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Visual Perception
This book presents an interdisciplinary overview of the main facts and theories that guide contemporary research on visual perception. While the chapters cover virtually all areas of visual science, from philosophical foundations to computational algorithms, and from photoreceptor processes to neuronal networks, no attempt has been made to provide an exhaustive treatment of these topics. Rather, researchers from such diverse disciplines as psychology, neurophysiology, anatomy, and clinical vision sciences have worked together to review some of the most important correlations between perceptual phenomena and the underlying neurophysiological processes and mechanisms. The book is thus intended...
Vision
Professor Colin Blakemore presents a fascinating insight to all the major topics in visual science research.
Neural Systems: Analysis and Modeling
In recent years there has been tremendous activity in computational neuroscience resulting from two parallel developments. On the one hand, our knowledge of real nervous systems has increased dramatically over the years; on the other, there is now enough computing power available to perform realistic simulations of actual neural circuits. This is leading to a revolution in quantitative neuroscience, which is attracting a growing number of scientists from non-biological disciplines. These scientists bring with them expertise in signal processing, information theory, and dynamical systems theory that has helped transform our ways of approaching neural systems. New developments in experimental ...
Unsupervised Learning
Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Physical and Biological Processing of Images
This book consists of papers presented at an international symposium spon sored and organised by The Rank Prize Funds and held at The Royal Society, London, on 27-29 September, 1982. Since the inception of the Funds, the Trustees and their Scientific Advi sory Committee on Opto-e1ectronics have considered that the scope of opto electronics should extend to cover the question of how the eye transduces and processes optical information. The Funds have aimed to organise symposia on topics which, because of their interdisciplinary nature, were not well cov ered by other regular international scientific meetings. It was therefore very appropriate that the 1982 symposium should be on Physical and ...
The Engineer
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Functional Organisation of the Human Visual Cortex
Functional Organisation of the Human Visual Cortex
Sensation and Judgment
Psychophysical theory exists in two distinct forms -- one ascribes the explanation of phenomena and empirical laws to sensory processes. Context effects arising through the use of particular methods are an unwanted nuisance whose influence must be eliminated so that one isolates the "true" sensory scale. The other considers psychophysics only in terms of cognitive variables such as the judgment strategies induced by instructions and response biases. Sensory factors play a minor role in cognitive approaches. This work admits the validity of both forms of theory by arguing that the same empirical phenomena should be conceptualized in two alternative, apparently contradictory, ways. This accept...
Foundations of Perceptual Theory
Historical analysis reveals that perceptual theories and models are doomed to relatively short lives. The most popular contemporary theories in perceptual science do not have as wide an acceptance among researchers as do some of those in other sciences. To understand these difficulties, the authors of the present volume explore the conceptual and philosophical foundations of perceptual science. Based on logical analyses of various problems, theories, and models, they offer a number of reasons for the current weakness of perceptual explanations. New theoretical approaches are also proposed. At the end of each chapter, dicussants contribute to the conclusions by critically examining the authors' ideas and analyses.
Correlative Learning
Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers. The book also presents an overview of the role of correlation in the human brain as well as in the adaptive signal processing world; unifies many well-established synaptic adaptations (learning) rules within the correlation-based learning framework, focusing on a particular correlative learning paradigm, ALOPEX; and presents case studies that illustrate how to use different computational tools and ALOPEX to help readers understand certain brain functions or fit specific engineering applications.
