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A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering experimental data. This book brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation. After an overview of the mathematical concepts, including Bayes theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging. Next, they examine the modeling of sensory processing, including the neural coding of information about the outside world, and finally, they explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.

This book presents a theoretical framework for understanding the regularity of the brain’s perceptions, its reactions to sensory stimuli, and its control of movements. The book offers an account of perception as the combination of prediction and observation: the brain builds internal models that describe what should happen and then combines this prediction with reports from the sensory system to form a belief. Considering the brain’s control of movements, and variations despite biomechanical similarities among old and young, healthy and unhealthy, and humans and other animals, chapters review evidence suggesting that motor commands reflect an economic decision made by our brain weighing reward and effort. This evidence also suggests that the brain prefers to receive a reward sooner than later, devaluing or discounting reward with the passage of time; then as the value of the expected reward changes in the brain with the passing of time (because of development, disease, or evolution), the shape of the movements will also change. The internal models formed by the brain provide it with an essential survival skill: the ability to predict based on past observations. The formal concepts presented by the authors offer a way to describe how representations are formed, what structure they have, and how the theoretical concepts can be tested.

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.

Cognitive electrophysiology concerns the study of the brain’s electrical and magnetic responses to both external and internal events. These can be measured using electroencephalograms (EEGs) or magnetoencephalograms (MEGs). With the advent of functional magnetic resonance imaging, another method of tracking brain signals, the tools and techniques of EEG and MEG data acquisition and analysis have been developing at a similarly rapid pace, and this book offers an overview of key recent advances in cognitive electrophysiology. The chapters highlight the increasing overlap in EEG and MEG analytic techniques, describing several methods applicable to both; discuss recent developments, including reverse correlation methods in visual-evoked potentials and a new approach to topographic mapping in high-density electrode montage; and relate the latest thinking on design aspects of EEG/MEG studies, discussing how to optimize the signal-to-noise ratio as well as statistical developments for maximizing power and accuracy in data analysis using repeated-measure ANOVAS.

Although science has made considerable progress in discovering the neural basis of cognition, how consciousness arises remains elusive. In this book, Pennartz analyzes which aspects of conscious experience can be peeled away to access its core: the relationship between brain processes and the qualitative nature of consciousness. Pennartz traces the problem back to its historical foundations and connects early ideas to contemporary computational neuroscience. What can we learn from neural network models, and where do they fall short in bridging the gap between neurons and conscious experiences? How can neural models of cognition help us define requirements for conscious processing in the brain? These questions underlie Pennartz’s examination of the brain’s anatomy and neurophysiology. This analysis is not limited to visual perception but broadened to include other sensory modalities and their integration. Formulating a representational theory, Pennartz outlines properties that complex neural structures must express to process information consciously. This theoretical framework is constructed using empirical findings from neuroscience and from theoretical arguments such as the ‘Cuneiform Room’ and the ‘Wall Street Banker’. Positing that qualitative experience is a multimodal and multilevel phenomenon at its roots, Pennartz places this body of theory in the wider context of mind-brain philosophy.

Cannabinoids and the Brain introduces an informed general audience to the scientific discovery of the endocannabinoid system and recent preclinical research that explains its importance in brain functioning. The endocannabinoids, anandamide and 2-AG, act on the same cannabinoid receptors, that are activated by the primary psychoactive compound found in marijuana, Δ‎⁹-tetrahydrocannabinol (THC). Therefore, the scientific investigations of the functions of the endocannabinoid system are guided by the known effects of marijuana on the brain and body. The book reviews the scientific evidence of the role that the endocannabinoid system plays in regulating emotion, anxiety, depression, psychosis, reward and addiction, learning and memory, feeding, nausea/vomiting, pain, epilepsy, and other neurological disorders. Anecdotal reports are linked with the current scientific literature on the medicinal benefits of marijuana. Cannabis contains over 80 chemicals that have closely related structures, called cannabinoids, but the only major mood-altering constituent is THC. Another major plant cannabinoid is cannabidiol (CBD), which is not psychoactive; yet, considerable recent preclinical research reviewed in various chapters reveals that CBD has promising therapeutic potential in treatment of pain, anxiety, nausea and epilepsy. Only recently, has research been conducted with some of the other compounds found in cannabis. The subject matter of the book is extremely timely in light of the current ongoing debate not only about medical marijuana, but also about its legal status.

The notion that neurons in the living brain can change in response to experience—a phenomenon known as “plasticity”—has become a major conceptual issue in neuroscience research as well as a practical focus for the fields of neural rehabilitation and neurodegenerative disease. Early work dealt with the plasticity of the developing brain and demonstrated the critical role played by sensory experience in normal development. Two broader themes have emerged in recent studies: the plasticity of the adult brain (one of the most rapidly developing areas of current research) and the search for the underlying mechanisms of plasticity—explanations for the cellular, molecular, and epigenetic factors controlling plasticity. Many scientists believe that achieving a fundamental understanding of what underlies neuronal plasticity could help us treat neurological disorders and even improve the learning capabilities of the human brain. This book offers contributions from leaders in the field that cover all three approaches to the study of cerebral plasticity. Chapters look at normal development and the influences of environmental manipulations; cerebral plasticity in adulthood; and underlying mechanisms of plasticity. Others deal with plastic changes in neurological conditions and with the enhancement of plasticity as a strategy for brain repair.

