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arxiv: cond-mat/9909276 · v2 · submitted 1999-09-20 · ❄️ cond-mat.dis-nn · cond-mat.stat-mech· q-bio

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

classification ❄️ cond-mat.dis-nn cond-mat.stat-mechq-bio
keywords patternsstatesdiscusshierarchicalmixedmodelstoringferromagnetic
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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.

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