Statistical Field Theory for Neural Networks
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
Provides the first self-contained introduction to field theory for neuronal networks Presents the main concepts from field theory that are relevant for network dynamics, including diagrammatic techniques and systematic perturbative and fluctuation expansions Introduces advanced concepts, like the effective action formalism, in mathematical minimal setting Includes in-depth derivations of classical seminal works and recent developments, such as the dynamical mean-field theory and chaos
Autor*in
Moritz Helias
Themen in »Statistical Field Theory for Neural Networks«
Statistical physics Neuronal networks Dynamic mean-field theory Diagrammatic techniques Chaotic network dynamics Correlated neuronal activity