Spatiotemporal Novelty Detection Using Resonance Networks

Benjamin Rowland and Anthony S. Maida

We present a single-layer recurrent neural network that implements novelty detection for spatiotemporal patterns. The architecture is based on the structure of region CA3 in the hippocampus, which is believed to implement this function. Through analysis and numerical simulation we generate theorems that constrain the operation of this network. We show that once a pattern has been encoded, it will never be miscategorized as novel. We also show that the upper bound on the network capacity is equal to the number of connections. We discuss the tradeoff between generalization and total performance in the architecture.

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