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Neuroscience

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The vast differences between the brain’s neural circuitry and a computer’s silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction.

Drawing on several decades of progress in computational neuroscience, together with recent results in Bayesian and reinforcement learning methodologies, Ballard factors the brain’s principal computational issues in terms of their natural place in an overall hierarchy. Each of these factors leads to a fresh perspective. A neural level focuses on the basic forebrain functions and shows how processing demands dictate the extensive use of timing-based circuitry and an overall organization of tabular memories. An embodiment level organization works in reverse, making extensive use of multiplexing and on-demand processing to achieve fast parallel computation. An awareness level focuses on the brain’s representations of emotion, attention and consciousness, showing that they can operate with great economy in the context of the neural and embodiment substrates.

A Multidisciplinary Perspective

Over the past decade, an explosion of empirical research in a variety of fields has allowed us to understand human moral sensibility as a sophisticated integration of cognitive, emotional, and motivational mechanisms shaped through evolution, development, and culture. Evolutionary biologists have shown that moral cognition evolved to aid cooperation; developmental psychologists have demonstrated that the elements that underpin morality are in place much earlier than we thought; and social neuroscientists have begun to map brain circuits implicated in moral decision making. This volume offers an overview of current research on the moral brain, examining the topic from disciplinary perspectives that range from anthropology and neurophilosophy to justice and law.

The contributors address the evolution of morality, considering precursors of human morality in other species as well as uniquely human adaptations. They examine motivations for morality, exploring the roles of passion, extreme sacrifice, and cooperation. They go on to consider the development of morality, from infancy to adolescence; findings on neurobiological mechanisms of moral cognition; psychopathic immorality; and the implications for justice and law of a more biological understanding of morality. These new findings may challenge our intuitions about society and justice, but they may also lead to more a humane and flexible legal system.

Contributors
Scott Atran, Abigail A. Baird, Nicolas Baumard, Sarah Brosnan, Jason M. Cowell, Molly J. Crockett, Ricardo de Oliveira-Souza, Andrew W. Delton, Mark R. Dadds, Jean Decety, Jeremy Ginges, Andrea L. Glenn, Joshua D. Greene, J. Kiley Hamlin, David J. Hawes, Jillian Jordan, Max M. Krasnow, Ayelet Lahat, Jorge Moll, Caroline Moul, Thomas Nadelhoffer, Alexander Peysakhovich, Laurent Prétôt, Jesse Prinz, David G. Rand, Rheanna J. Remmel, Emma Roellke, Regina A. Rini, Joshua Rottman, Mark Sheskin, Thalia Wheatley, Liane Young, Roland Zahn

The Neuroscience of Aesthetic Experience

In Feeling Beauty, G. Gabrielle Starr argues that understanding the neural underpinnings of aesthetic experience can reshape our conceptions of aesthetics and the arts. Drawing on the tools of both cognitive neuroscience and traditional humanist inquiry, Starr shows that neuroaesthetics offers a new model for understanding the dynamic and changing features of aesthetic life, the relationships among the arts, and how individual differences in aesthetic judgment shape the varieties of aesthetic experience.

Starr, a scholar of the humanities and a researcher in the neuroscience of aesthetics, proposes that aesthetic experience relies on a distributed neural architecture—a set of brain areas involved in emotion, perception, imagery, memory, and language. More important, it emerges from networked interactions, intricately connected and coordinated brain systems that together form a flexible architecture enabling us to develop new arts and to see the world around us differently. Focusing on the "sister arts" of poetry, painting, and music, Starr builds and tests a neural model of aesthetic experience valid across all the arts. Asking why works that address different senses using different means seem to produce the same set of feelings, she examines particular works of art in a range of media, including a poem by Keats, a painting by van Gogh, a sculpture by Bernini, and Beethoven's Diabelli Variations. Starr's innovative, interdisciplinary analysis is true to the complexities of both the physical instantiation of aesthetics and the realities of artistic representation.

An Introduction to Neuroanthropology

The brain and the nervous system are our most cultural organs. Our nervous system is especially immature at birth, our brain disproportionately small in relation to its adult size and open to cultural sculpting at multiple levels. Recognizing this, the new field of neuroanthropology places the brain at the center of discussions about human nature and culture. Anthropology offers brain science more robust accounts of enculturation to explain observable difference in brain function; neuroscience offers anthropology evidence of neuroplasticity's role in social and cultural dynamics. This book provides a foundational text for neuroanthropology, offering basic concepts and case studies at the intersection of brain and culture.

After an overview of the field and background information on recent research in biology, a series of case studies demonstrate neuroanthropology in practice. Contributors first focus on capabilities and skills—including memory in medical practice, skill acquisition in martial arts, and the role of humor in coping with breast cancer treatment and recovery—then report on problems and pathologies that range from post-traumatic stress disorder among veterans to smoking as a part of college social life.

Neural Reuse and the Interactive Brain

The computer analogy of the mind has been as widely adopted in contemporary cognitive neuroscience as was the analogy of the brain as a collection of organs in phrenology. Just as the phrenologist would insist that each organ must have its particular function, so contemporary cognitive neuroscience is committed to the notion that each brain region must have its fundamental computation. In After Phrenology, Michael Anderson argues that to achieve a fully post-phrenological science of the brain, we need to reassess this commitment and devise an alternate, neuroscientifically grounded taxonomy of mental function.

