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Statistical Field Theory for Neural Networks. 1st ed. 2020
・ISBN 978-3-030-46443-1 paper EUR 74.99
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| 著者・編者 | Helias, Moritz / Dahmen, David, |
|---|---|
| シリーズ | (Lecture Notes in Physics) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2020 |
| ページ数 | 203 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-59147＞ |
解説
This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks.
This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.