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Lee-Associator—a chaotic auto-associative network for progressive memory recalling by Raymond S.T. Lee is a Computer Science article available to read on EtoBox.

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In the past few decades, neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis and robot vision. Moreover, current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception (e.g. vision, olfaction, etc.) are highly chaotic and involved non-linear neural dynamics and oscillations. In this paper, the author proposes an innovative chaotic neural oscillator-namely the Lee-oscillator (Lee's Chaotic Neural Oscillator) to provide a chaotic neural coding and information processing scheme. To illustrate the capability of Lee-oscillators upon pattern association, a chaotic auto-associative network, namely Lee-Associator (Lee's Chaotic Auto-associator) is constructed. Different from classical auto-associators such as the celebrated Hopfield network, which provides time-independent and static pattern association scheme, the Lee-Associator provides a remarkable progressive memory association scheme (what the author called 'Progressive Memory Recalling Scheme, PMRS') during the chaotic memory associa

Who reads Lee-Associator—a chaotic auto-associative network for progressive memory recalling?

Author

Raymond S.T. Lee

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)

Published

2006

Language

EN

Field

Computer Science (Physical Sciences)