Adaptive Perceptual Pattern Recognition by Self-Organizing Neural Networks: Context, Uncertainty, Multiplicity, and Scale

Adaptive Perceptual Pattern Recognition by Self-Organizing Neural Networks: Context, Uncertainty, Multiplicity, and Scale (1995)

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by Jonathan A. Marshall

Venue:	NEURAL NETWORKS
Citations:	19 - 9 self

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@ARTICLE{Marshall95adaptiveperceptual,
    author = {Jonathan A. Marshall},
    title = {Adaptive Perceptual Pattern Recognition by Self-Organizing Neural Networks: Context, Uncertainty, Multiplicity, and Scale},
    journal = {NEURAL NETWORKS},
    year = {1995},
    volume = {8},
    number = {3},
    pages = {335--362}
}

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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 contextsensitive 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 techn...

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