ArticleFrontiers in computational neuroscience2025
Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks.
Article in Frontiers in computational neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters. We derive analytical expressions for maximum theoretical memory capacity and introduce a grid-based construction and sub-sampling method for pattern generation that takes advantage of the full storage potential of the network. Our findings indicate that maximum capacity scales as (
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