Statistical Mechanics of Neural Networks

Chapter 1:  Introduction

Chapter 2:  Spin Glass Models and Cavity Method

Chapter 3:  Variational Mean-Field Theory and Belief Propagation

Chapter 4:  Monte-Carlo Simulation Methods

Chapter 5:  High-Temperature Expansion Techniques

Chapter 6: Nishimori Model

Chapter 7: Random Energy Model

Chapter 8:  Statistical Mechanics of Hopfield Model

Chapter 9:  Replica Symmetry and Symmetry Breaking

Chapter 10: Statistical Mechanics of Restricted Boltzmann Machine

Chapter 11: Simplest Model of Unsupervised Learning with Binary Synapses

Chapter 12: Inherent-Symmetry Breaking in Unsupervised Learning

Chapter 13: Mean-Field Theory of Ising Perceptron

Chapter 14: Mean-Field Model of Multi-Layered Perceptron

Chapter 15: Mean-Field Theory of Dimension Reduction in Neural Networks

Chapter 16: Chaos Theory of Random Recurrent Networks

Chapter 17: Statistical Mechanics of Random Matrices

Chapter 18: Perspectives