arXiv:2606. 10238v1 Announce Type: cross Abstract: Neural population geometry shapes downstream computation.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2603.15412v2 Announce Type: replace Abstract: The mammalian brain, most extensively studied in rodents and bats, solves an enormous variety of non-spatial cognitive tasks using neural circuitry...
arXiv:2511. 02584v2 Announce Type: replace-cross Abstract: Associative memory, traditionally modeled by Hopfield networks, enables the retrieval of previously stored patterns from partial or noisy cues.
arXiv:2609.13219v1 Announce Type: cross Abstract: Neural correlates of spatial cognitive map are well documented, yet exactly how neural circuits perform spatial navigation in complex environments -...
arXiv:2606. 11245v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence (AGI).
The paper studies how hierarchical correlations in data can be learned by a dense Hopfield network with polynomial activation. It analytically derives conditions for each level of a hierarchical memory model to be locally stable, meaning they correspond to local energy minima. Using prototype reconstruction as a minimal generalization test, the authors show that only a quasi‑polynomial amount of information is needed to generalize beyond specific memories or groups, and they observe a similar phase diagram for Fashion‑MNIST data.
arXiv:2609.35948v1 Announce Type: cross Abstract: Geometry does more than constrain an associative memory: curvature determines what it remembers and which states it creates. We develop intrinsic den...