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Sensory-Motor Areas and Aspects of Cortical Connectivity
Volume 5 of Cerebral Cortex completes the sequence of three volumes on the individual functional areas of the cerebral cortex by covering the somatosensory and motor areas. However, the chapters on these areas lead naturally to a series of others on patterns of connectivity in the cortex, intracortical and subcortical, so that the volume as a whole achieves a much broader viewpoint. The individual chapters on the sensory-motor areas reflect the considerable diversity of interest within the field, for each of the authors has given his or her chapter a different emphasis, reflecting in part topical interest and in part the body of data resulting from work in a particular species. In considerin...
The Computational Neurobiology of Reaching and Pointing
An introduction to the computational biology of reaching and pointing, with an emphasis on motor learning. Neuroscience involves the study of the nervous system, and its topics range from genetics to inferential reasoning. At its heart, however, lies a search for understanding how the environment affects the nervous system and how the nervous system, in turn, empowers us to interact with and alter our environment. This empowerment requires motor learning. The Computational Neurobiology of Reaching and Pointing addresses the neural mechanisms of one important form of motor learning. The authors integrate material from the computational, behavioral, and neural sciences of motor control that is...
The Neuropsychology of Anxiety
The Neuropsychology of Anxiety first appeared in 1982 as the first volume in the Oxford Psychology Series, and it quickly established itself as a classic work in the psychology and neuroscience literature. It presented an innovative, and at times controversial, theory of anxiety and the brain systems, especially the septo-hippocampal system, that subserve it. This completely updated and revised third edition provides a further updated theory of septo hippocampal function combined with an improved understanding of anxiety. The book includes a new chapter on prefrontal cortex integrating frontal and hippocampal views of anxiety, as well as an extensively modified chapter on personality providing a new basis for further developments of Reinforcement Sensitivity Theory. In addition, numerous figures have been fully updated and converted to colour to support the text. This book is essential for postgraduate students and researchers in experimental psychology and neuroscience, as well as for all clinical psychologists and psychiatrists.
Scientific Directory and Annual Bibliography
Each issue lists papers published during the preceding year.
An Introductory Course in Computational Neuroscience
A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and...
Neurobiology of Sensation and Reward
Synthesizing coverage of sensation and reward into a comprehensive systems overview, Neurobiology of Sensation and Reward presents a cutting-edge and multidisciplinary approach to the interplay of sensory and reward processing in the brain. While over the past 70 years these areas have drifted apart, this book makes a case for reuniting sensation a
Visual Cortex and Deep Networks
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications. The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks—which do not reflect several important features of the ventral stream architecture and physiology—have been trained with extremely large datasets, resulting in model neurons that mimic object recognition but do not explain the nature of the computations carried o...
Modeling Neural Circuits Made Simple with Python
An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between...
Brain Computation as Hierarchical Abstraction
An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is.
Principles of Brain Dynamics
Experimental and theoretical approaches to global brain dynamics that draw on the latest research in the field. The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field. It includes contributions from both theoreticians and experimentalists, offering an eclectic treatment of fundamental issues. Topics addressed range from experimental and computational approaches to transient brain dynamics to the free-energy principle as a global brain theory. The book concludes with a short but rigorous guide to modern nonlinear dynamics and their application to neural dynamics.
