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A Spiking Neural Network Model of Multi-modal Language Processing of Robot Instructions

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Biomimetic Neural Learning for Intelligent Robots

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3575))

Abstract

Presented is a spiking neural network architecture of human language instruction recognition and robot control. The network is based on a model of a leaky Integrate-And-Fire (lIAF) spiking neurone with Active Dendrites and Dynamic Synapses (ADDS) [1,2,3]. The architecture contains several main modules associating information across different modalities: an auditory system recognising single spoken words, a visual system recognising objects of different colour and shape, motor control system for navigation and motor control and a working memory. The main focus of this presentation is the working memory module whose function is sequential processing of word from a language instruction, task and goal representation and cross-modal association of objects and actions. We test the model with a robot whose goal is to recognise and execute language instructions. The work demonstrates the potential of spiking neurons for processing spatio-temporal patterns and the experiments present spiking neural networks as a paradigm which can be applied for modelling sequence detectors at word level for robot instructions.

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Authors and Affiliations

  1. School of Computing and Technology, University of Sunderland, St. Peter’s Campus, Sunderland, SR6 0DD, United Kingdom

    Christo Panchev

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  1. Christo Panchev
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  1. University of Sunderland, SR6 0DD, Sunderland, United Kingdom

    Stefan Wermter

  2. Institute of Neural Information Processing, University of Ulm, D-89069, Ulm, Germany

    Günther Palm

  3. Hybrid Intelligent Systems, SCAT, University of Sunderland, UK

    Mark Elshaw

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Panchev, C. (2005). A Spiking Neural Network Model of Multi-modal Language Processing of Robot Instructions. In: Wermter, S., Palm, G., Elshaw, M. (eds) Biomimetic Neural Learning for Intelligent Robots. Lecture Notes in Computer Science(), vol 3575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11521082_11

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  • DOI: https://doi.org/10.1007/11521082_11

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