Learning to categorize objects using temporal coherence

Part of Advances in Neural Information Processing Systems 5 (NIPS 1992)

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Suzanna Becker


The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We present an un(cid:173) supervised learning procedure which maximizes the mutual infor(cid:173) mation between the representations adopted by a feed-forward net(cid:173) work at consecutive time steps. We demonstrate that the network can learn, entirely unsupervised, to classify an ensemble of several patterns by observing pattern trajectories, even though there are abrupt transitions from one object to another between trajecto(cid:173) ries. The same learning procedure should be widely applicable to a variety of perceptual learning tasks.