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Modeling Brain Function
One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the st...
Statistical Physics
This invaluable textbook is an introduction to statistical physics that has been written primarily for self-study. It provides a comprehensive approach to the main ideas of statistical physics at the level of an introductory course, starting from the kinetic theory of gases and proceeding all the way to Bose–Einstein and Fermi–Dirac statistics. Each idea is brought out with ample motivation and clear, step-by-step, deductive exposition. The key points and methods are presented and discussed on the basis of concrete representative systems, such as the paramagnet, Einstein's solid, the diatomic gas, black body radiation, electric conductivity in metals and superfluidity. The book is written in a stimulating style and is accompanied by a large number of exercises appropriately placed within the text and by self-assessment problems at the end of each chapter. Detailed solutions of all the exercises are provided.
Field Theory, The Renormalization Group, And Critical Phenomena: Graphs To Computers (3rd Edition)
This volume links field theory methods and concepts from particle physics with those in critical phenomena and statistical mechanics, the development starting from the latter point of view. Rigor and lengthy proofs are trimmed by using the phenomenological framework of graphs, power counting, etc., and field theoretic methods with emphasis on renormalization group techniques. Non-perturbative methods and numerical simulations are introduced in this new edition. Abundant references to research literature complement this matter-of-fact approach. The book introduces quantum field theory to those already grounded in the concepts of statistical mechanics and advanced quantum theory, with sufficient exercises in each chapter for use as a textbook in a one-semester graduate course.The following new chapters are included:I. Real Space MethodsII. Finite Size ScalingIII. Monte Carlo Methods. Numerical Field Theory
Neural Networks: From Biology To High Energy Physics - Proceedings Of The Third Workshop
The papers appearing in this proceedings volume cover a broad range of subjects, owing to the highly cross-disciplinary character of the workshop, and include: experiments and models concerning the dynamics of the neural activity in the cortex (DMS experiments, attractor dynamics in the cortex, spontaneous activity…); hippocampus, space and memory; theoretical advances in neural network modeling; information processing in neural networks; applications of neural networks to experimental physics, particularly to high energy physics; digital and analog hardware implementations of neural networks; etc.
Selected Papers of Daniel Amit (1938-2007)
This book provides a selection of papers of the late Daniel Amit (1938–2007). Daniel Amit was a physicist who spent the last 22 years of his life working on neural network models. He was one of the pioneers in the field. The volume contains 21 papers, from the highly influential 1985 paper on the Hopfield model (published together with Hanoch Gutfreund and Haim Sompolinsky), to his last (unpublished) manuscript. Many of these papers are landmark papers in the field. The book also provides a biography; an introduction on Daniel Amit's scientific career before the Hopfield model; and introductions to each of the included papers, written by their co-authors. This book will be of interest to physicists, computational neuroscientists and neurobiologists.
Field Theory, The Renormalization Group And Critical Phenomena (2nd Edition)
This volume links field theory methods and concepts from particle physics with those in critical phenomena and statistical mechanics, the development starting from the latter point of view. Rigor and lengthy proofs are trimmed by using the phenomenological framework of graphs, power counting, etc., and field theoretic methods with emphasis on renormalization group techniques. The book introduces quantum field theory to those already grounded in the concepts of statistical mechanics and advanced quantum theory, with sufficient exercises in each chapter for use as a textbook in a one-semester graduate course.
Statistical Physics
The material presented in this invaluable textbook has been tested in two courses. One of these is a graduate-level survey of statistical physics; the other, a rather personal perspective on critical behavior. Thus, this book defines a progression starting at the book-learning part of graduate education and ending in the midst of topics at the research level. To supplement the research-level side the book includes some research papers. Several of these are classics in the field, including a suite of six works on self-organized criticality and complexity, a pair on diffusion-limited aggregation, some papers on correlations near critical points, a few of the basic sources on the development of the real-space renormalization group, and several papers on magnetic behavior in a plain geometry. In addition, the author has included a few of his own papers.
