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2018 edition, Scopus 2019

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Front. Robot. AI | doi: 10.3389/frobt.2019.00062

Natural language processing in large-scale neural models for medical screenings

  • 1Klinik für Phoniatrie, Pädaudiologie und Kommunikationsstörungen, Faculty of Medicine, RWTH Aachen University, Germany
  • 2Applied Brain Research, Inc., Canada
  • 3Centre for Theoretical Neuroscience, Faculty of Arts, University of Waterloo, Canada

Many medical screenings used for the diagnosis of neurological, psychological or language and speech disorders access the language and speech processing system. Specifically, patients are asked to fulfill a task (perception) and then requested to give answers verbally or by writing (production). To analyze cognitive or higher-level linguistic impairments or disorders it is thus expected that specific parts of the language and speech processing system of patients are working correctly or that verbal instructions are replaced by pictures (avoiding auditory perception) or oral answers by pointing (avoiding speech articulation). The first goal of this paper is to propose a large-scale neural model which comprises cognitive and lexical levels of the human neural system, and which is able to simulate the human behavior occurring in medical screenings. The second goal of this paper is to relate (microscopic) neural deficits introduced into the model to corresponding (macroscopic) behavioral deficits resulting from the model simulations. The Neural Engineering Framework and the Semantic Pointer Architecture are used to develop the large-scale neural model. Parts of two medical screenings are simulated: (1) a screening of word naming for the detection of developmental problems in lexical storage and lexical retrieval; and (2) a screening of cognitive abilities for the detection of mild cognitive impairment and early dementia. Both screenings include cognitive, language, and speech processing, and for both screenings the same model is simulated with and without neural deficits (physiological case vs. pathological case). While the simulation of both screenings results in the expected normal behavior in the physiological case the simulations clearly show a deviation of behavior, e.g. an increase in errors in the pathological case. Moreover, specific types of neural dysfunctions resulting from different types of neural defects lead to differences in the type and strength of the observed behavioral deficits.

Keywords: neurocomputational model, spiking neural networks, detailed computer simulations of natural language processes, Behavioral testing, brain-behavior connection, medical screenings

Received: 10 Dec 2018; Accepted: 09 Jul 2019.

Edited by:

Giovanni L. Masala, Manchester Metropolitan University, United Kingdom

Reviewed by:

James C. Knight, University of Sussex, United Kingdom
Nicola Vanello, Department of Information Engineering, University of Pisa, Italy  

Copyright: © 2019 Stille, Bekolay, Blouw and Kröger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mrs. Catharina M. Stille, Faculty of Medicine, RWTH Aachen University, Klinik für Phoniatrie, Pädaudiologie und Kommunikationsstörungen, Aachen, Germany, Catharina.Stille@rwth-aachen.de