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The abilities to perceive, learn, and use generalities, similarities,classes, i.e., semantic memory (SM), is central to cognition. Machine learning(ML), neural network, and AI research has been primarily driven by tasksrequiring such abilities. However, another central facet of cognition,single-trial formation of permanent memories of experiences, i.e., episodicmemory (EM), has had relatively little focus. Only recently has EM-likefunctionality been added to Deep Learning (DL) models, e.g., Neural TuringMachine, Memory Networks. However, in these cases: a) EM is implemented as aseparate module, which entails substantial data movement (and so, time andpower) between the DL net itself and EM; and b) individual items are storedlocalistically within the EM, precluding realizing the exponentialrepresentational efficiency of distributed over localist coding. We describeSparsey, an unsupervised, hierarchical, spatial/spatiotemporal associativememory model differing fundamentally from mainstream ML models, most crucially,in its use of sparse distributed representations (SDRs), or, cell assemblies,which admits an extremely efficient, single-trial learning algorithm that mapsinput similarity into code space similarity (measured as intersection). SDRs ofindividual inputs are stored in superposition and because similarity ispreserved, the patterns of intersections over the assigned codes reflect thesimilarity, i.e., statistical, structure, of all orders, not simply pairwise,over the inputs. Thus, SM, i.e., a generative model, is built as acomputationally free side effect of the act of storing episodic memory tracesof individual inputs, either spatial patterns or sequences. We report initialresults on MNIST and on the Weizmann video event recognition benchmarks. Whilewe have not yet attained SOTA class accuracy, learning takes only minutes on asingle CPU.

Superposed Episodic and Semantic Memory via Sparse Distributed Representation