Memory Capacity Bounds via Spherical Random Sequential Adsorption

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Authors: Gabriel Aubin-Morneau
Cited by: 0

Abstract

Paper 1 derives the first fundamental constraint of adaptive memory systems: a geometric capacity limit. Using sphere-packing arguments and random sequential adsorption on the state manifold, it shows that the number of distinct stable representations is bounded by the geometry of the representational space rather than simply by dimensionality. This result explains why increasing parameter count or embedding width does not indefinitely increase usable representational capacity.

Fields of Study & Subjects

Neural Networks and Applications Cognitive Computing and Networks Memory and Neural Mechanisms

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 March 2026
Language
EN