Neural analog diffusion-enhancement layer and spatio-temporal grouping in early vision
Advances in neural information processing systems 1
Principles and networks for self-organization in space-time
Neural Networks - New developments in self-organizing maps
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Spatiotemporal grouping phenomena are examined in the context of static and time-varying stimuli. Dynamics that exhibit static feature grouping on multiple scales as a function of time, and long-range apparent motion between time-varying visual, auditory, tactile, and multimodal inputs, are developed for a diffusion-enhancement bilayer network. The architecture consists of a diffusion layer and a contrast-enhancement layer coupled by feedforward and feedback connections; time-varying input is provided by a separate feature extracting layer. The model is cast as an analog circuit that is realizable in large VLSI technology, the parameters of which are selected to satisfy a psychophysical database of the following long-range apparent motion phenomena: gamma motion of a single light; smooth motion between two lights; reverse motion; split and merge among three lights; Ternus motion among multiple lights; and peripheral motion. The relation between motion on a uniform network (i.e., cortex) and inputs to a nonuniform sampling array (i.e., retina) are discussed in the context of a logarithmic scaling of visual space. A new interpretation of short-range and long-range visual motion systems is introduced.