What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

In this landmark work on early conceptual and lexical development, Ellen Markman explores the fascinating problem of how young children succeed at the task of inducing concepts. Backed by extensive experimental results, she challenges the fundamental assumptions of traditional theories of language acquisition and proposes that a set of constraints or principles of induction allows children to efficiently integrate knowledge and to induce information about new examples of familiar categories.Ellen M. Markman is Professor of Psychology at Stanford University.

Why does the world look to us as it does? Generally speaking, this question has received two types of answers in the cognitive sciences in the past fifty or so years. According to the first, the world looks to us the way it does because we construct it to look as it does. According to the second, the world looks as it does primarily because of how the world is. In The Innocent Eye, Nico Orlandi defends a position that aligns with this second, world-centered tradition, but that also respects some of the insights of constructivism. Orlandi develops an embedded understanding of visual processing according to which, while visual percepts are representational states, the states and structures that precede the production of percepts are not representations.
If we study the environmental contingencies in which vision occurs, and we properly distinguish functional states and features of the visual apparatus from representational states and features, we obtain an empirically more plausible, world-centered account. Orlandi shows that this account accords well with models of vision in perceptual psychology -- such as Natural Scene Statistics and Bayesian approaches to perception -- and outlines some of the ways in which it differs from recent 'enactive' approaches to vision. The main difference is that, although the embedded account recognizes the importance of movement for perception, it does not appeal to action to uncover the richness of visual stimulation.
The upshot is that constructive models of vision ascribe mental representations too liberally, ultimately misunderstanding the notion. Orlandi offers a proposal for what mental representations are that, following insights from Brentano, James and a number of contemporary cognitive scientists, appeals to the notions of de-coupleability and absence to distinguish representations from mere tracking states.

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes.
Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation.
Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

For over thirty years, Nobel Prize winner David H. Hubel has been at the forefront of research on questions of vision. In Eye, Brain, and Vision, he brings you to the edge of current knowledge about vision, and explores the tasks scientists face in deciphering the many remaining mysteries of vision and the workings of the human brain.

Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, that is, between action and outcome. In cognitive terms, the question becomes one of how people construct and reason with the causal models we use to represent our world. A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. These fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called 'causal Bayesian networks'. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention: how does intervening on one thing affect other things? This question is not merely about probability (or logic), but about action. The framework offers a new understanding of mind: thought is about the effects of intervention, so cognition is thereby intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. In this book, Steven Sloman offers a conceptual introduction to the key mathematical ideas in the framework, presenting them in a non-technical way, by focusing on the intuitions rather than the theorems. He tries to show why the ideas are important to understanding how people explain things, and why it is so central to human action to think not only about the world as it is, but also about the world as it could be. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning. In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation.

There are currently two major theories about the role of the hippocampus, a distinctive structure in the back of the temporal lobe. One says that it stores a cognitive map, the other that it is a key locus for the temporary storage of episodic memories. A. David Redish takes the approach that understanding the role of the hippocampus in space will make it possible to address its role in less easily quantifiable areas such as memory. Basing his investigation on the study of rodent navigation—one of the primary domains for understanding information processing in the brain—he places the hippocampus in its anatomical context as part of a greater functional system.
Redish draws on the extensive experimental and theoretical work of the last 100 years to paint a coherent picture of rodent navigation. His presentation encompasses multiple levels of analysis, from single-unit recording results to behavioral tasks to computational modeling. From this foundation, he proposes a novel understanding of the role of the hippocampus in rodents that can shed light on the role of the hippocampus in primates, explaining data from primate studies and human neurology. The book will be of interest not only to neuroscientists and psychologists, but also to researchers in computer science, robotics, artificial intelligence, and artificial life.

In The Mind within the Brain, David Redish brings together cutting edge research in psychology, robotics, economics, neuroscience, and the new fields of neuroeconomics and computational psychiatry, to offer a unified theory of human decision-making. Most importantly, Redish shows how vulnerabilities, or "failure-modes," in the decision-making system can lead to serious dysfunctions, such as irrational behavior, addictions, problem gambling, and PTSD. Told with verve and humor in an easily readable style, Redish makes these difficult concepts understandable. Ranging widely from the surprising roles of emotion, habit, and narrative in decision-making, to the larger philosophical questions of how mind and brain are related, what makes us human, the nature of morality, free will, and the conundrum of robotics and consciousness, The Mind within the Brain offers fresh insight into one of the most complex aspects of human behavior.

"Individuals who live in groups hold common beliefs which define their reality, not only as persons, but also as group members. This reality becomes especially important when group members become aware that they share beliefs and are convinced that these beliefs characterize them as a group. In this case common beliefs become group beliefs." With this statement, Dr. Bar-Tal begins his far-reaching analysis of beliefs as a group phenomenon. Group beliefs are shown to have important behavioral, cognitive, and affective implications for group members and the group as a whole. They may contribute to the behavioral direction a group takes, coordinate group activities, determine the intensity and involvement of group members, and influence the way group members affect the leaders. This book introduces and articulates the implications of a new concept of group beliefs, shedding new light on the structure and processes of groups, focusing on such phenomena as group formation, subgrouping, splits, mergence and group disintegration. By taking an interdisciplinary approach, this integrative conception opens new avenues to the study and understanding of group behavior.

A Nobel Prize-winning scientist and a leading brain researcher show how the brain creates conscious experience In A Universe of Consciousness, Gerald Edelman builds on the radical ideas he introduced in his monumental trilogy-Neural Darwinism, Topobiology, and The Remembered Present-to present for the first time an empirically supported full-scale theory of consciousness. He and the neurobiolgist Giulio Tononi show how they use ingenious technology to detect the most minute brain currents and to identify the specific brain waves that correlate with particular conscious experiences. The results of this pioneering work challenge the conventional wisdom about consciousness.