Bi-stability of mixed states in neural network storing hierarchical patterns

TL;DR

This paper investigates the bi-stability of symmetric mixed states in a neural network model that stores hierarchically correlated patterns, revealing first-order transitions and discussing physiological implications.

Contribution

It demonstrates the bi-stability of mixed states in hierarchical pattern storage models using statistical mechanics and simulations, linking results to neural physiology.

Findings

Bi-stability of symmetric mixed states in the model.

First-order phase transition occurs in the ferromagnetic phase.

Potential implications for neural information processing.

Abstract

We discuss the properties of equilibrium states in an autoassociative memory model storing hierarchically correlated patterns (hereafter, hierarchical patterns). We will show that symmetric mixed states (hereafter, mixed states) are bi-stable on the associative memory model storing the hierarchical patterns in a region of the ferromagnetic phase. This means that the first-order transition occurs in this ferromagnetic phase. We treat these contents with a statistical mechanical method (SCSNA) and by computer simulation. Finally, we discuss a physiological implication of this model. Sugase et al. analyzed the time-course of the information carried by the firing of face-responsive neurons in the inferior temporal cortex. We also discuss the relation between the theoretical results and the physiological experiments of Sugase et al.