papersSEP 12 04:00 UTC
Paper Explores Probabilistic In-Memory Hardware for Bayesian Learning
A new arXiv preprint examines how the neural dynamics behind Bayesian learning and decision-making in animals could be recreated in hardware. The authors propose using probabilistic in-memory computing circuits to integrate sensory evidence with prior beliefs under uncertainty. The work is presented as the first part of a series linking bio-inspired computation to physical device design.