Corticostriatal response selection in sentence production: Insights from neural network simulation with reservoir computing
Brain and Language. 2015-11-01; 150: 54-68
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Hinaut X(1), Lance F(2), Droin C(2), Petit M(2), Pointeau G(2), Dominey PF(3).
(1)CNPS, UMR CNRS 8195, University Paris-Sud, Orsay, France.
(2)INSERM Stem Cell and Brain Research Institute, Human and Robot Cognitive Systems, 18 Ave Lepine, 69675 Bron Cedex, France.
(3)INSERM Stem Cell and Brain Research Institute, Human and Robot Cognitive Systems, 18 Ave Lepine, 69675 Bron Cedex, France. Electronic address:
Language production requires selection of the appropriate sentence structure to
accommodate the communication goal of the speaker – the transmission of a particular meaning. Here we consider event meanings, in terms of predicates and thematic roles, and we address the problem that a given event can be described from multiple perspectives, which poses a problem of response selection. We present a model of response selection in sentence production that is inspired by the primate corticostriatal system. The model is implemented in the context of reservoir computing where the reservoir – a recurrent neural network with fixed connections – corresponds to cortex, and the readout corresponds to the striatum. We demonstrate robust learning, and generalization properties of the model, and demonstrate its cross linguistic capabilities in English and Japanese. The
results contribute to the argument that the corticostriatal system plays a role in response selection in language production, and to the stance that reservoir computing is a valid potential model of corticostriatal processing.
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