Statistical Field Theory for Neural Networks

Statistical Field Theory for Neural Networks
Author: Moritz Helias
Publisher: Springer Nature
Total Pages: 203
Release: 2020-08-20
Genre: Science
ISBN: 303046444X


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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
Language: en
Pages: 203
Authors: Moritz Helias
Categories: Science
Type: BOOK - Published: 2020-08-20 - Publisher: Springer Nature

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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: 0
Authors: Haiping Huang
Categories:
Type: BOOK - Published: 2021 - Publisher:

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This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses
Statistical Mechanics of Neural Networks
Language: en
Pages: 302
Authors: Haiping Huang
Categories: Science
Type: BOOK - Published: 2022-01-04 - Publisher: Springer Nature

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This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses
Neural Network Modeling
Language: en
Pages: 259
Authors: P. S. Neelakanta
Categories: Technology & Engineering
Type: BOOK - Published: 2018-02-06 - Publisher: CRC Press

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Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings
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

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This volume develops an effective theory approach to understanding deep neural networks of practical relevance.