Connection Strategies in Associative Memory Models with Spiking and Non-spiking Neurons

Abstract

The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks.

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

“…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?…”