Neurocomputational Models of Natural Language

Abstract

In this chapter I review computational models of the neural circuitry which implements the human capacity for language. These models cover many different aspects of the language capacity, from representations of phonology and word forms to representations of sentence meanings and syntactic structures. The computational models discussed are neural networks: structures of simple units which are densely interconnected and can be active in parallel. I review the computational properties of the networks introduced and also empirical evidence from different sources (neural imaging, behavioral experiments, patterns of impairment following brain dysfunction) which supports the models described.

Keywords

Word Form Word Meaning Phonological Representation Concrete Word Context Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

2-D

two-dimensional

EEG

electroencephalography

PFC

prefrontal cortex

RAAM

recursive auto-associative memory

SOM

self-organizing map

SRN

simple recurrent network

STS

superior temporal sulcus

TMS

transcranial magnetic stimulation

fMRI

functional magnetic resonance imaging

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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of OtagoDunedinNew Zealand

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