Attractor networks are computational models that represent how neural circuits can maintain stable patterns of activity, particularly in the context of working memory. These networks create a landscape of attractors, where each attractor corresponds to a particular memory or state, allowing for the persistent activity associated with maintaining information over short periods. The structure of these networks facilitates the retrieval and stabilization of memories through recurrent connections among neurons.
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