An Introductory Course in Computational Neuroscience

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Author :
Publisher : MIT Press
ISBN 13 : 0262347563
Total Pages : 405 pages
Book Rating : 4.2/5 (623 download)

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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 2018-10-09 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.

An Introductory Course in Computational Neuroscience

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Author :
Publisher : MIT Press
ISBN 13 : 0262038250
Total Pages : 405 pages
Book Rating : 4.2/5 (62 download)

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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.

Computational Neuroscience

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Publisher : Springer Science & Business Media
ISBN 13 : 3319008617
Total Pages : 142 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 142 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.

Fundamentals of Computational Neuroscience

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Publisher : Oxford University Press
ISBN 13 : 0199568413
Total Pages : 417 pages
Book Rating : 4.1/5 (995 download)

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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.

Neuronal Dynamics

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Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Neuronal Dynamics by : Wulfram Gerstner

Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

From Neuron to Cognition via Computational Neuroscience

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Publisher : MIT Press
ISBN 13 : 0262335271
Total Pages : 810 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis From Neuron to Cognition via Computational Neuroscience by : Michael A. Arbib

Download or read book From Neuron to Cognition via Computational Neuroscience written by Michael A. Arbib and published by MIT Press. This book was released on 2016-11-04 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Principles of Computational Modelling in Neuroscience

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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.

Biophysics of Computation

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Publisher : Oxford University Press
ISBN 13 : 0195181999
Total Pages : 587 pages
Book Rating : 4.1/5 (951 download)

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Book Synopsis Biophysics of Computation by : Christof Koch

Download or read book Biophysics of Computation written by Christof Koch and published by Oxford University Press. This book was released on 2004-10-28 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

Dynamical Systems in Neuroscience

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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.

Theoretical Neuroscience

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Publisher : MIT Press
ISBN 13 : 0262541858
Total Pages : 477 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Theoretical Neuroscience by : Peter Dayan

Download or read book Theoretical Neuroscience written by Peter Dayan and published by MIT Press. This book was released on 2005-08-12 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

An Introduction to Modeling Neuronal Dynamics

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Publisher : Springer
ISBN 13 : 3319511718
Total Pages : 445 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis An Introduction to Modeling Neuronal Dynamics by : Christoph Börgers

Download or read book An Introduction to Modeling Neuronal Dynamics written by Christoph Börgers and published by Springer. This book was released on 2017-04-17 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction to differential equations is more than enough mathematical background. Only a slim, high school-level background in physics is assumed, and none in biology. Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity. An extensive online collection of Matlab programs generating the figures accompanies the book.

Research Methods for Cognitive Neuroscience

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Publisher : SAGE
ISBN 13 : 1473952980
Total Pages : 1006 pages
Book Rating : 4.4/5 (739 download)

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Book Synopsis Research Methods for Cognitive Neuroscience by : Aaron Newman

Download or read book Research Methods for Cognitive Neuroscience written by Aaron Newman and published by SAGE. This book was released on 2019-03-18 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fresh, new textbook provides a thorough and student-friendly guide to the different techniques used in cognitive neuroscience. Given the breadth of neuroimaging techniques available today, this text is invaluable, serving as an approachable text for students, researchers, and writers. This text provides the right level of detail for those who wish to understand the basics of neuroimaging and also provides more advanced material in order to learn further about particular techniques. With a conversational, student-friendly writing style, Aaron Newman introduces the key principles of neuroimaging techniques, the relevant theory and the recent changes in the field.

MATLAB for Neuroscientists

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Publisher : Academic Press
ISBN 13 : 0123838371
Total Pages : 571 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 571 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

Introduction to Social Neuroscience

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Publisher : Princeton University Press
ISBN 13 : 069118917X
Total Pages : 302 pages
Book Rating : 4.6/5 (911 download)

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Book Synopsis Introduction to Social Neuroscience by : Stephanie Cacioppo

