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Titlebook: Cybernetics 2.0; A General Theory of Bernard Widrow Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to

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发表于 2025-3-21 16:21:08 | 显示全部楼层 |阅读模式
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Titlebook: Cybernetics 2.0; A General Theory of  Bernard Widrow Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to
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
doihttps://doi.org/10.1007/978-3-030-98140-2
isbn_softcover978-3-030-98142-6
isbn_ebook978-3-030-98140-2Series ISSN 2520-8535 Series E-ISSN 2520-8543
issn_series 2520-8535
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Lyle Yorks,Judy O’Neil,Victoria Marsicks. The % Hebbian-LMS algorithm implements the extended Hebbian learning rules for small values of the postsynaptic membrane potential, but at larger values, the Hebbian rules begin to break down. The % Hebbian-LMS algorithm applies at small and large values of the membrane potential and predicts the phenomenon of homeostasis.
发表于 2025-3-22 05:53:55 | 显示全部楼层
Hebbian Learningesented to support a “spike timing” learning rule. Actually their data casts doubt on the spike timing hypothesis. Another more consistent analysis suggests that the extended Hebbian rules are more likely to be nature’s learning rules.
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https://doi.org/10.1007/978-90-481-3935-4esented to support a “spike timing” learning rule. Actually their data casts doubt on the spike timing hypothesis. Another more consistent analysis suggests that the extended Hebbian rules are more likely to be nature’s learning rules.
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