Statistical Field Theory for Neural Networks

Statistical Field Theory for Neural Networks
Author :
Publisher : Springer Nature
Total Pages : 213
Release :
ISBN-10 : 9783030464448
ISBN-13 : 303046444X
Rating : 4/5 (48 Downloads)

Book Synopsis Statistical Field Theory for Neural Networks by : Moritz Helias

Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias and published by Springer Nature. This book was released on 2020-08-20 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


Statistical Field Theory for Neural Networks Related Books

Statistical Field Theory for Neural Networks
Language: en
Pages: 213
Authors: Moritz Helias
Categories: Science
Type: BOOK - Published: 2020-08-20 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These power
Statistical Mechanics of Neural Networks
Language: en
Pages: 302
Authors: Haiping Huang
Categories: Science
Type: BOOK - Published: 2022-01-04 - Publisher: Springer Nature

DOWNLOAD EBOOK

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses
The Principles of Deep Learning Theory
Language: en
Pages: 473
Authors: Daniel A. Roberts
Categories: Computers
Type: BOOK - Published: 2022-05-26 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Statistical Field Theory
Language: en
Pages: 366
Authors: Giorgio Parisi
Categories: Science
Type: BOOK - Published: 1998-11-26 - Publisher: Westview Press

DOWNLOAD EBOOK

Specifically written to introduce researchers and advanced students to the modern developments in statistical mechanics and field theory, this book's leitmotiv
Markov Chain Monte Carlo Methods in Quantum Field Theories
Language: en
Pages: 134
Authors: Anosh Joseph
Categories: Science
Type: BOOK - Published: 2020-04-16 - Publisher: Springer Nature

DOWNLOAD EBOOK

This primer is a comprehensive collection of analytical and numerical techniques that can be used to extract the non-perturbative physics of quantum field theor