Storage Capacity of the Linear Associator: Beginnings of a Theory of Computational Memory

This thesis presents a characterization of a simple connectionist-system, the linear-associator, as both a memory and a classifier. Toward this end, a theory of memory based on information-theory is devised. The principles of the information-theory of memory are then used in conjunction with the dynamics of the linear-associator to discern its storage capacity and classification capabilities as they scale with system size. To determine storage capacity, a set of M vector-pairs called "items" are stored in an associator with N connection-weights. The number of bits of information stored by the system is then determined to be about (N/2)log$\sb2$M. The maximum number of items storable is found to be half the number of weights so that the information capacity of the system is quantified to be (N/2)log$\sb2$N.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Mumme, Dean C.

Contributors dc:contributor

• Schneider, Walter

• Computer Science

Identifier

(UMI)AAI8823208

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/69597