Description
| Product ID: | 9783030464431 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | CH |
| Series: | Lecture Notes in Physics |
| Title: | Statistical Field Theory for Neural Networks |
| Authors: | Author: David Dahmen, Moritz Helias |
| Page Count: | 203 |
| Subjects: | Mathematical modelling, Mathematical modelling, Statistical physics, Neurosciences, Mathematical and statistical software, Maths for computer scientists, Machine learning, Statistical physics, Neurosciences, Mathematical & statistical software, Maths for computer scientists, Machine learning |
| Description: | Select Guide Rating This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. 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. |
| Imprint Name: | Springer Nature Switzerland AG |
| Publisher Name: | Springer Nature Switzerland AG |
| Country of Publication: | GB |
| Publishing Date: | 2020-08-21 |
