title: Cross-talk theory of memory capacity in neural networks.
abstract: The present paper presents a theory for the mechanics of cross-talk among constituent neurons in networks in which multiple memory traces have been embedded, and develops criteria for memory capacity based on the disruptive influences of this cross-talk. The theory is based on interconnection patterns defined by the sequential configuration model of dynamic firing patterns. The theory accurately predicts the memory capacities observed in computer simulated nets, and predicts that cortical-like modules should be able to store up to about 300-900 selectively retrievable memory traces before disruption by cross-talk is likely. It also predicts that the cortex may has designed itself for modules of 30,000 neurons to at least in part to optimize memory capacity.
authors.affiliation: Aerospace Engineering Sciences, University of Colarado, Boulder 80309-0429.
authors.forename: R J
authors.initials: RJ
authors.lastname: MacGregor
authors.forename: G L
authors.initials: GL
authors.lastname: Gerstein
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology; D008433:Mathematics; D008568:Memory; D008959:Models, Neurological; D008960:Models, Psychological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*