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Two distinguished neuroscientists distil general principles from more than a century of scientific study, "reverse engineering" the brain to understand its design.
Learning and Memory: A Comprehensive Reference, Second Edition is the authoritative resource for scientists and students interested in all facets of learning and memory. This updated edition includes chapters that reflect the state-of-the-art of research in this area. Coverage of sleep and memory has been significantly expanded, while neuromodulators in memory processing, neurogenesis and epigenetics are also covered in greater detail. New chapters have been included to reflect the massive increase in research into working memory and the educational relevance of memory research. No other reference work covers so wide a territory and in so much depth. Provides the most comprehensive and authoritative resource available on the study of learning and memory and its mechanisms Incorporates the expertise of over 150 outstanding investigators in the field, providing a ‘one-stop’ resource of reputable information from world-leading scholars with easy cross-referencing of related articles to promote understanding and further research Includes further reading for each chapter that helps readers continue their research Includes a glossary of key terms that is helpful for users who are unfamiliar with neuroscience terminology
A richly illustrated undergraduate textbook on the physics and biology of light Students in the physical and life sciences, and in engineering, need to know about the physics and biology of light. Recently, it has become increasingly clear that an understanding of the quantum nature of light is essential, both for the latest imaging technologies and to advance our knowledge of fundamental life processes, such as photosynthesis and human vision. From Photon to Neuron provides undergraduates with an accessible introduction to the physics of light and offers a unified view of a broad range of optical and biological phenomena. Along the way, this richly illustrated textbook builds the necessary background in neuroscience, photochemistry, and other disciplines, with applications to optogenetics, superresolution microscopy, the single-photon response of individual photoreceptor cells, and more. With its integrated approach, From Photon to Neuron can be used as the basis for interdisciplinary courses in physics, biophysics, sensory neuroscience, biophotonics, bioengineering, or nanotechnology. The goal is always for students to gain the fluency needed to derive every result for themselves, so the book includes a wealth of exercises, including many that guide students to create computer-based solutions. Supplementary online materials include real experimental data to use with the exercises. Assumes familiarity with first-year undergraduate physics and the corresponding math Overlaps the goals of the MCAT, which now includes data-based and statistical reasoning Advanced chapters and sections also make the book suitable for graduate courses An Instructor's Guide and illustration package is available to professors
Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.
Interoception is the body-to-brain axis of sensations that originates from the internal body and visceral organs. It plays a unique role in ensuring homeostasis, allowing human beings to experience and perceive the state of their bodies at any one time. However, interoception is rapidly gaining interest amongst those studying the human mind. It is believed that beyond homeostasis interoception is fundamental in understanding human emotion and motivation and their impact upon behavior. That link between interoception and self-awareness is supported by a growing body of experimental findings. The Interoceptive Mind: From Homeostasis to Awareness offers a state-of-the-art overview of, and insights into, the role of interoception for mental life, awareness, subjectivity, affect, and cognition. Structured across three parts, this multidisciplinary volume highlights the role that interoceptive signals, and our awareness of them, play in our mental life. It considers deficits in interoceptive processing and awareness in various mental health conditions. But it also considers the equally important role of interoception for well-being, approaching interoception from both a theoretical and a philosophical perspective. Written by leading experts in their fields, all chapters within this volume share a common concern for what it means to experience oneself, for the crucial role of emotions, and for issues of health and wellbeing. Each of those concerns is discussed on the joint basis of our bodily existence and interoception. The research presented here will undoubtedly accelerate the much-anticipated coming of age of interoceptive research in psychology, cognitive neurosciences and philosophy, making this vital reading for anyone working in those fields.
A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier. Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks. The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning. Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.

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