Min side Kundeservice Gavekort – en perfekt gave Registrer deg

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 to pressing open problems in theoretical neuroscience and also machine learning. Les mer

943,-
Paperback
Sendes innen 7 virkedager
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.

Detaljer

Forlag
Springer Nature Switzerland AG
Innbinding
Paperback
Språk
Engelsk
Sider
203
ISBN
9783030464431
Utgivelsesår
2020
Format
24 x 16 cm

Om forfatteren

Moritz Helias is group leader at the Julich Research Centre and assistant professor in the department of physics of the RWTH Aachen University, Germany. He obtained his diploma in theoretical solid state physics at the University of Hamburg and his PhD in computational neuroscience at the University of Freiburg, Germany. Post-doctoral positions in RIKEN Wako-Shi, Japan and Julich Research Center followed. His main research interests are neuronal network dynamics and function, and their quantitative analysis with tools from statistical physics and field theory.

David Dahmen is a post-doctoral researcher in the Institute of Neuroscience and Medicine at the Julich Research Centre, Germany. He obtained his Master's degree in physics from RWTH Aachen University, Germany, working on effective field theory approaches to particle physics. Afterwards he moved to the field of computational neuroscience, where he received his PhD in 2017. His research comprises modeling, analysis and simulation of recurrent neuronal networks with special focus on development and knowledge transfer of mathematical tools and simulation concepts. His main interests are field-theoretic methods for random neural networks, correlations in recurrent networks, and modeling of the local field potential.

Kunders vurdering

Oppdag mer

Bøker som ligner på Statistical Field Theory for Neural Networks:

Se flere

Logg inn

Ikke medlem ennå? Registrer deg her

Glemt medlemsnummer/passord?

Handlekurv