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Paradigms for Computing with Spiking Neurons

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Models of Neural Networks IV

Part of the book series: Physics of Neural Networks ((NEURAL NETWORKS))

Abstract

In this chapter we define for various neural coding schemes formal models of computation in networks of spiking neurons. The main results about the computational power of these models are surveyed. In particular, we compare their computational power with that of common models for artificial neural networks. Some rigorous theoretical results are presented which show that for temporal coding of inputs and outputs certain functions can be computed in a feedforward network of spiking neurons with fewer neurons than in any multi-layer perceptron (i.e., feedforward network of sigmoidal neurons). This chapter also presents a brief survey of the literature on computations in networks of spiking neurons.

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Authors
  1. Wolfgang Maass
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Editors and Affiliations

  1. Institut für Theoretische Physik, Technische Universität München, D-85747, Garching bei München, Germany

    J. Leo van Hemmen

  2. Department of Mathematics, University of Chicago, 60637, Chicago, IL, USA

    Jack D. Cowan

  3. Department of Electronics, Weizmann Institute of Science, 76100, Rehovot, Israel

    Eytan Domany

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© 2002 Springer Science+Business Media New York

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Maass, W. (2002). Paradigms for Computing with Spiking Neurons. In: van Hemmen, J.L., Cowan, J.D., Domany, E. (eds) Models of Neural Networks IV. Physics of Neural Networks. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21703-1_9

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  • DOI: https://doi.org/10.1007/978-0-387-21703-1_9

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2875-7

  • Online ISBN: 978-0-387-21703-1

  • eBook Packages: Springer Book Archive

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