Two decades of single unit recording in monkeys performing short-term memory tasks has established that information can be stored as sustained neural activity. The mechanism of this information storage is unknown. The learning-based model described here demonstrates that a mechanism using only the dynamic activity in recurrent networks is sufficient to account for the observed phenomena. The temporal activity patterns of neurons in the model match those of real memory-associated neurons, while the model's gating properties and attractor dynamics provide explanations for puzzling aspects of the experimental data.

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