Title Statistical Field Theory for Neural Networks Author(s) Helias, Moritz;Dahmen, David Publication Cham, Springer International Publishing, 2020. Description XVII, 203 p. 127 illus., 5 illus. in color : online resource Abstract Note 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 ISBN,Price 9783030464448 Keyword(s) 1. EBOOK 2. EBOOK - SPRINGER 3. MACHINE LEARNING 4. Mathematical Models of Cognitive Processes and Neural Networks 5. MATHEMATICAL STATISTICS 6. Neural networks (Computer science)?? 7. Neurosciences 8. Probability and Statistics in Computer Science 9. STATISTICAL PHYSICS 10. Statistical Physics and Dynamical Systems Item Type eBook Multi-Media Links Please Click here for eBook Circulation Data Accession#   Call# Status Issued To Return Due On Physical Location I09583     On Shelf