The Stability of Recruited Concepts

Introduction

Recruitment learning is prone to instability when a chain of concepts is recruited in cascade as seen in Figure 8.1. The statistical variance inherent in the recruitment method causes increasing perturbations to the recruited set size, and thus instability (Valiant, 1994). We previously proposed a boost-and-limit algorithm to improve recruitment stability, and verified the applicability of this method with a software model in a spiking neuroidal net simulator (Günay and Maida, 2001, 2006b), which was overviewed in Chapter 7. In that model, excess recruitment candidates were rejected to enforce a stable recruitment level. This chapter describes the improvements of the previous boost-and-limit method to use a more biologically realistic neural mechanism (Günay and Maida, 2006a).