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

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 [1]. We also discuss the relation between the theoretical results and the physiological experiments of Sugase et al. PACS numbers: 84.35.+i, 87.10.+e, 05.90.+m, 89.70.+c Typeset using REVTEX 1

DOI: 10.1088/0305-4470/33/14/308

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@inproceedings{Toya1999BistabilityOM, title={Bi-stability of mixed states in neural network storing hierarchical patterns}, author={Kaname Toya and Kunihiko Fukushima and Yoshiyuki Kabashima and Masato Okada Osaka University and The University of Electro-Communications and Tokyo Institute of Technology and Japan Science and Technology Corporation}, year={1999} }