Attractor networks are a type of neural network characterized by their ability to store and retrieve patterns through stable states called attractors. These networks enable the processing of information by converging on these attractors in response to input, allowing for the representation of memories, concepts, or sensory experiences. The dynamics of attractor networks closely mirror cognitive functions in the brain, showcasing how neural activity can lead to stable mental states.
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