A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
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 potentiall...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Neural Circuits |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncir.2020.00012/full |
| _version_ | 1819093361058906112 |
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| author | Dario Dematties Silvio Rizzi George K. Thiruvathukal George K. Thiruvathukal Mauricio David Pérez Alejandro Wainselboim B. Silvano Zanutto B. Silvano Zanutto |
| author_facet | Dario Dematties Silvio Rizzi George K. Thiruvathukal George K. Thiruvathukal Mauricio David Pérez Alejandro Wainselboim B. Silvano Zanutto B. Silvano Zanutto |
| author_sort | Dario Dematties |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T23:10:17Z |
| format | Article |
| id | doaj.art-5d2e169c4b554d2ebe5963acb77ac103 |
| institution | Directory Open Access Journal |
| issn | 1662-5110 |
| language | English |
| last_indexed | 2024-12-21T23:10:17Z |
| publishDate | 2020-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neural Circuits |
| spelling | doaj.art-5d2e169c4b554d2ebe5963acb77ac1032022-12-21T18:47:04ZengFrontiers Media S.A.Frontiers in Neural Circuits1662-51102020-04-011410.3389/fncir.2020.00012517861A Computational Theory for the Emergence of Grammatical Categories in Cortical DynamicsDario Dematties0Silvio Rizzi1George K. Thiruvathukal2George K. Thiruvathukal3Mauricio David Pérez4Alejandro Wainselboim5B. Silvano Zanutto6B. Silvano Zanutto7Universidad de Buenos Aires, Facultad de Ingeniería, Instituto de Ingeniería Biomédica, Buenos Aires, ArgentinaArgonne National Laboratory, Lemont, IL, United StatesArgonne National Laboratory, Lemont, IL, United StatesComputer Science Department, Loyola University Chicago, Chicago, IL, United StatesMicrowaves in Medical Engineering Group, Division of Solid-State Electronics, Department of Electrical Engineering, Uppsala University, Uppsala, SwedenCentro Científico Tecnológico Conicet Mendoza, Instituto de Ciencias Humanas, Sociales y Ambientales, Mendoza, ArgentinaUniversidad de Buenos Aires, Facultad de Ingeniería, Instituto de Ingeniería Biomédica, Buenos Aires, ArgentinaInstituto de Biología y Medicina Experimental-CONICET, Buenos Aires, ArgentinaA 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.https://www.frontiersin.org/article/10.3389/fncir.2020.00012/fullcortical dynamicsgrammar emergencebrain-inspired artificial neural networksunsupervised learningcomputational linguisticsonline sentence processing |
| spellingShingle | Dario Dematties Silvio Rizzi George K. Thiruvathukal George K. Thiruvathukal Mauricio David Pérez Alejandro Wainselboim B. Silvano Zanutto B. Silvano Zanutto A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics Frontiers in Neural Circuits cortical dynamics grammar emergence brain-inspired artificial neural networks unsupervised learning computational linguistics online sentence processing |
| title | A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics |
| title_full | A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics |
| title_fullStr | A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics |
| title_full_unstemmed | A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics |
| title_short | A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics |
| title_sort | computational theory for the emergence of grammatical categories in cortical dynamics |
| topic | cortical dynamics grammar emergence brain-inspired artificial neural networks unsupervised learning computational linguistics online sentence processing |
| url | https://www.frontiersin.org/article/10.3389/fncir.2020.00012/full |
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