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• W2171414866 abstract "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. 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 involved in temporal (or what we call transient) nonlinear neural dynamics and oscillations. This paper is an extension of the author's previous work on the dynamic neural model (EGDLM) of memory processing and on composite neural oscillators for scene segmentation. Moreover, it is inspired by the work of Aihara et al. and Wang on chaotic neural oscillators in pattern association. In this paper, the author proposes a new transient chaotic neural oscillator, namely the Lee oscillator, to provide temporal neural coding and an information processing scheme. To illustrate its capability for memory association, a chaotic autoassociative network, namely the Transient-Chaotic Auto-associative Network (TCAN) was constructed based on the Lee oscillator. Different from classical autoassociators such as the celebrated Hopfield network, which provides a time-independent pattern association, the TCAN provides a remarkable progressive memory association scheme [what we call progressive memory recalling (PMR)] during the transient chaotic memory association. This is exactly consistent with the latest research in psychiatry and perception psychology on dynamic memory recalling schemes." @default.
• W2171414866 created "2016-06-24" @default.
• W2171414866 creator A5021848908 @default.
• W2171414866 date "2004-09-01" @default.
• W2171414866 modified "2023-10-10" @default.
• W2171414866 title "A Transient-Chaotic Autoassociative Network (TCAN) Based on Lee Oscillators" @default.
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• W2171414866 doi "https://doi.org/10.1109/tnn.2004.832729" @default.
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