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...

Full description

Bibliographic Details
Main Author: Huang, Haiping
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2021, 2021
Edition:1st ed. 2021
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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