Anderson contends that the cognitive roles played by each region of the brain are highly various, reflecting different neural partnerships established under different circumstances. He proposes quantifying the functional properties of neural assemblies in terms of their dispositional tendencies rather than their computational or information-processing operations. Exploring larger-scale issues, and drawing on evidence from embodied cognition, Anderson develops a picture of thinking rooted in the exploitation and extension of our early-evolving capacity for iterated interaction with the world. He argues that the multidimensional approach to the brain he describes offers a much better fit for these findings, and a more promising road toward a unified science of minded organisms.

Making Sense of What We See

For many years, researchers have studied visual recognition with objects—single, clean, clear, and isolated objects, presented to subjects at the center of the screen. In our real environment, however, objects do not appear so neatly. Our visual world is a stimulating scenery mess; fragments, colors, occlusions, motions, eye movements, context, and distraction all affect perception. In this volume, pioneering researchers address the visual cognition of scenes from neuroimaging, psychology, modeling, electrophysiology, and computer vision perspectives.

Building on past research—and accepting the challenge of applying what we have learned from the study of object recognition to the visual cognition of scenes—these leading scholars consider issues of spatial vision, context, rapid perception, emotion, attention, memory, and the neural mechanisms underlying scene representation. Taken together, their contributions offer a snapshot of our current knowledge of how we understand scenes and the visual world around us.

Contributors
Elissa M. Aminoff, Moshe Bar, Margaret Bradley, Daniel I. Brooks, Marvin M. Chun, Ritendra Datta, Russell A. Epstein, Michèle Fabre-Thorpe, Elena Fedorovskaya, Jack L. Gallant, Helene Intraub, Dhiraj Joshi, Kestutis Kveraga, Peter J. Lang, Jia Li Xin Lu, Jiebo Luo, Quang-Tuan Luong, George L. Malcolm, Shahin Nasr, Soojin Park, Mary C. Potter, Reza Rajimehr, Dean Sabatinelli, Philippe G. Schyns, David L. Sheinberg, Heida Maria Sigurdardottir, Dustin Stansbury, Simon Thorpe, Roger Tootell, James Z. Wang

Each edition of this classic reference has proved to be a benchmark in the developing field of cognitive neuroscience. The fifth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biological underpinnings of complex cognitio—the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. It offers entirely new material, reflecting recent advances in the field.

Many of the developments in cognitive neuroscience have been shaped by the introduction of novel tools and methodologies, and a new section is devoted to methods that promise to guide the field into the future—from sophisticated models of causality in brain function to the application of network theory to massive data sets. Another new section treats neuroscience and society, considering some of the moral and political quandaries posed by current neuroscientific methods.

Other sections describe, among other things, new research that draws on developmental imaging to study the changing structure and function of the brain over the lifespan; progress in establishing increasingly precise models of memory; research that confirms the study of emotion and social cognition as a core area in cognitive neuroscience; and new findings that cast doubt on the so-called neural correlates of consciousness.

Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences. Information is inherently dynamic; the same ideas can be organized and reorganized in countless ways, and the ability to translate between such organizational structures is becoming increasingly important in the sciences. Category theory offers a unifying framework for information modeling that can facilitate the translation of knowledge between disciplines.

Written in an engaging and straightforward style, and assuming little background in mathematics, the book is rigorous but accessible to non-mathematicians. Using databases as an entry to category theory, it begins with sets and functions, then introduces the reader to notions that are fundamental in mathematics: monoids, groups, orders, and graphs—categories in disguise. After explaining the “big three” concepts of category theory—categories, functors, and natural transformations—the book covers other topics, including limits, colimits, functor categories, sheaves, monads, and operads. The book explains category theory by examples and exercises rather than focusing on theorems and proofs. It includes more than 300 exercises, with solutions.

Category Theory for the Sciences is intended to create a bridge between the vast array of mathematical concepts used by mathematicians and the models and frameworks of such scientific disciplines as computation, neuroscience, and physics.

Downloadable instructor resources available for this title: 193 exercises, separate from those included in the book, with solutions

Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision.

Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models.

Contributors
A. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Rémi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing

A Critical Appraisal of Cognitive Neuroscience

Cognitive neuroscience explores the relationship between our minds and our brains, most recently by drawing on brain imaging techniques to align neural mechanisms with psychological processes. In Mind and Brain, William Uttal offers a critical review of cognitive neuroscience, examining both its history and modern developments in the field. He pays particular attention to the role of brain imaging--especially functional magnetic resonance imaging (fMRI)--in studying the mind-brain relationship. He argues that, despite the explosive growth of this new mode of research, there has been more hyperbole than critical analysis of what experimental outcomes really mean. With Mind and Brain, Uttal attempts a synoptic synthesis of this substantial body of scientific literature.

Uttal considers psychological and behavioral concerns that can help guide the neuroscientific discussion; work done before the advent of imaging systems; and what brain imaging has brought to recent research. Cognitive neuroscience, Uttal argues, is truly both cognitive and neuroscientific. Both approaches are necessary and neither is sufficient to make sense of the greatest scientific issue of all: how the brain makes the mind.

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