A neural network model is proposed that forms sparse spatiotemporal memory traces of spatiotemporal events given single occurrences of the events. The traces are distributed in that each individual cell and synapse participates in numerous traces. This sharing of representational substrate provides the basis for similarity-based generalization and thus semantic memory. Simulation results are provided demonstrating that similar spatiotemporal patterns map to similar traces. The model achieves this property by measuring the degree of match, G, between the current input pattern on each time slice and the expected input given the preceding time slices (i.e., temporal context) and then adding an amount of noise, inversely proportional to G, to the process of choosing the internal representation for the current time slice. Thus, if G is small, indicating novelty, we add much noise and the resulting internal representation of the current input pattern has low overlap with any preexisting representations of time slices. If G is large, indicating a familiar event, we add very little noise resulting in reactivation of all or most of the preexisting representation of the input pattern
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