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Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields. Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs).
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience. - A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community - Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research - Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, decisions must be made given current knowledge. Surprisingly, making what is believed to be the best decision is not always the best strategy, even when learning in a supervised learning setting. An emerging body of work on learning under different rules applies perturbations to decision and learning procedures. These methods provide simple and highly efficient learning rules with improved theoretical guarantees. This book describes perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees, offering readers a state-of-the-art overview. Chapters address recent modeling ideas that have arisen within the perturbations framework, including Perturb & MAP, herding, and the use of neural networks to map generic noise to distribution over highly structured data. They describe new learning procedures for perturbation models, including an improved EM algorithm and a learning algorithm that aims to match moments of model samples to moments of data. They discuss understanding the relation of perturbation models to their traditional counterparts, with one chapter showing that the perturbations viewpoint can lead to new algorithms in the traditional setting. And they consider perturbation-based regularization in neural networks, offering a more complete understanding of dropout and studying perturbations in the context of deep neural networks.
Multirate Statistical Signal Processing introduces a statistical theory for extracting information from related signals with different sampling rates. This new theory generalizes the conventional deterministic theory of multirate systems beyond many of its constraints. Further, it allows for the formulation and solution of new problems: spectrum estimation, time-delay estimation and sensor fusion in the realm of multirate signal processing. This self-contained book presents background material, potential applications and leading-edge research.
State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.
This book is concerned with computing in materio: that is, unconventional computing performed by directly harnessing the physical properties of materials. It offers an overview of the field, covering four main areas of interest: theory, practice, applications and implications. Each chapter synthesizes current understanding by deliberately bringing together researchers across a collection of related research projects. The book is useful for graduate students, researchers in the field, and the general scientific reader who is interested in inherently interdisciplinary research at the intersections of computer science, biology, chemistry, physics, engineering and mathematics.
With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of 'digital obesity'. In this century, computational intelligence, such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas — from social sciences, economics and biology, medicine and social networks, to cyber security.The Handbook of Computational Intelligence (in two volumes) prompts readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems. Volume one explores the subjects of fuzzy logic and systems, artificial neural networks, and learning systems. Volume two delves into evolutionary computation, hybrid systems, as well as the applications of computational intelligence in decision making, the process industry, robotics, and autonomous systems.This work is a 'one-stop-shop' for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.
Leading neuroscientists discuss the function of microcircuits, functional modules that act as elementary processing units bridging single cells to systems and behavior. Microcircuits, functional modules that act as elementary processing units bridging single cells to systems and behavior, could provide the link between neurons and global brain function. Microcircuits are designed to serve particular functions; examples of these functional modules include the cortical columns in sensory cortici, glomeruli in the olfactory systems of insects and vertebrates, and networks generating different aspects of motor behavior. In this Dahlem Workshop volume, leading neuroscientists discuss how microcircuits work to bridge the single cell and systems levels and compare the intrinsic function of microcircuits with their ion channel subtypes, connectivity, and receptors, in order to understand the design principles and function of the microcircuits. The chapters cover the four major areas of microcircuit research: motor systems, including locomotion, respiration, and the saccadic eye movements; the striatum, the largest input station of the basal ganglia; olfactory systems and the neural organization of the glomeruli; and the neocortex. Each chapter is followed by a group report, a collaborative discussion among senior scientists. Contributors Lidia Alonso-Nanclares, Hagai Bergman, Maria Blatow, J. Paul Bolam, Ansgar Büschges, Antonio Caputi, Jean-Pierre Changeux, Javier DeFelipe, Carsten Duch, Paul Feinstein, Stuart Firestein, Yves Frégnac, Rainer W. Friedrich, C. Giovanni Galizia, Ann M. Graybiel, Charles A. Greer, Sten Grillner, Tadashi Isa, Ole Kiehn, Minoru Kimura, Anders Lanser, Gilles Laurent, Pierre-Marie Lledo, Wolfgang Maass, Henry Markram, David A. McCormick, Christoph M. Michel, Peter Mombaerts, Hannah Monyer, Hans-Joachim Pflüger, Dietmar Plenz, Diethelm W. Richter, Silke Sachse, H. Sebastian Seung, Keith T. Sillar, Jeffrey C. Smith, David L. Sparks, D. James Surmeier, Eörs Szathmáry, James M. Tepper, Jeff R. Wickens, Rafael Yuste
This book constitutes the refereed proceedings of the The International Workshop on Optical SuperComputing, OSC 2008, held in Vienna, Austria, August 2008 in conjunction with the 7th International Conference on Unconventional Computation UC 2008. OCS is a new annual forum for research presentations on all facets of optical computing for solving hard computation tasks. Topics of interest include, but are not limited to: Design of optical computing devices, electrooptics devices for interacting with optical computing devices, practical implementations, analysis of existing devices and case studies, optical and laser switching technologies, applications and algorithms for optical devices, alpha practical, x-rays and nano-technologies for optical computing.