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Learning Spatio-Temporal Behavioural Sequences

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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))

Abstract

Living beings are able to adapt their behaviour repertoire to environmental constraints. Among the capabilities needed for such improvement, the ability to store and retrieve temporal sequences is of particular importance. This chapter focuses on the description of an architecture based on spiking neurons, able to learn and autonomously generate a sequence of generic objects or events. The neural architecture is inspired by the insect mushroom bodies already taken into account in the previous chapters as a crucial centre for multimodal sensory integration and behaviour modulation in insects. Sequence learning is only one among a variety of functionalities that coexist within the insect brain computational model. We will propose a series of implementations that can be adopted to obtain these objectives and report the simulation results obtained. We will embed these mechanisms also in roving robots thereby proposing forward-thinking experiments.

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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). Learning Spatio-Temporal Behavioural Sequences. 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_5

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  • DOI: https://doi.org/10.1007/978-3-319-73347-0_5

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