Analysis and synthesis of associative memories based on Brain-State-in-a-Box neural networks

Zhigang Zeng, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, a design procedure is presented for synthesizing associative memories based on the Brain-State-in-a-Box neural network model. The theoretical analysis herein guarantees that the desired memory patterns are stored as asymptotically stable equilibrium points with very few spurious states. In order to avoid extensive computation, learning and forgetting are utilized by adding patterns to be stored as asymptotically stable equilibrium points to an existing set of stored patterns and deleting specified patterns from a given set of stored patterns without affecting the rest in a given network. Furthermore, the number of the memorized patterns in a designed Brain-State-in-a-Box neural network model can be made much more than that of neurons. Simulation results demonstrate the validity and characteristics of the proposed approach.

Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages3512-3519
Number of pages8
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: 14 Jun 200919 Jun 2009

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/TerritoryUnited States
CityAtlanta, GA
Period14/06/0919/06/09

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