Memory Capacity Bounds via Spherical Random Sequential Adsorption
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