SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks. (arXiv:2107.05747v4 [cs.LG] UPDATED)

Hebbian plasticity in winner-take-all (WTA) networks is highly attractive for
neuromorphic on-chip learning, owing to its efficient, local, unsupervised, and
on-line nature. Moreover, its biological plausibility may help overcome
important limitations of artificial algorithms, such as their susceptibility to
adversarial attacks, and their high demands for training-example quantity and
repetition. However, Hebbian WTA learning has found little use in machine
learning (ML), likely because it has been missing an optimization theory
compatible with deep learning (DL). Here we show rigorously …

arxiv bayesian bayesian inference inference networks unsupervised