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Titlebook: Reservoir Computing; Theory, Physical Imp Kohei Nakajima,Ingo Fischer Book 2021 Springer Nature Singapore Pte Ltd. 2021 Reservoir Computing

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書目名稱Reservoir Computing
副標(biāo)題Theory, Physical Imp
編輯Kohei Nakajima,Ingo Fischer
視頻videohttp://file.papertrans.cn/829/828152/828152.mp4
概述The first comprehensive book on reservoir computing.Provides an introduction and cutting-edge research in a wide range of domains.Contributed by leading researchers in the field
叢書名稱Natural Computing Series
圖書封面Titlebook: Reservoir Computing; Theory, Physical Imp Kohei Nakajima,Ingo Fischer Book 2021 Springer Nature Singapore Pte Ltd. 2021 Reservoir Computing
描述.This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications..The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored byleading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamic
出版日期Book 2021
關(guān)鍵詞Reservoir Computing; Neural Networks; Machine Learning; Soft Robotics; Signal Processing; dynamical syste
版次1
doihttps://doi.org/10.1007/978-981-13-1687-6
isbn_ebook978-981-13-1687-6Series ISSN 1619-7127 Series E-ISSN 2627-6461
issn_series 1619-7127
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

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Reservoir Computing Leveraging the Transient Non-linear Dynamics of Spin-Torque Nano-Oscillators platform to test these components, because a single component can emulate a whole neural network. Using this method, we classify sine and square waveforms perfectly and achieve spoken-digit recognition with state of the art results. We illustrate optimization of the oscillator’s operating regime with sine/square classification.
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The Cerebral Cortex: A Delay-Coupled Recurrent Oscillator Network?ose of natural brains, suggesting that the two systems share the same computational principles. In this chapter, evidence is reviewed which indicates that the computational operations of natural systems differ in some important aspects from those implemented in artificial systems. Natural processing
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Cortico-Striatal Origins of Reservoir Computing, Mixed Selectivity, and Higher Cognitive Functioning that in this context, one of the most prevalent features of the cerebral cortex is its massive recurrent connectivity. Despite this central principle of cortical organization, it is only slowly becoming recognized that the cortex is a reservoir. Of course there are mechanisms in the cortex that
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Deep Reservoir Computingttention in the neural networks community. Within this context, we focus on describing the major features of Deep Echo State Networks based on the hierarchical composition of multiple reservoirs. The intent is to provide a useful reference to guide applications and further developments of this effic
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