Download or read book Introduction to Social Neuroscience written by Stephanie Cacioppo and published by Princeton University Press. This book was released on 2020-08-11 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook that lays down the foundational principles for understanding social neuroscience Humans, like many other animals, are a highly social species. But how do our biological systems implement social behaviors, and how do these processes shape the brain and biology? Spanning multiple disciplines, Introduction to Social Neuroscience seeks to engage students and scholars alike in exploring the effects of the brain’s perceived connections with others. This wide-ranging textbook provides a quintessential foundation for comprehending the psychological, neural, hormonal, cellular, and genomic mechanisms underlying such varied social processes as loneliness, empathy, theory-of-mind, trust, and cooperation. Stephanie and John Cacioppo posit that our brain is our main social organ. They show how the same objective relationship can be perceived as friendly or threatening depending on the mental states of the individuals involved in that relationship. They present exercises and evidence-based findings readers can put into practice to better understand the neural roots of the social brain and the cognitive and health implications of a dysfunctional social brain. This textbook’s distinctive features include the integration of human and animal studies, clinical cases from medicine, multilevel analyses of topics from genes to societies, and a variety of methodologies. Unveiling new facets to the study of the social brain’s anatomy and function, Introduction to Social Neuroscience widens the scientific lens on human interaction in society. The first textbook on social neuroscience intended for advanced undergraduates and graduate students Chapters address the psychological, neural, hormonal, cellular, and genomic mechanisms underlying the brain’s perceived connections with others Materials integrate human and animal studies, clinical cases, multilevel analyses, and multiple disciplines

Reasoning

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Publisher : Academic Press
ISBN 13 : 0128095768
Total Pages : 356 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Reasoning by : Daniel Krawczyk

Download or read book Reasoning written by Daniel Krawczyk and published by Academic Press. This book was released on 2017-11-13 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reasoning: The Neuroscience of How We Think is a comprehensive guide to the core topics related to a thorough understanding of reasoning. It presents the current knowledge of the subject in a unified, complete manner, ranging from animal studies, to applied situations, and is the only book available that presents a sustained focus on the neurobiological processes behind reasoning throughout all chapters, while also synthesizing research from animal behavior, cognitive psychology, development, and philosophy for a truly multidisciplinary approach. The book considers historical perspectives, state-of-the-art research methods, and future directions in emerging technology and cognitive enhancement. Written by an expert in the field, this book provides a coherent and structured narrative appropriate for students in need of an introduction to the topic of reasoning as well as researchers seeking well-rounded foundational content. It is essential reading for neuroscientists, cognitive scientists, neuropsychologists and others interested in the neural mechanisms behind thinking, reasoning and higher cognition. Provides a comparative perspective considering animal cognition and its relevance to human reasoning Includes developmental and lifespan considerations throughout the book Discusses technological development and its role in reasoning, both currently and in the future Considers perspectives from not only neuroscience, but cognitive psychology, philosophy, development, and animal behavior for a multidisciplinary treatment Contains highlight boxes featuring additional details on methods, historical descriptions and experimental tasks

Retinal Computation

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Publisher : Elsevier
ISBN 13 : 0128198966
Total Pages : 340 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Retinal Computation by : Greg Schwartz

