书目名称 | Cybernetics 2.0 | 副标题 | A General Theory of | 编辑 | Bernard Widrow | 视频video | | 概述 | Discusses in depth the role of adaptivity and homeostasis in living systems.Shows how the Hebbian-LMS algorithm can be used to explain homeostatic mechanisms in biology.Discusses plenty of examples re | 丛书名称 | Springer Series on Bio- and Neurosystems | 图书封面 |  | 描述 | .This book takes the notions of adaptivity and learning from the realm of engineering into the realm of biology and natural processes. It introduces a Hebbian-LMS algorithm, an integration of unsupervised Hebbian learning and supervised LMS learning in neural networks, as a mathematical representation of a general theory for synaptic learning in the brain, and adaptation and functional control of homeostasis in living systems. Written in a language that is able to address students and scientists with different backgrounds, this book accompanies readers on a unique journey through various homeostatic processes in living organisms, such as body temperature control and synaptic plasticity, explaining how the Hebbian-LMS algorithm can help understand them, and suggesting some open questions for future research. It also analyses cell signalling pathways from an unusual perspective, where hormones and hormone receptors are shown to be regulated via the principles of the Hebbian-LMS algorithm. It further discusses addiction and pain, and various kinds of mood disorders alike, showing how they can be modelled with the Hebbian-LMS algorithm. For the first time, the Hebbian-LMS algorithm, w | 出版日期 | Book 2023 | 关键词 | Hebbian-LMS algorithm; Hebbian Learning; Homeostasis mechanisms; Synaptic Learning in the Brain; Synapti | 版次 | 1 | doi | https://doi.org/10.1007/978-3-030-98140-2 | isbn_softcover | 978-3-030-98142-6 | isbn_ebook | 978-3-030-98140-2Series ISSN 2520-8535 Series E-ISSN 2520-8543 | issn_series | 2520-8535 | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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