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This work builds, directly from head-direction cell data, recurrent networks that behave as quasicontinuous attractors, and recovers the ingredients of classical ring attractors, namely, Mexican hat interactions and a spontaneously broken symmetry producing bump states.
An analytical mean-field theory governed mainly by the variability along true-class and rival-class centroid coordinates, together with a global renormalization of the class radius that compensates for the non-Gaussian statistics of real representations is derived.
A large language model is used as a model system for continual episodic memory, with its pretrained weights supplying the semantic context in which new episodes are embedded, yielding a neural-network model that stores, recalls, and consolidates naturalistic episodic memories, thereby capturing key functional features...
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