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Conclusion

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

Abstract

This book provides an overview of recruitment learning, a neural network learning system which exhibits high biological plausibility due to a number of core features - notably sparse connectivity levels and the use of weight updates which both respect synaptic constraints and support rapid concept acquisition. The earlier sections of this volume have given form to these ideas, for both the discrete time systems characteristic of the early wave of connectionism, and the more recent continuous time spiking neural systems - which allow closer alignment with neural observation. In this concluding discussion, it is worthwhile to summarise these ideas and to identify opportunities which remain unexplored, most notably those which arise through the emergence of adult neurogenesis.

Keywords

  • Adult Neurogenesis
  • Sparse Code
  • Data Mining Application
  • Neural Network Simulation
  • High Dimensional Input

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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© 2010 Springer-Verlag Berlin Heidelberg

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Diederich, J., Günay, C., Hogan, J.M. (2010). Conclusion. In: Recruitment Learning. Studies in Computational Intelligence, vol 303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14028-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-14028-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14027-3

  • Online ISBN: 978-3-642-14028-0

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