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

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

1999-09-20

arxiv.org/abs/cond-mat/9909276v2

Physics

Condensed Matter

Disordered Systems and Neural Networks

18 pages, 6 figures

Scientific paper

10.1088/0305-4470/33/14/308

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.

Affiliated with

Also associated with

No associations

Rating

Bi-stability of mixed states in neural network storing hierarchical patterns does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Bi-stability of mixed states in neural network storing hierarchical patterns, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bi-stability of mixed states in neural network storing hierarchical patterns will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-429336