Semantically-correlated memories in a dense associative model
<|reference_start|>Semantically-correlated memories in a dense associative model: I introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns. Employing an arbitrary graph structure to semantically link memory patterns, CDAM is theoretically and numerically analysed, revealing four distinct dynamical modes: auto-association, narrow hetero-association, wide hetero-association, and neutral quiescence. Drawing inspiration from inhibitory modulation studies, I employ anti-Hebbian learning rules to control the range of hetero-association, extract multi-scale representations of community structures in graphs, and stabilise the recall of temporal sequences. Experimental demonstrations showcase CDAM's efficacy in handling real-world data, replicating a classical neuroscience experiment, performing image retrieval, and simulating arbitrary finite automata.<|reference_end|>
@article{burns2024semantically-correlated,
    title={Semantically-correlated memories in a dense associative model},
    author={Thomas F Burns},
    journal={arXiv preprint arXiv:2404.07123},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07123},
    primaryClass={cs.NE cs.AI cs.LG q-bio.NC}
}