Neurocomputational Models of Natural Language

  • Alistair Knott


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.


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.







prefrontal cortex


recursive auto-associative memory


self-organizing map


simple recurrent network


superior temporal sulcus


transcranial magnetic stimulation


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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