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Computational Neuroscience

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Advances in Neuroelectric and Neuromagnetic Methods
Edited by Todd C. Handy

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 (fMRI), another method of tracking brain signals, the tools and techniques of ERP, 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.

Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only.

Probabilistic Approaches to Neural Coding

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.

From Systems to Brains

Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.

The Geometry of Excitability and Bursting

In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology.

Proceedings of the 2005 Conference

The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications.

Computational and Mathematical Modeling of Neural Systems

Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.

Head direction cells—neurons that fire only when an animal orients its head in a certain direction—are found in several different brain areas, with different neurons selective for different head orientations; they are influenced by landmarks as well as motor and vestibular information concerning how the head moves through space. These properties suggest that head direction cells play an important role in determining orientation in space and in navigation.

Proceedings of the 2004 Conference

The annual Neural Information Processing Systems (NIPS) conference is the flagship meeting on neural computation. It draws a diverse group of attendees—physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging, vision, speech and signal processing, reinforcement learning and control, emerging technologies, and applications.

A Foundation for 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.

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