read book Neural Control Engineering written by Steven J. Schiff and published by MIT Press. This book was released on 2022-11-01 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications. Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience. The revolution in nonlinear control engineering in the late 1990s has made the intersection of control theory and neuroscience possible. In Neural Control Engineering, Steven Schiff seeks to bridge the two fields, examining the application of new methods in nonlinear control engineering to neuroscience. After presenting extensive material on formulating computational neuroscience models in a control environment—including some fundamentals of the algorithms helpful in crossing the divide from intuition to effective application—Schiff examines a range of applications, including brain-machine interfaces and neural stimulation. He reports on research that he and his colleagues have undertaken showing that nonlinear control theory methods can be applied to models of single cells, small neuronal networks, and large-scale networks in disease states of Parkinson's disease and epilepsy. With Neural Control Engineering the reader acquires a working knowledge of the fundamentals of control theory and computational neuroscience sufficient not only to understand the literature in this trandisciplinary area but also to begin working to advance the field. The book will serve as an essential guide for scientists in either biology or engineering and for physicians who wish to gain expertise in these areas.
Book Synopsis Fundamentals of Neuromechanics by : Francisco J. Valero-Cuevas
Download or read book Fundamentals of Neuromechanics written by Francisco J. Valero-Cuevas and published by Springer. This book was released on 2015-09-07 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a conceptual and computational framework to study how the nervous system exploits the anatomical properties of limbs to produce mechanical function. The study of the neural control of limbs has historically emphasized the use of optimization to find solutions to the muscle redundancy problem. That is, how does the nervous system select a specific muscle coordination pattern when the many muscles of a limb allow for multiple solutions? I revisit this problem from the emerging perspective of neuromechanics that emphasizes finding and implementing families of feasible solutions, instead of a single and unique optimal solution. Those families of feasible solutions emerge naturally from the interactions among the feasible neural commands, anatomy of the limb, and constraints of the task. Such alternative perspective to the neural control of limb function is not only biologically plausible, but sheds light on the most central tenets and debates in the fields of neural control, robotics, rehabilitation, and brain-body co-evolutionary adaptations. This perspective developed from courses I taught to engineers and life scientists at Cornell University and the University of Southern California, and is made possible by combining fundamental concepts from mechanics, anatomy, mathematics, robotics and neuroscience with advances in the field of computational geometry. Fundamentals of Neuromechanics is intended for neuroscientists, roboticists, engineers, physicians, evolutionary biologists, athletes, and physical and occupational therapists seeking to advance their understanding of neuromechanics. Therefore, the tone is decidedly pedagogical, engaging, integrative, and practical to make it accessible to people coming from a broad spectrum of disciplines. I attempt to tread the line between making the mathematical exposition accessible to life scientists, and convey the wonder and complexity of neuroscience to engineers and computational scientists. While no one approach can hope to definitively resolve the important questions in these related fields, I hope to provide you with the fundamental background and tools to allow you to contribute to the emerging field of neuromechanics.
Book Synopsis Lectures in Supercomputational Neuroscience by : Peter Graben
Download or read book Lectures in Supercomputational Neuroscience written by Peter Graben and published by Springer. This book was released on 2007-10-19 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written from the physicist’s perspective, this book introduces computational neuroscience with in-depth contributions by system neuroscientists. The authors set forth a conceptual model for complex networks of neurons that incorporates important features of the brain. The computational implementation on supercomputers, discussed in detail, enables you to adapt the algorithm for your own research. Worked-out examples of applications are provided.
