Memorizing binary vector sequences by a sparsely encoded network

We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors by association. A ternary version of the Kanerva memory, folded into a feedback configuration, is shown to perform the basic sequence memorization and regeneration function. The inclusion of lateral connections between the internal cells increases the network capacity considerably and facilitates the correction of individual input patterns and the detection of large errors. The introduction of higher delays in the transmission lines between the external input-output layer and the internal memory layer is shown to further improve the network´s error correction capability