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Titlebook: Mathematical and Theoretical Neuroscience; Cell, Network and Da Giovanni Naldi,Thierry Nieus Book 2017 Springer International Publishing AG

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書(shū)目名稱Mathematical and Theoretical Neuroscience
副標(biāo)題Cell, Network and Da
編輯Giovanni Naldi,Thierry Nieus
視頻videohttp://file.papertrans.cn/627/626727/626727.mp4
概述Offers a survey on recent hot topics in computational neuroscience.Includes state of the art chapters (within a huge literature).Provides recent applications using big data and modelling
叢書(shū)名稱Springer INdAM Series
圖書(shū)封面Titlebook: Mathematical and Theoretical Neuroscience; Cell, Network and Da Giovanni Naldi,Thierry Nieus Book 2017 Springer International Publishing AG
描述This volume gathers contributions from theoretical, experimental and computational researchers who are working on various topics in theoretical/computational/mathematical neuroscience. The focus is on mathematical modeling, analytical and numerical topics, and statistical analysis in neuroscience with applications. The following subjects are considered:?mathematical modelling in Neuroscience, analytical ?and numerical topics; ?statistical analysis in Neuroscience;?Neural Networks;?Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience.
出版日期Book 2017
關(guān)鍵詞Computational Neurosciences; Theoretical Neurosciences; Biophysics of neuron; Biological neural network
版次1
doihttps://doi.org/10.1007/978-3-319-68297-6
isbn_softcover978-3-030-09828-5
isbn_ebook978-3-319-68297-6Series ISSN 2281-518X Series E-ISSN 2281-5198
issn_series 2281-518X
copyrightSpringer International Publishing AG, part of Springer Nature 2017
The information of publication is updating

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Book 2017th applications. The following subjects are considered:?mathematical modelling in Neuroscience, analytical ?and numerical topics; ?statistical analysis in Neuroscience;?Neural Networks;?Theoretical Neuroscience. The book is addressed to researchers involved in mathematical models applied to neuroscience.
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Simultaneous Jumps in Interacting Particle Systems: From Neuronal Networks to a General Framework, studied for some special cases, recently a study of propagation of chaos and of the corresponding McKean-Vlasov equation has appeared in a general framework. Here we justify the link between this approach and the neuronal models.
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Theory and Application of Nonlinear Time Series Analysis, particular, testing their capability to extract hidden information from these signals. Moreover, the results of the analysis of Local Field Recordings (LFP), measured from electrodes placed on the visual cortex of mice, will be discussed too.
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