On setting unit thresholds in an incompletely connected associative net

Abstract

The associative net is a matrix model of associative memory which has an efficiency of recall approaching that of a random access memory with no associative capability. This is a fully connected network, which makes it possible to use a simple strategy for setting the thresholds of the units in recall. However, most brain structures that are thought to underlie learning and memory have only partial interconnectivity. We describe five different strategies for setting the thresholds of units in partially connected nets. The simplest is a mechanism of the winners-take-all type. The most sophisticated strategy employs information about the density of modified synapses and their distribution on each output unit. The action of this mechanism is shown to be equivalent to minimization of output error but without requiring numerical solution of a set of equations, which would be biologically implausible. Simulation results demonstrate the superiority of this mechanism in a typical case. Parameter sensitivity analysis of this mechanism shows its robustness and that performance improves as connectivity becomes more complete.