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Scene learning and glance recognizability based on competitively growing spiking neural network

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1 Author(s)
Atsumi, M. ; Dept. of Inf. Syst. Sci., Soka Univ., Tokyo, Japan

We have been building the competitively growing spiking neural network for quick one-shot object learning and glance object recognition, which is the core of our saliency-based scene memory model. This neural network represents objects using latency-based temporal coding and grows size and recognizability through learning and self-organization. Through simulation experiments of a robot equipped with a camera, it is shown that object and scene learning and glance object recognition are well performed by our model.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004