If you're interested in this you may want to take a look at SDM content-addressable memory, which uses neural networks as address encoders/decoders: https://en

If you're interested in this you may want to take a look at SDM content-addressable memory, which uses neural networks as address encoders/decoders: https://en.wikipedia.org/wiki/Sparse_distributed_memory https://en.wikipedia.org/wiki/Sparse_distributed_memory it was developed by Pentti Kanerva at NASA in the 80s For related memory-augmented models see: https://en.wikipedia.org/wiki/Deep_learning#Networks_with_separate_memory_structures https://en.wikipedia.org/wiki/Deep_learning#Networks_with_se... There was also "Reasoning, Attention, Memory (RAM)" NIPS Workshop 2015 organized by Jason Weston on this topic: http://www.thespermwhale.com/jaseweston/ram/ http://www.thespermwhale.com/jaseweston/ram/ There is disproportionate amount of work on training learning models while ignoring memory mechanics. For some reason this research is pursued by very few machine learning/AI labs, mostly at Google DeepMind/Brain, FB, and Numenta, and may be Tom Mitchell's 'never-ending learning' project at CMU.