Download or read book Retinal Computation written by Greg Schwartz and published by Elsevier. This book was released on 2021-08-17 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Retinal Computation summarizes current progress in defining the computations performed by the retina, also including the synaptic and circuit mechanisms by which they are implemented. Each chapter focuses on a single retinal computation that includes the definition of the computation and its neuroethological purpose, along with the available information on its known and unknown neuronal mechanisms. All chapters contain end-of-chapter questions associated with a landmark paper, as well as programming exercises. This book is written for advanced graduate students, researchers and ophthalmologists interested in vision science or computational neuroscience of sensory systems. While the typical textbook's description of the retina is akin to a biological video camera, the real retina is actually the world's most complex image processing machine. As part of the central nervous system, the retina converts patterns of light at the input into a rich palette of representations at the output. The parallel streams of information in the optic nerve encode features like color, contrast, orientation of edges, and direction of motion. Image processing in the retina is undeniably complex, but as one of the most accessible parts of the central nervous system, the tools to study retinal circuits with unprecedented precision are up to the task. This book provides a practical guide and resource about the current state of the field of retinal computation. Editorial Reviews: ".this book is also a unique overview of our current understanding of the why and the how of retinal computation and there is something here for anyone with a grounding in vision science who recognises that there is more to what the retina does than. meets the eye." -- Prof Steven Dakin, New Zealand Optics, May 28, 2022. "I want to commend Dr. Schwartz for assembling this incredible resource and strongly recommend Retinal Computation to everyone who is a student of vision. The vast majority of modern topics in retina are covered yet in a fashion that is clear, and concise. The book covers the cellular and circuit basis of computations ranging from those covered by most textbooks, such as center-surround receptive field or direction selectivity , to those you probably do not associated with the retina such as "motion anticipation" and "threat detection". Each chapter is self-contained, meaning you can easily "pick and choose" the topics. A quick perusal of the chapter titles are almost certainly going to pique your interest. For example, you may know that the retina has single photon sensitivity but do you know "How many photons does it take to create a percept"? (Chapter 1). How does the retina encode texture (i.e. spatial fluctuations within the receptive field)? (Chapter 7). Is object motion sensitivity related to Direction selectivity? (Chapter 12). The list goes on. This book will also serve as a great resource for those teaching advanced undergraduate or graduate level vision courses for students with backgrounds in experimental or computational vision science. Each chapter contains what Dr. Schwartz's considers a "landmark paper" in the field, with a set of questions that can be used as a guide for reading these papers. And finally he includes programming exercises that can be easily implemented in Matlab to address basic concepts introduced in the chapter. The instructions are detailed so that even those new to Matlab will be able to implement these exercises these straightforward. It is this combination - textbook chapter + primary literature + quantitative exercises that will solidify these concepts. There are many vision science topics not covered in the book. For example, there is little on retinal disease or development. But these limitations are far outweighed by where the book succeeds. The vast majority of the book is written by Dr. Schwartz, giving it a uniformity that is welcome. Despite tackling quite modern questions where there is ongoing progress, Dr.Schwartz has extracted what are key findings that are likely to stand the test of time. And finally, it is really interesting! For those who think that the retina is "solved", think again. Retinal computations is a fantastic way for all circuit neuroscientist to learn how much computations can be achieved with very few synapses." -- Marla B. Feller, Ph. D., Paul Licht Distinguished Professor in Biological Sciences, Division of Neurobiology, Department of Molecular and Cell Biology & Helen Wills Neuroscience Institute University of California, Berkeley "This fantastic new textbook from a rising star in the field clearly and thoroughly updates our picture of what the retina computes. It is detailed enough for senior researchers but also pedagogical, providing a go-to reference for students. The illustrations within the text and for the chapter headings are both beautiful and informative." -- Stephanie E. Palmer, Ph.D., Associate Professor, Department of Organismal Biology and Anatomy, Department of Physics, University of Chicago "This book summarizes the impressive recent progress in understanding how visual computations are performed by retinal circuits. The book is an important resource not only for retinal experts, but more generally for anyone seeking to explain how the brain works at the level of neural circuits. Greg Schwartz and his co-authors have made a major contribution to the field." -- Sebastian Seung, Anthony B. Evnin '62 Professor, Neuroscience Institute and Computer Science Dept., Princeton University "This is a wonderful book from a true expert in the retina field. It is a fantastic resource for researchers, lecturers, and students alike. The book nicely covers the many facets of how the retina processes the visual input that enters the eye. Despite the richness in material, the presentation manages to stay accessible and always connects back to fundamental questions of visual processing. Each chapter by itself is a great entry point into a particular area of how the neural network of the retina deals with a specific set of visual challenges. I have thoroughly enjoyed this wonderful overview of retinal computation, served on a silver platter, and I will use the book both as background material for research and as a resource for teaching. I particularly like the sets of exercises that conclude each chapter." -- Dr. Tim Gollisch, Professor for Sensory Processing in the Retina, Department of Ophthalmology, University Medical Center Göttingen

Principles of Neural Design

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Publisher : MIT Press
ISBN 13 : 0262028700
Total Pages : 567 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Principles of Neural Design by : Peter Sterling

Download or read book Principles of Neural Design written by Peter Sterling and published by MIT Press. This book was released on 2015-05-22 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to "reverse engineer" the brain -- disassembling it to understand it -- Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of "anticipatory regulation"; identify constraints on neural design and the need to "nanofy"; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes "save only what is needed." Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.