2026/05/01 by Anders Lansner, Andreas Knoblauch, Naresh B Ravichandran +2
Computer Science · Engineering · Neuroscience · #Ferroelectric and Negative Capacitance Devices #Functional Brain Connectivity Studies #Neural Networks and Applications #cs.LG #cs.NE
paper · pdf · doi:10.1007/s12559-026-10638-y
published as Cognitive Computation (2026) · 31 pages, 9 + 2 suppl figures, 5 tables
arxiv created 2026/05/01 · openalex publication_date 2026/07/31 · openalex created_date 2026/08/01 · openalex updated_date 2026/08/02 · arxiv updated 2026/08/03
Abstract Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same time a key concept in cognitive and computational brain science. Many different neural network architectures and learning rules have been proposed to model the brain’s associative memory while investigating key component functions like figure-ground segmentation, perceptual reconstruction and rivalry. A less investigated but equally important capability of associative memory is prototype extraction where the training set comprises distorted prototype instances and the task is to recall the correct generating prototype given a new distorted instance. In this paper, we benchmark the associative memory function of seven different Hebbian learning rules employed in non-modular and modular recurrent networks with winner-take-all dynamics operating on moderately sparse binary patterns. We measure pattern storage and weight information capacity, prototype extraction capabilities, and sensitivity to correlations in data. The original additive Hebb rule comes out with worst capacity, covariance learning proves to be robust but with moderate capacity, and the Bayesian-Hebbian learning rules show highest capacity in almost all conditions tested.