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Both the acquisition of new and the modification of previously acquired motor skills are necessary to achieve optimal levels of motor performance in everyday functioning as well as to attain expert performance levels that are evident in sports and arts. A multitude of factors have been shown to influence the various stages of the learning process, from the acquisition (i.e., motor memory encoding) to the consolidation and subsequent retention of a skill. These factors, or modulators, can affect learning through online processes taking place during practice of a new motor skill or through offline processes occurring in the absence of task performance (i.e., after training sessions). Although much of the recent research from various disciplines has placed an increased emphasis on identifying factors that can influence the motor learning process, we lack an integrated understanding of online and offline determinants of motor skill behaviours. Potential motor learning modulators include, but are certainly not limited to, stress, anxiety, attention, executive functioning, social interaction, stimulus-response mapping, training schedule/regimen, learning environment, vigilance/consciousness states including sleep, wakefulness or meditation, brain stimulation, interference as well as resting state brain connectivity. Pathological and non-pathological (i.e., development or aging) changes in the brain can also be conceptualized as potential modulators. The aim of this Research Topic is to bridge research from the cognitive, sensory, motor and psychological domains using various behavioural paradigms and neuroimaging techniques in order to provide a comprehensive view of the online and offline modulators of motor learning, and how they interact to influence motor performance. Critically, the overarching goal is to gain a better understanding of how motor behaviour can be optimized. We believe that merging research from diverse neuroscientific communities would contribute to fulfilling this goal and potentially highlight possible shared neurophysiological mechanisms influencing motor learning.
Brain Mapping: A Comprehensive Reference, Three Volume Set offers foundational information for students and researchers across neuroscience. With over 300 articles and a media rich environment, this resource provides exhaustive coverage of the methods and systems involved in brain mapping, fully links the data to disease (presenting side by side maps of healthy and diseased brains for direct comparisons), and offers data sets and fully annotated color images. Each entry is built on a layered approach of the content – basic information for those new to the area and more detailed material for experienced readers. Edited and authored by the leading experts in the field, this work offers the most reputable, easily searchable content with cross referencing across articles, a one-stop reference for students, researchers and teaching faculty. Broad overview of neuroimaging concepts with applications across the neurosciences and biomedical research Fully annotated color images and videos for best comprehension of concepts Layered content for readers of different levels of expertise Easily searchable entries for quick access of reputable information Live reference links to ScienceDirect, Scopus and PubMed
The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
Neurorehabilitation is an expanding field with an increasing clinical impact due to an ageing population. During the last 20 years, neurorehabilitation has developed from a discipline with little scientific background, separated from other medical centers, to a medical entity largely based on the principles of 'evidenced based medicine' with strong ties to basic research and clinical neurology. Today neurorehabilitation is still a work in progress and treatment standards are not yet established for all aspects of the field. There are very few books that address contemporary neurorehabilitation from this perspective. This new edition of the Oxford Textbook of Neurorehabilitation provides an understanding of the theoretical underpinnings of the subject as well as a clear perspective on how (and why) to approach treatment decisions on an individualized basis. The book has been thoroughly updated to reflect novel important developments in the field and includes new chapters on vocational rehabilitation, self-management strategies in neurorehabilitation, and music supported therapy in neurorehabilitation. This indispensable book will be of great interest to rehabilitation physicians, neurologists, and allied health care professionals who look after patients requiring neurorehabilitation.
A unified, up-to-date account of circular data-handling techniques, useful throughout science.
A scientific publication system needs to provide two basic services: access and evaluation. The traditional publication system restricts the access to papers by requiring payment, and it restricts the evaluation of papers by relying on just 2-4 pre-publication peer reviews and by keeping the reviews secret. As a result, the current system suffers from a lack of quality and transparency of the peer-review evaluation process, and the only immediately available indication of a new paper’s quality is the prestige of the journal it appeared in. Open access is now widely accepted as desirable and is slowly beginning to become a reality. However, the second essential element, evaluation, has received less attention. Open evaluation, an ongoing post-publication process of transparent peer review and rating of papers, promises to address the problems of the current system. However, it is unclear how exactly such a system should be designed. The evaluation system steers the attention of the scientific community and, thus, the very course of science. For better or worse, the most visible papers determine the direction of each field and guide funding and public policy decisions. Evaluation, therefore, is at the heart of the entire endeavor of science. As the number of scientific publications explodes, evaluation and selection will only gain importance. A grand challenge of our time, therefore, is to design the future system, by which we evaluate papers and decide which ones deserve broad attention. So far scientists have left the design of the evaluation process to journals and publishing companies. However, the steering mechanism of science should be designed by scientists. The cognitive, computational, and brain sciences are best prepared to take on this task, which will involve social and psychological considerations, software design, and modeling of the network of scientific papers and their interrelationships. This Research Topic in Frontiers in Computational Neuroscience collects visions for a future system of open evaluation. Because critical arguments about the current system abound, these papers will focus on constructive ideas and comprehensive designs for open evaluation systems. Design decisions include: Should the reviews and ratings be entirely transparent, or should some aspects be kept secret? Should other information, such as paper downloads be included in the evaluation? How can scientific objectivity be strengthened and political motivations weakened in the future system? Should the system include signed and authenticated reviews and ratings? Should the evaluation be an ongoing process, such that promising papers are more deeply evaluated? How can we bring science and statistics to the evaluation process (e.g. should rating averages come with error bars)? How should the evaluative information about each paper (e.g. peer ratings) be combined to prioritize the literature? Should different individuals and organizations be able to define their own evaluation formulae (e.g. weighting ratings according to different criteria)? How can we efficiently transition toward the future system? Ideally, the future system will derive its authority from a scientific literature on community-based open evaluation. We hope that these papers will provide a starting point.