Cogprints

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

Marshall, J.A. (1995) Adaptive perceptual pattern recognition by self-organizing neural networks: Context, uncertainty, multiplicity, and scale. [Journal (Paginated)]

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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.

Item Type:Journal (Paginated)
Subjects:Computer Science > Complexity Theory
Computer Science > Machine Learning
Computer Science > Machine Vision
Computer Science > Neural Nets
Psychology > Perceptual Cognitive Psychology
ID Code:440
Deposited By:Marshall, Jonathan
Deposited On:28 Apr 1998
Last Modified:11 Mar 2011 08:53

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