Sparsely-connected associative memory models with displaced connectivity

Abstract. Our work is concerned with finding optimum connection strategies in highperformance associative memory models. Taking inspiration from axonal branching in biological neurons, we impose a displacement of the point of efferent arborisation, so that the output from each node travels a certain distance before branching to connect to other units. This technique is applied to networks constructed with a connectivity profile based on Gaussian distributions, and the results compared to those obtained with a network containing purely local connections, displaced in the same manner. It is found that displacement of the point of arborisation has a very beneficial effect on the performance of both network types, with the displaced locally-connected network performing the best.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Lee Calcraft, Rod Adams, Neil Davey
• Keywords: Axonal Branching, Displaced Locally-connected Network, ESANN 2007, Neural Networks, Optimum Connection Strategies