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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.

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.