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

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.