Chaotic Associative Recalls for Fixed Point Attractor Patterns

Liang Zhao, Juan Carlos Gutierrez Caceres, Harold Szu

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


Human perception is a complex nonlinear dynamics. On the one hand it is periodic dynamics and on the other hand it is chaotic. Thus, we wish to propose a hybrid - the spatial chaotic dynamics for the associative recall to retrieve patterns, similar to Walter Freeman's discovery, and the fixed point dynamics for memory storage, similar to Hopfield and Grossberg's discoveries. In this model, each neuron in the network could be a chaotic map, whose phase space is divided into two states: one is periodic dynamic state with period-V, which is used to represent a V-value retrieved pattern; another is chaotic dynamic state. Firstly, patterns are stored in the memory by fixed point learning algorithm. In the retrieving process, all neurons are initially set in the chaotic region. Due to the ergodicity property of chaos, each neuron will approximate the periodic points covered by the chaotic attractor at same instants. When this occurs, the control is activated to drive the dynamic of each neuron to their corresponding stable periodic point. Computer simulations confirm the theoretical prediction.

Original languageEnglish
Number of pages5
StatePublished - 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 20 Jul 200324 Jul 2003


ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR


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