Statistical Field Theory for Neural Networks (Paperback, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Lecture Notes in Physics, 970
Release date:	August 2020
First published:	2020
Authors:	Moritz Helias • David Dahmen
Dimensions:	235 x 155 x 23mm (L x W x T)
Format:	Paperback
Pages:	203
Edition:	1st ed. 2020
ISBN-13:	978-3-03-046443-1
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Neurosciences Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	3-03-046443-1
Barcode:	9783030464431

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