Adaptive perceptual pattern recognition by self-organizing neural networks: Context, uncertainty, multiplicity, and scale

Abstract

A new context-sensitive neural network, called an "EXIN" (excitatory+ inhibitory) network, is described. EXIN networks self-organize in complex perceptual environments, in the presence of multiple superimposed patterns, multiple scales, and uncertainty. The networks use a new inhibitory learning rule, in addition to an excitatory learning rule, to allow superposition of multiple simultaneous neural activations (multiple winners), under strictly regulated circumstances, instead of forcing winner-take-all pattern classifications. The multiple activations represent uncertainty or multiplicity in perception and pattern recognition. Perceptual scission (breaking of linkages) between independent category groupings thus arises and allows effective global context-sensitive segmentation constraint satisfaction, and exclusive credit attribution. A Weber Law neuron-growth rule lets the network learn and classify input patterns despite variations in their spatial scale. Applications of the new techniques include segmentation of superimposed auditory or biosonar signals, segmentation of visual regions, and representation of visual transparency.