Naresh B Ravichandran

arXiv2026

Benchmarking local Hebbian learning rules for memory storage and prototype extraction

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 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 different conditions tested.

cs.NE↗

arXiv2025

Benchmarking Hebbian learning rules for associative memory

Associative memory or content addressable memory is an important component function in computer science and information processing and is a key concept in cognitive and computational brain science. Many different neural network architectures and learning rules have been proposed to model associative memory of the brain while investigating key functions like pattern completion and rivalry, noise reduction, and storage capacity. A less investigated but important function is prototype extraction where the training set comprises pattern instances generated by distorting prototype patterns and the task of the trained network is to recall the correct prototype pattern given a new instance. In this paper we characterize these different aspects of associative memory performance and benchmark six different learning rules on storage capacity and prototype extraction. We consider only models with Hebbian plasticity that operate on sparse distributed representations with unit activities in the interval [0,1]. We evaluate both non-modular and modular network architectures and compare performance when trained and tested on different kinds of sparse random binary pattern sets, including correlated ones. We show that covariance learning has a robust but low storage capacity under these conditions and that the Bayesian Confidence Propagation learning rule (BCPNN) is superior with a good margin in all cases except one, reaching a three times higher composite score than the second best learning rule tested.

cs.NE↗