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 association. This is exactly consistent with the latest research in psychiatry and perception psychology on dynamic memory recalling schemes, as well as the implications and analogues to human perception as illustrated by the remarkable Rubin-vase experiment on visual psychology.