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Non-linear Neuro-inspired Circuits and Systems: Processing and Learning Issues

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Nonlinear Circuits and Systems for Neuro-inspired Robot Control

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSNONLINCIRC))

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Abstract

In this chapter the main elements useful for the design and realization of the neural architectures reported in the following chapters will be presented. Considering spiking and non-spiking neurons, the models used for implementing each of them, the synaptic models, the basic learning and plasticity algorithms and the network architectures will be introduced and analysed. The key elements that led to their selection and application in the developed neuro-inspired systems will be discussed briefly.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica Elettronica e dei Sistemi, University of Catania, Catania, Italy

    Luca Patanè & Paolo Arena

  2. Institut für Entwicklungsbiologie und Neurobiologie, Johannes Gutenberg Universität Mainz, Mainz, Germany

    Roland Strauss

Authors
  1. Luca Patanè
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  2. Roland Strauss
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  3. Paolo Arena
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Correspondence to Paolo Arena .

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Patanè, L., Strauss, R., Arena, P. (2018). Non-linear Neuro-inspired Circuits and Systems: Processing and Learning Issues. In: Nonlinear Circuits and Systems for Neuro-inspired Robot Control. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-73347-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-73347-0_2

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