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Original Research ARTICLE

Front. Neural Circuits | doi: 10.3389/fncir.2020.00012

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics Provisionally accepted The final, formatted version of the article will be published soon. Notify me

  • 1Faculty of Engineering, University of Buenos Aires, Argentina
  • 2Argonne National Laboratory (DOE), United States
  • 3Loyola University Chicago, United States
  • 4Uppsala University, Sweden
  • 5CONICET Mendoza, Argentina
  • 6Instituto de Biología y Medicina Experimental (IBYME), Argentina

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely
and very quickly during online sentence processing. Although several theories have advanced
arguments regarding the neurocomputational bases of this phenomenon, we argue that these
theories could potentially benefit by including neurophysiological data concerning cortical
dynamics constraints in brain tissue. In addition, some theories promote the integration of
complex optimization methods in neural tissue. In this paper we attempt to fill these gaps
introducing a computational model inspired in the dynamics of cortical tissue. In our modelling
approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic
branches–on the other hand–contribute independently to somatic depolarization by means of
dendritic spikes, and finally, prediction failures produce massive firing events preventing formation
of sparse distributed representations. The model presented in this paper combines semantic
and coarse-grained syntactic constraints for each word in a sentence context until grammatically
related word function discrimination emerges spontaneously by the sole correlation of lexical
information from different sources without applying complex optimization methods. By means
of support vector machine techniques, we show that the sparse activation features returned
by our approach are well suited–bootstrapping from the features returned by Word Embedding
mechanisms–to accomplish grammatical function classification of individual words in a sentence.
In this way we develop a biologically guided computational explanation for linguistically relevant
unification processes in cortex which connects psycholinguistics to neurobiological accounts of
language. We also claim that the computational hypotheses established in this research could
foster future work on biologically-inspired learning algorithms for natural language processing
applications.

Keywords: cortical dynamics, Grammar Emergence, Brain-Inspired Artificial Neural Networks, unsupervised learning, computational linguistics, Online sentence processing

Received: 05 Dec 2019; Accepted: 16 Mar 2020.

Copyright: © 2020 Dematties, Rizzi, Thiruvathukal, Perez, Wainselboim and Zanutto. 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: Mr. Dario Dematties, Faculty of Engineering, University of Buenos Aires, Buenos Aires, Argentina, djdematties@gmail.com