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The Stability of Recruited Concepts

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Part of the Studies in Computational Intelligence book series (SCI,volume 303)

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).

Keywords

  • Lateral Inhibition
  • Inhibitory Feedback
  • Excitatory Synapse
  • Spike Rate
  • Inhibitory Synapse

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Diederich, J., Günay, C., Hogan, J.M. (2010). The Stability of Recruited Concepts. In: Recruitment Learning. Studies in Computational Intelligence, vol 303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14028-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-14028-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14027-3

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