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Spiking Neural Networks and Temporal Binding

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

Introduction

Previous chapters of this book gave an overview of connectionist recruitment learning or the neuroidal network (Feldman, 1982, 1990; Valiant, 1988, 1994, 1998, 2000b, 2005) as a discrete-time algorithm. By extending recruitment into continuous-time to make it closer to the neuronal networks of the brain, this chapter puts it in the context of spiking neural networks (Hebb, 1949; Von der Malsburg, 1981, 1994; Abeles, 1991; Gerstner, 2001) and shows its similarity to temporal binding (Von der Malsburg, 1981; Singer and Gray, 1995).

The chapter is organized as follows. First, classical neural network research is reviewed to describe the theory of spiking neural network models. Then, the development of concept representation in neural networks and the binding problem is reviewed to present the relationship between recruitment learning and the temporal binding hypothesis. A historical account of these developments is also given.

Keywords

  • Combinatorial Explosion
  • Classical Neural Network
  • Variable Binding
  • Spike Neural Network
  • Temporal Binding

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). Spiking Neural Networks and Temporal Binding. In: Recruitment Learning. Studies in Computational Intelligence, vol 303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14028-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-14028-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14027-3

  • Online ISBN: 978-3-642-14028-0

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