Efficient connection strategies in 1D and 2D associative memory models with and without displaced connectivity

Abstract

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.

Clustering predicts memory performance in networks of spiking and non-spiking neurons

“…The patterns were stationary (no sequences) and so was largely the dynamics of the network (no oscillations or temporal patterns). Our study was conducted on a ring of neurons, but the results can be extended to 2-D networks (Calcraft et al, 2008). …”

“…In the present paper, building on our previous work (Davey and Adams, 2004; Calcraft et al, 2006, 2007, 2008; Davey et al, 2006; Chen et al, 2009), we use computer simulations to address three other biological constraints for sparsely connected networks. Firstly, how does the topology of the wiring pattern affect memory performance?…”