Efficient connection strategies in 1D and 2D associative memory models with and without displaced connectivity.
This study examines the performance of sparsely connected associative memory models built using a number of different connection strategies, applied to one- and two-dimensional topologies. Efficient patterns of connectivity are identified which yield high performance at relatively low wiring costs in both topologies. Networks with displaced connectivity are seen to perform particularly well. It is found that two-dimensional models are more tolerant of variations in connection strategy than their one-dimensional counterparts; though networks built with both topologies become less so as their connection density is decreased.
Calcraft
Lee
L
School of Computer Science, University of Hertfordshire, College Lane, Hatfield, Hertfordshire, United Kingdom. l.calcraft@herts.ac.uk
Adams
Rod
R
Davey
Neil
N
D001245:Association Learning; D003198:Computer Simulation; D008568:Memory; D016571:Neural Networks, Computer