This book offers an introduction to current methods in computational modeling in neuroscience, and describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A “how to” book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. The book is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book. The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists and offers comprehensive coverage with little overlap, and extensive cross-references moving from basic building blocks to more complex applications.

Most neurons in the brain are covered by dendritic spines, small protrusions that arise from dendrites, covering them like leaves on a tree. But a hundred and twenty years after spines were first described by Ramón y Cajal, their function is still unclear. Dozens of different functions have been proposed, from Cajal's idea that they enhance neuronal interconnectivity to hypotheses that spines serve as plasticity machines, neuroprotective devices, or even digital logic elements. This book attempts to solve the “spine problem,” searching for the fundamental function of spines. The text does this by examining many aspects of spine biology that been sources of fascination over the years, including their structure, development, motility, plasticity, biophysical properties, and calcium compartmentalization. it argues that we may never understand how the brain works without understanding the specific function of spines. The book offers a synthesis of the information that has been gathered on spines (much of which comes from studies of the mammalian cortex), linking their function with the computational logic of the neuronal circuits that use them. It argues that once viewed from the circuit perspective, all the pieces of the spine puzzle fit together nicely into a single, overarching function. The book connects these two topics, integrating current knowledge of spines with that of key features of the circuits in which they operate. It concludes with a speculative chapter on the computational function of spines, searching for the ultimate logic of their existence in the brain.

Our intuition tells us that we, our conscious selves, cause our own voluntary acts. Yet scientists have long questioned this; Thomas Huxley, for example, in 1874 compared mental events to a steam whistle that contributes nothing to the work of a locomotive. New experimental evidence (most notably, work by Benjamin Libet and Daniel Wegner) has brought the causal status of human behavior back to the forefront of intellectual discussion. This multidisciplinary collection advances the debate, approaching the question from a variety of perspectives. The contributors begin by examining recent research in neuroscience which suggests that consciousness does not cause behavior, offering the outline of an empirically based model which shows how the brain causes behavior and where consciousness might fit in. Other contributors address the philosophical presuppositions that may have informed the empirical studies, raising questions about what can be legitimately concluded about the existence of free will from Libet’s and Wegner’s experimental results. Others examine the effect recent psychological and neuroscientific research could have on legal, social, and moral judgments of responsibility and blame—in situations including a Clockwork Orange-like scenario of behavior correction.

A fundamental shift is occurring in neuroscience and related disciplines. In the past, researchers focused on functional specialization of the brain, discovering complex processing strategies based on convergence and divergence in slowly adapting anatomical architectures. Yet for the brain to cope with ever-changing and unpredictable circumstances, it needs strategies with richer interactive short-term dynamics. Recent research has revealed ways in which the brain effectively coordinates widely distributed and specialized activities to meet the needs of the moment. This book explores these findings, examining the functions, mechanisms, and manifestations of distributed dynamical coordination in the brain and mind across different species and levels of organization. It identifies three basic functions of dynamic coordination: contextual disambiguation, dynamic grouping, and dynamic routing. The book considers the role of dynamic coordination in temporally structured activity and explores these issues at different levels, from synaptic and local circuit mechanisms to macroscopic system dynamics, emphasizing their importance for cognition, behavior, and psychopathology.

Dyslexia research has made dramatic progress since the mid-1980s. Once discounted as a “middle-class myth,” dyslexia is now the subject of a complex—and confusing—body of theoretical and empirical research. This book provides a uniquely broad and coherent analysis of dyslexia theory. Unlike most dyslexia research, which addresses the question “what is the cause of the reading disability called dyslexia?” the work presented here addressed the deeper question of “what is the cause of the learning disability that manifests as reading problems?” This perspective allows the text to place dyslexia research within the much broader disciplines of cognitive psychology and cognitive neuroscience and has led to a rich framework, including two established leading theories, the automatization deficit account (1990) and the cerebellar deficit hypothesis (2001). The chapters in this book show that extensive evidence has accumulated to support these two theories and that they may be seen as subsuming the established phonological deficit account and sensory processing accounts. Moving to the explanatory level of neural systems, they argue that all these disorders reflect problems in some component of the procedural learning system, a multi-region system including major components of cortical and subcortical regions. The authors’ answer to the fundamental question “what is dyslexia?” offers a challenge and motivation for research throughout the learning disabilities, laying the foundations for future progress.

This book 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, the book 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. The book 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, the book 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, the book 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.

This book shows new ways of thinking about how the brain relates to the world, to cognition, and to behavior. Based on foundations previously established the book considers the implications of these ground rules for thalamic inputs, thalamocortical connections, and cortical outputs. The book argues that functional and structural analyses of pathways connecting thalamus and cortex point beyond these to lower centers and through them to the body and the world. Each cortical area depends on the messages linking it to body and world. These messages relate to the way we act and think; each cortical area receives thalamic inputs and has outputs to motor centers. The book goes on to discuss such topics as the role of branching axons that carry motor instructions as well as copies of these motor instructions for relay to cortex under the control of the thalamic gate. This gate allows the thalamus to control the passage of information on the basis of which cortex relates to the rest of the nervous system.

