A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

Dematties, Dario; Rizzi, Silvio; Thiruvathukal, George K.; Pérez, Mauricio David; Wainselboim, Alejandro; Zanutto, B. Silvano

doi:10.3389/fncir.2020.00012

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

Journal Article · Thu Apr 16 00:00:00 EDT 2020 · Frontiers in Neural Circuits

DOI:https://doi.org/10.3389/fncir.2020.00012· OSTI ID:1817166

Dematties, Dario ^[1]; Rizzi, Silvio ^[2]; Thiruvathukal, George K. ^[3]; Pérez, Mauricio David ^[4]; Wainselboim, Alejandro ^[5]; Zanutto, B. Silvano ^[6]

Univ. of Buenos Aires (Argentina); OSTI
Argonne National Lab. (ANL), Lemont, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States); Loyola University, Chicago, IL (United States)
Uppsala Univ. (Sweden)
Centro Científico Tecnológico Conicet Mendoza (Argentina)
Instituto de Biología y Medicina Experimental (CONICET), Buenos Aires (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 modeling 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1817166

Journal Information:: Frontiers in Neural Circuits, Journal Name: Frontiers in Neural Circuits Vol. 14; ISSN 1662-5110

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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