階層型神経回路モデルによる時空間パタ-ンの認識 (ニュ-ラルネットワ-クの応用総合特集号)

概要

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In this paper, a neural network model is proposed which can recognize spatiotemporal patterns. The model is composed of feature detection module and feature integration module. The former extracts time-pecific features such as onsets and offsets of input signals. The latter consists of two types of neurons, a P-neuron and a T-neuron. A P-neuro integrates features. A T-neuron has a memory mechanism with input-dependent decay time. Pairs of these two types of neural layers are connected in cascade in the model. Combination of these two types of layers in hierarchical structure produces an ability of duration-independent recognition of spatio-temporal patterns. In a computer simulation, it has been shown that the model can tolerate wide range of distortion of input patterns such as elongation and shrink along the temporal axis, shift along spatial axis and deformation in shape.
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