Semantically-correlated memories in a dense associative model
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- April 2024
- DOI:
- 10.48550/arXiv.2404.07123
- arXiv:
- arXiv:2404.07123
- Bibcode:
- 2024arXiv240407123B
- Keywords:
-
- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- Quantitative Biology - Neurons and Cognition;
- 68T07;
- 92B20;
- 68T01;
- 00A69;
- I.2;
- I.5;
- I.4;
- J.2;
- J.3
- E-Print:
- 35 pages, 32 figures