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Statistical Field Theory for Neural Networks

Helias, Moritz Dahmen, David

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Título:
Statistical Field Theory for Neural Networks
Autor: Helias, Moritz
Dahmen, David
Assuntos: Machine Learning ; Mathematical Models of Cognitive Processes and Neural Networks ; Neurosciences ; Physics ; Physics and Astronomy ; Probability and Statistics in Computer Science ; Statistical Physics and Dynamical Systems ; Stochastic differential equations
Descrição: 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.
Títulos relacionados: Lecture Notes in Physics
Editor: Cham: Springer Nature
Data de criação/publicação: 2020
Formato: 213
Idioma: Inglês

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