Abstract
The development of the concept of feature binding as fundamental to neural dynamics has made possible recent advances in the modeling of difficult problems of perception and brain function. Major weaknesses of past neural modeling (most prominently its inability to work with natural stimuli and its ‘learning-time’ barrier) have been traced back to improper treatment of the binding issue. Signal synchrony is now seen as playing a major role in binding. Inclusion of temporal binding in neural models has led to recent breakthroughs in solving important perceptual problems. Among them is perceptual segmentation, invariant object recognition and natural language parsing, as well as overcoming the ‘learning-time’ barrier.
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