Browse by author
Lookup NU author(s): Dr Hermann Moisl
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
This paper addresses a key question in the volume in which it appears by applying the chaotic dynamics found in biological brains to design of a strictly sequential artificial neural network based natural language understanding (NLU) system. The discussion is in three parts. The first part argues 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 determines string semantics-- are unnecessary, and that it is sufficient to process 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 processing, 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 a sequential NLU system.
Author(s): Moisl HL
Editor(s): Wermter, S; Austin, J; Willshaw, D
Publication type: Book Chapter
Publication status: Published
Book Title: Emergent Neural Computational Architectures Based on Neuroscience: Towards Neuroscience-Inspired Computing
Year: 2001
Volume: 2036
Pages: 442-460
Print publication date: 01/01/2001
Series Title: Lecture Notes in Computer Science
Publisher: Springer
Place Published: Berlin; London
Library holdings: Search Newcastle University Library for this item
ISBN: 9783540423638
