The Tan-HWG framework treats Hebbian plasticity as Wasserstein minimizing movements of probability measures, with sequential stability conditions on Hebbian energies enabling projections that recover classical rules and explain synaptic competition.
Neuralnetworksandphysicalsystemswithemergentcollectivecomputational abilities.Proceedings of the National Academy of Sciences, 79(8):2554–2558
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Uniform-based discrete diffusion models behave as associative memories that retrieve unseen data, with a dataset-size-driven memorization-to-generalization transition detectable via conditional entropy of token predictions.
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A Wasserstein Geometric Framework for Hebbian Plasticity
The Tan-HWG framework treats Hebbian plasticity as Wasserstein minimizing movements of probability measures, with sequential stability conditions on Hebbian energies enabling projections that recover classical rules and explain synaptic competition.
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Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
Uniform-based discrete diffusion models behave as associative memories that retrieve unseen data, with a dataset-size-driven memorization-to-generalization transition detectable via conditional entropy of token predictions.