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Neural Codes and Distributed Representations
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. The present volume focuses on neural codes and representations, topics of broad interest to neuroscientists and modelers. The topics addressed are: how neurons encode information through action potential firing patterns, how populations of neurons represent information, and how individual neurons use dendritic processing and biophysical properties of synapses to decode spike trains. The papers encompass a wide range of levels of investigation, from dendrites and neurons to networks and systems.
The Computational Brain, 25th Anniversary Edition
An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists. Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computat...
Handbook of Embodied Cognition and Sport Psychology
The first systematic collaboration between cognitive scientists and sports psychologists considers the mind–body relationship from the perspective of athletic skill and sports practice. This landmark work is the first systematic collaboration between cognitive scientists and sports psychologists that considers the mind–body relationship from the perspective of athletic skill and sports practice. With twenty-six chapters by leading researchers, the book connects and integrates findings from fields that range from philosophy of mind to sociology of sports. The chapters show not only that sports can tell scientists how the human mind works but also that the scientific study of the human min...
Visual Attention and Cortical Circuits
An attempt to derive a comprehensive theory of attention from both neurobiological and psychological data.
Bayesian Philosophy of Science
Jan Sprenger and Stephan Hartmann offer a fresh approach to central topics in philosophy of science, including causation, explanation, evidence, and scientific models. Their Bayesian approach uses the concept of degrees of belief to explain and to elucidate manifold aspects of scientific reasoning.
Advances in Neural Information Processing Systems 7
November 28-December 1, 1994, Denver, Colorado NIPS is the longest running annual meeting devoted to Neural Information Processing Systems. Drawing on such disparate domains as neuroscience, cognitive science, computer science, statistics, mathematics, engineering, and theoretical physics, the papers collected in the proceedings of NIPS7 reflect the enduring scientific and practical merit of a broad-based, inclusive approach to neural information processing. The primary focus remains the study of a wide variety of learning algorithms and architectures, for both supervised and unsupervised learning. The 139 contributions are divided into eight parts: Cognitive Science, Neuroscience, Learning ...
Optimally Irrational
For a long time, economists have assumed that we were cold, self-centred, rational decision makers – so-called Homo economicus; the last few decades have shattered this view. The world we live in and the situations we face are of course rich and complex, revealing puzzling aspects of our behaviour. Optimally Irrational argues that our improved understanding of human behaviour shows that apparent 'biases' are good solutions to practical problems – that many of the 'flaws' identified by behavioural economics are actually adaptive solutions. Page delivers an ambitious overview of the literature in behavioural economics and, through the exposition of these flaws and their meaning, presents a sort of unified theory of behaviouralism, cognitive psychology and evolutionary biology. He gathers theoretical and empirical evidence about the causes of behavioural 'biases' and proposes a big picture of what the discipline means for economics.
Sensory Cue Integration
This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The probabilistic approaches elaborated in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed.
Visual Cortex and Deep Networks
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried o...
