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Statistical field theory for neural 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...

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Detalles Bibliográficos
Autores principales: Helias, Moritz, Dahmen, David
Lenguaje:eng
Publicado: Springer 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-46444-8
http://cds.cern.ch/record/2729480
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author Helias, Moritz
Dahmen, David
author_facet Helias, Moritz
Dahmen, David
author_sort Helias, Moritz
collection CERN
description 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.
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institution Organización Europea para la Investigación Nuclear
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spelling cern-27294802021-04-21T18:05:09Zdoi:10.1007/978-3-030-46444-8http://cds.cern.ch/record/2729480engHelias, MoritzDahmen, DavidStatistical field theory for neural networksMathematical Physics and MathematicsThis 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.Springeroai:cds.cern.ch:27294802020
spellingShingle Mathematical Physics and Mathematics
Helias, Moritz
Dahmen, David
Statistical field theory for neural networks
title Statistical field theory for neural networks
title_full Statistical field theory for neural networks
title_fullStr Statistical field theory for neural networks
title_full_unstemmed Statistical field theory for neural networks
title_short Statistical field theory for neural networks
title_sort statistical field theory for neural networks
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-46444-8
http://cds.cern.ch/record/2729480
work_keys_str_mv AT heliasmoritz statisticalfieldtheoryforneuralnetworks
AT dahmendavid statisticalfieldtheoryforneuralnetworks