288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

May 2019, Vol. 31, No. 5, Pages 998-1014
(doi: 10.1162/neco_a_01181)
© 2019 Massachusetts Institute of Technology
Sparse Associative Memory
Article PDF (877.05 KB)
It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies. We call this model sparse associative memory (SAM) because it is based on sparse projections from neural patterns to pattern-specific neurons. These sparse projections have been shown to be sufficient to uniquely encode a neural pattern. Based on this principle, we investigate theoretically and in simulation our SAM model, which turns out to have high memory efficiency and a vanishingly small probability of spurious memories. This model may serve as a basic building block of brain functions involving associative memory.