Humans are awesome. Our brains are gigantic, seven times larger than they should be for the size of our bodies, use 25% of all the energy the body requires each day, and became enormous in hardly any time in evolution, leaving our cousins, the great apes, behind. So the human brain is special, right? Wrong: according to the evidence uncovered by the author, humans have developed cognitive abilities that outstrip those of all other animals because we have a brain built in the image of other primate brains that managed to gather the largest number of neurons in the cerebral cortex due to a technological innovation that allowed a larger caloric intake in less time: cooking.

Through four key themes, this book explores the relationships between language, music, and the brain and the crosstalk between them: (a) song and dance as a bridge between music and language; (b) multiple levels of structure from brain to behavior to culture; (c) the semantics of internal and external worlds and the role of emotion; and (d) the evolution and development of language. Specially commissioned as part of the Strungmann Forum Reports Series, these expositions of current research provide access to experts across disciplines and to non-experts. These chapters provide the background for reports by groups of specialists that chart current controversies and future directions of research on each theme. The book looks beyond mere auditory experience, probing the embodiment that links speech to gesture and music to dance. The study of the brains of monkeys and songbirds illuminates hypotheses on the evolution of brain mechanisms that support music and language, while the study of infants calibrates the developmental timetable of their capacities. The result is a unique book that will interest any reader seeking to learn more about language or music and will appeal especially to readers intrigued by the relationships of language and music with each other and with the brain.

This book offers an interdisciplinary approach to the understanding of human memory, with contributions from both neuroscientists and humanists. Linking the neuroscientific study of memory to the investigation of memory in the humanities, it connects the latest findings in memory research with insights from philosophy, literature, theater, art, music, and film. Chapters from the scientific perspective discuss both fundamental concepts and ongoing debates from genetic and epigenetic approaches, functional neuroimaging, connectionist modeling, dream analysis, and neurocognitive studies. The humanist analyses offer insights about memory from outside the laboratory: a taxonomy of memory gleaned from modernist authors including Virginia Woolf, James Joyce, and William Faulkner; the organization of memory, seen in drama ranging from Hamlet to The Glass Menagerie; procedural memory and emotional memory in responses to visual art; music's dependence on the listener's recall; and the vivid renderings of memory and forgetting in such films as Memento and Eternal Sunshine of the Spotless Mind. The chapters from the philosophical perspective serve as the bridge between science and the arts. The book's introduction offers an integrative merging of neuroscientific and humanistic findings.

This book offers a range of perspectives on a simple problem: How does the brain choose efficiently and adaptively among options to ensure coherent, goal-directed behavior? The contributors, from fields as varied as anatomy, psychology, learning theory, neuroimaging, neurophysiology, behavioral economics, and computational modeling, present an overview of key approaches in the study of cognitive control and decision making. The book presents a survey of cutting-edge research on the topic. The chapters consider such topics as the anatomical and physiological basis of control, examining core components of the control system, including contributions of the cerebral cortex, the ways in which subcortical brain regions underpin the control functions of the cortex, and neurotransmitter systems; variations in control seen in the development from adolescence to adulthood, in healthy adults, and in patient populations; recent developments in computational approaches, including reinforcement learning; and overarching trends in the current literature, including neuroeconomics, social decision making, and model-based approaches to data from neuroimaging and electrophysiology.

For many psychiatric disorders, neurobiological findings do not help to diagnose a specific disease or to predict its outcome. This book suggests to take a new look at mental disorders by using computational models to better understand human decision making. It shows how such models can be applied to basic learning mechanisms that cut across established nosological boundaries of mental disorders. Such a computational and dimensional approach focuses on the malleability of human behavior and its biological underpinnings. The book argues that this computational and dimensional approach can help to promote and focus neurobiological research, however, it does not replace an anthropological understanding of clinical questions including the definition of mental disorders and ethical considerations. This is illustrated by describing the new understanding of mental disorders with respect to clinical and neuro-computational aspects of psychosis, affective and addictive disorders.

Cognitive science is experiencing a pragmatic turn away from the traditional representation-centered framework toward a view that focuses on understanding cognition as “enactive.” This enactive view holds that cognition does not produce models of the world but rather subserves action as it is grounded in sensorimotor skills. In this volume, experts from cognitive science, neuroscience, psychology, robotics, and philosophy of mind assess the foundations and implications of a novel action-oriented view of cognition. Their contributions and supporting experimental evidence show that an enactive approach to cognitive science enables strong conceptual advances, and the chapters explore key concepts for this new model of cognition. The contributors discuss the implications of an enactive approach for cognitive development; action-oriented models of cognitive processing; action-oriented understandings of consciousness and experience; and the accompanying paradigm shifts in the fields of philosophy, brain science, robotics, and psychology.