Titlebook: Brain and Human Body Modeling 2020; Computational Human Sergey N. Makarov,Gregory M. Noetscher,Aapo Nummen Book‘‘‘‘‘‘‘‘ 2021 The Editor(s)

DAFT

https://doi.org/10.1007/978-981-33-4022-0om differences in the electric field (E-field) induced in subjects’ brains during stimulation. The E-field determines how neurons respond to stimulation, and it can be used as a proxy for predicting the concurrent effects of stimulation, like changes in cortical excitability, and, ultimately, its pl

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https://doi.org/10.1007/978-981-13-2592-2e neuromodulation of spinal sensory and motor pathways, with possible clinical application in spinal functional rehabilitation. Modelling studies on the electric field (EF) distribution during tsDCS and tsMS can be powerful tools to understand the underlying biophysics and to guide stimulation proto

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https://doi.org/10.1007/978-981-15-3219-1ychiatric diseases. One of TMS’s significant limitations is its low spatial resolution, which often results in a mismatch between the target area in the brain and the stimulation site on the scalp. To enhance its spatial resolution, we designed and built a complete stimulation system complete with a

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Jinjun Xiong,Yada Zhu,Jingrui He typically have many elements, many modes of interconnectedness of those elements, and often exhibit sensitivity to initial conditions. Complex systems by their nature are generally unpredictable and can be highly unstable.

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Machine Learning in the Oil and Gas Industryanalysis and inter-species connectivity comparisons, and computational power to simulate neural circuitry. The interest in connectomes is reflected in the exponentially rising number of articles on the subject. What are our goals? What are the “functional requirements” of connectome modelers? We giv

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Jinjun Xiong,Yada Zhu,Jingrui He typically have many elements, many modes of interconnectedness of those elements, and often exhibit sensitivity to initial conditions. Complex systems by their nature are generally unpredictable and can be highly unstable.

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