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Titlebook: Probabilistic Spiking Neuronal Nets; Neuromathematics for Antonio Galves,Eva Löcherbach,Christophe Pouzat Book 2024 The Editor(s) (if appli

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书目名称Probabilistic Spiking Neuronal Nets
副标题Neuromathematics for
编辑Antonio Galves,Eva Löcherbach,Christophe Pouzat
视频video
概述provides a self-contained introduction to a new class of stochastic models for systems of spiking neurons.directed at mathematicians and neuroscientists and emphasizes a constructive and algorithmic p
丛书名称Lecture Notes on Mathematical Modelling in the Life Sciences
图书封面Titlebook: Probabilistic Spiking Neuronal Nets; Neuromathematics for Antonio Galves,Eva Löcherbach,Christophe Pouzat Book 2024 The Editor(s) (if appli
描述This book provides a self-contained introduction to a new class of stochastic models for systems of spiking neurons. These systems have a large number of interacting components, each one evolving as a stochastic process with a memory of variable length. Several mathematical tools are put to use, such as Markov chains, stochastic chains having memory of variable length, point processes having stochastic intensity, Hawkes processes, random graphs, mean field limits, perfect sampling algorithms, the Context algorithm, and statistical model selection..The book’s focus on mathematically tractable objects distinguishes it from other texts on theoretical neuroscience. The biological complexity of neurons is not ignored, but reduced to some of its main features, such as the intrinsic randomness of neuronal dynamics. This reduction in complexity aims at explaining and reproducing statistical regularities and collective phenomena that are observed in experimental data, an approach that leads to mathematically rigorous results. ..With an emphasis on a constructive and algorithmic point of view, this book is directed towards mathematicians interested in learning about stochastic network models
出版日期Book 2024
关键词mathematical neuroscience; mathematical statistics; spiking neurons; stochastic chains; probability; chai
版次1
doihttps://doi.org/10.1007/978-3-031-68409-8
isbn_softcover978-3-031-68408-1
isbn_ebook978-3-031-68409-8Series ISSN 2193-4789 Series E-ISSN 2193-4797
issn_series 2193-4789
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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