Statistical Mechanics of Neural Networks /

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in details important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition...

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Bibliographic Details
Main Author:	Huang, Haiping (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore : Imprint: Springer, 2021.
Edition:	1st ed. 2021.
Subjects:	Statistical Physics. Neural networks (Computer science) . Statistical Mechanics. Computational intelligence. Artificial intelligence. Mathematical Models of Cognitive Processes and Neural Networks. Computational Intelligence. Artificial Intelligence. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Introduction
Spin glass models and cavity method
Variational mean-ﬁeld theory and belief propagation
Monte Carlo simulation methods
High-temperature expansion
Nishimori line
Random energy model
Statistical mechanical theory of Hopﬁeld model
Replica symmetry and replica symmetry breaking
Statistical mechanics of restricted Boltzmann machine
Simplest model of unsupervised learning with binary synapses
Inherent-symmetry breaking in unsupervised learning
Mean-ﬁeld theory of Ising Perceptron
Mean-ﬁeld model of multi-layered Perceptron
Mean-ﬁeld theory of dimension reduction
Chaos theory of random recurrent neural networks
Statistical mechanics of random matrices
Perspectives.