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Emergent Neural Computational Architectures Based on Neuroscience pp 442–460Cite as

Linguistic Computation with State Space Trajectories

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

Abstract

This paper addresses the key question of this book by apply- ing the chaotic dynamics found in biological brains to design of a strictly sequential artificial neural network-based natural language understand- ing (NLU) system. The discussion is in three parts. The first part ar- gues that, for NLU, two foundational principles of generative linguistics, mainstream cognitive science, and much of artificial intelligence -that natural language strings have complex syntactic structure processed by structure-sensitive algorithms, and that this syntactic structure deter- mines string semantics- are unnecessary, and that it is sufficient to pro- cess strings purely as symbol sequences. The second part then describes neuroscientific work which identifies chaotic attractor trajectory in state space as the fundamental principle of brain function at a level above that of the individual neuron, and which indicates that sensory process- ing, and perhaps higher cognition more generally, are implemented by cooperating attractor sequence processes. Finally, the third part sketches a possible application of this neuroscientific work to design of an a se- quential NLU system.

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Author information

Authors and Affiliations

  1. Centre for Research in Linguistics, University of Newcastle upon Tyne, Tyne

    Hermann Moisl

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  1. Hermann Moisl
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Editors and Affiliations

  1. Centre of Informatics, SCET, University of Sunderland, St PetersWay, Sunderland SR6 0DD, UK

    Stefan Wermter

  2. Department of Computer Science, University of York, York, Y010 5DD, UK

    Jim Austin

  3. Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, UK

    David Willshaw

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Moisl, H. (2001). Linguistic Computation with State Space Trajectories. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_32

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  • DOI: https://doi.org/10.1007/3-540-44597-8_32

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  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

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