For more than 30 years, the visual cortex has been the source of new theories and ideas about how the brain processes information. The visual cortex is easily accessible through a variety of recording and imagining techniques and allows mapping of high level behavior relatively directly to neural mechanisms. Understanding the computations in the visual cortex is therefore an important step toward a general theory of computational brain theory.

How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answers to these questions that modern theories of mind and brain have recently proposed. Stephen Grossberg is broadly acknowledged to be the most important pioneer and current research leader who has, for the past 50 years, modelled how brains give rise to minds, notably how neural circuits in multiple brain regions interact together to generate psychological functions. This research has led to a unified understanding of how, where, and why our brains can consciously see, hear, feel, and know about the world, and effectively plan and act within it. The work embodies revolutionary Principia of Mind that clarify how autonomous adaptive intelligence is achieved. It provides mechanistic explanations of multiple mental disorders, including symptoms of Alzheimer's disease, autism, amnesia, and sleep disorders; biological bases of morality and religion, including why our brains are biased towards the good so that values are not purely relative; perplexing aspects of the human condition, including why many decisions are irrational and self-defeating despite evolution's selection of adaptive behaviors; and solutions to large-scale problems in machine learning, technology, and Artificial Intelligence that provide a blueprint for autonomously intelligent algorithms and robots. Because brains embody a universal developmental code, unifying insights also emerge about shared laws that are found in all living cellular tissues, from the most primitive to the most advanced, notably how the laws governing networks of interacting cells support developmental and learning processes in all species. The fundamental brain design principles of complementarity, uncertainty, and resonance that Grossberg has discovered also reflect laws of the physical world with which our brains ceaselessly interact, and which enable our brains to incrementally learn to understand those laws, thereby enabling humans to understand the world scientifically. Accessibly written, and lavishly illustrated, Conscious Mind/Resonant Brain is the magnum opus of one of the most influential scientists of the past 50 years, and will appeal to a broad readership across the sciences and humanities.

This is the third edition of the translation, by Laurence Garey, of "Vergleichende Lokalisationslehre der Grosshirnrinde" by Korbinian Brodmann, originally published by Barth-Verlag in Leipzig in 1909. It is one of the major "classics" of the neurological world. Even today it forms the basis for so-called "localisation" of function in the cerebral cortex. Brodmann's "areas" are still used to designate functional regions in the cortex, the part of the brain that brings the world that surrounds us into consciousness, and which governs our responses to the world. For example, we use "area 4" for the "motor" cortex, with which we control our muscles, "area 17" for "visual" cortex, with which we see, and so on. This nomenclature is used by neurologists and neurosurgeons in the human context, as well as by experimentalists in various animals. Indeed, Brodmann's famous "maps" of the cerebral cortex of humans, monkeys and other mammals must be among the most commonly reproduced figures in neurobiological publishing. The most famous of all is that of the human brain. There can be few textbooks of neurology, neurophysiology or neuroanatomy in which Brodmann is not cited, and his concepts pervade most research publications on systematic neurobiology. In spite of this, few people have ever seen a copy of the 1909 monograph, and even fewer have actually read it! There had never been a complete English translation available until the first edition of the present translation of 1994, and the original book had been almost unavailable for 50 years or more, the few antiquarian copies still around commanding high prices. As Laurence Garey, too, used Brodmann’s findings and maps in his neurobiological work, and had the good fortune to have access to a copy of the book, he decided to read the complete text and soon discovered that this was much more than just a report of laboratory findings of a turn-of-the-twentieth-century neurologist. It was an account of neurobiological thinking at that time, covering aspects of comparative neuroanatomy, neurophysiology and neuropathology, as well as giving a fascinating insight into the complex relationships between European neurologists during the momentous times when the neuron theory was still new.

In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

For more than 30 years, the visual cortex has been the source of new theories and ideas about how the brain processes information. The visual cortex is easily accessible through a variety of recording and imagining techniques and allows mapping of high level behavior relatively directly to neural mechanisms. Understanding the computations in the visual cortex is therefore an important step toward a general theory of computational brain theory.

This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang

Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.

The study of learning and memory is a central topic in neuroscience and psychology. Many of the basic research findings are directly applicable in the treatment of diseases and aging phenomena, and have found their way into educational theory and praxis. Concise Learning and Memory represents the best 30 chapters from Learning and Memory: A comprehensive reference (Academic Press March 2008), the most comprehensive source of information about learning and memory ever assembled, selected by one of the most respective scientists in the field, John H. Byrne. This concise version provides a truly authoritative collection of overview articles representing fundamental reviews of our knowledge of this central cognitive function of animal brains. It will be an affordable and accessible reference for scientists and students in all areas of neuroscience and psychology. There is no other single-volume reference with such authority and comprehensive coverage and depth currently available. - Represents an authoritative selection of the fundamental chapters from the most comprehensive source of information about learning and memory ever assembled, Learning and Memory - A comprehensive reference (Academic Press Mar 2008) - Representing outstanding scholarship, each chapter is written by a leader in the field and an expert in the topic area - All topics represent the most up to date research - Full color throughout, heavily illustrated - Priced to provide an affordable reference to individuals and workgroups