Book Synopsis Neural Engineering by : Chris Eliasmith
Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Book Synopsis The Computational Neurobiology of Reaching and Pointing by : Reza Shadmehr
Download or read book The Computational Neurobiology of Reaching and Pointing written by Reza Shadmehr and published by MIT Press. This book was released on 2004-10-28 with total page 608 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the computational biology of reaching and pointing, with an emphasis on motor learning. Neuroscience involves the study of the nervous system, and its topics range from genetics to inferential reasoning. At its heart, however, lies a search for understanding how the environment affects the nervous system and how the nervous system, in turn, empowers us to interact with and alter our environment. This empowerment requires motor learning. The Computational Neurobiology of Reaching and Pointing addresses the neural mechanisms of one important form of motor learning. The authors integrate material from the computational, behavioral, and neural sciences of motor control that is not available in any other single source. The result is a unified, comprehensive model of reaching and pointing. The book is intended to be used as a text by graduate students in both neuroscience and bioengineering and as a reference source by experts in neuroscience, robotics, and other disciplines. The book begins with an overview of the evolution, anatomy, and physiology of the motor system, including the mechanisms for generating force and maintaining limb stability. The sections that follow, "Computing Locations and Displacements", "Skills, Adaptations, and Trajectories", and "Predictions, Decisions, and Flexibility", present a theory of sensorially guided reaching and pointing that evolves organically based on computational principles rather than a traditional structure-by-structure approach. The book also includes five appendixes that provide brief refreshers on fundamentals of biology, mathematics, physics, and neurophysiology, as well as a glossary of relevant terms. The authors have also made supplemental materials available on the Internet. These web documents provide source code for simulations, step-by-step derivations of certain mathematical formulations, and expanded explanations of some concepts.
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
Book Synopsis Models of Information Processing in the Basal Ganglia by : James C. Houk
Download or read book Models of Information Processing in the Basal Ganglia written by James C. Houk and published by MIT Press. This book was released on 1995 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models. Recent years have seen a remarkable expansion of knowledge about the anatomical organization of the part of the brain known as the basal ganglia, the signal processing that occurs in these structures, and the many relations both to molecular mechanisms and to cognitive functions. This book brings together the biology and computational features of the basal ganglia and their related cortical areas along with select examples of how this knowledge can be integrated into neural network models. Organized in four parts - fundamentals, motor functions and working memories, reward mechanisms, and cognitive and memory operations - the chapters present a unique admixture of theory, cognitive psychology, anatomy, and both cellular- and systems- level physiology written by experts in each of these areas. The editors have provided commentaries as a helpful guide to each part. Many new discoveries about the biology of the basal ganglia are summarized, and their impact on the computational role of the forebrain in the planning and control of complex motor behaviors discussed. The various findings point toward an unexpected role for the basal ganglia in the contextual analysis of the environment and in the adaptive use of this information for the planning and execution of intelligent behaviors. Parallels are explored between these findings and new connectionist approaches to difficult control problems in robotics and engineering. Contributors James L. Adams, P. Apicella, Michael Arbib, Dana H. Ballard, Andrew G. Barto, J. Brian Burns, Christopher I. Connolly, Peter F. Dominey, Richard P. Dum, John Gabrieli, M. Garcia-Munoz, Patricia S. Goldman-Rakic, Ann M. Graybiel, P. M. Groves, Mary M. Hayhoe, J. R. Hollerman, George Houghton, James C. Houk, Stephen Jackson, Minoru Kimura, A. B. Kirillov, Rolf Kotter, J. C. Linder, T. Ljungberg, M. S. Manley, M. E. Martone, J. Mirenowicz, C. D. Myre, Jeff Pelz, Nathalie Picard, R. Romo, S. F. Sawyer, E Scarnat, Wolfram Schultz, Peter L. Strick, Charles J. Wilson, Jeff Wickens, Donald J. Woodward, S. J. Young
Book Synopsis Computational Cognitive Neuroscience by : Yuko Munakata
Download or read book Computational Cognitive Neuroscience written by Yuko Munakata and published by Independently Published. This book was released on 2012-09 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to computer modeling of the brain, to understand how people think. Networks of interacting neurons produce complex emergent behavior including perception, attention, motor control, learning, memory, language, and executive functions (motivation, decision making, planning, etc).
