Storing sequences in binary tournament-based neural networks

International audienceAn extension to a recently introduced architecture of clique-based neural networks is presented. This extension makes it possible to store sequences with high eff i ciency. To obtain this property, network connections are provided with orientation and with f l exible redundancy carried by both spatial and temporal redundancy, a mechanism of anticipation being introduced in the model. In addition to the sequence storage with high efficiency, this new scheme also offers biological plausibility. In order to achieve accurate sequence retrieval, a double layered structure combining hetero-association and auto-association is also proposed