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

Autores
Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George K.; Pérez Cesaretti, Mauricio David; Wainselboim, Alejandro Javier; Zanutto, Bonifacio Silvano
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Dematties, Dario Jesus. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Rizzi, Silvio. Argonne National Laboratory; Estados Unidos
Fil: Thiruvathukal, George K.. University of Chicago; Estados Unidos. Argonne National Laboratory; Estados Unidos
Fil: Pérez Cesaretti, Mauricio David. Uppsala Universitet.; Suecia
Fil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Materia
CORTICAL DYNAMICS
UNSUPERVISED LEARNING
BRAIN-INSPIRED ARTIFICIAL NEURAL NETWORKS
GRAMMAR EMERGENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/107553
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A Computational Theory for the Emergence of Grammatical Categories in Cortical DynamicsDematties, Dario JesusRizzi, SilvioThiruvathukal, George K.Pérez Cesaretti, Mauricio DavidWainselboim, Alejandro JavierZanutto, Bonifacio SilvanoCORTICAL DYNAMICSUNSUPERVISED LEARNINGBRAIN-INSPIRED ARTIFICIAL NEURAL NETWORKSGRAMMAR EMERGENCEA 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.Fil: Dematties, Dario Jesus. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFil: Rizzi, Silvio. Argonne National Laboratory; Estados UnidosFil: Thiruvathukal, George K.. University of Chicago; Estados Unidos. Argonne National Laboratory; Estados UnidosFil: Pérez Cesaretti, Mauricio David. Uppsala Universitet.; SueciaFil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; ArgentinaFrontiers Research Foundation2020-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/vnd.openxmlformats-officedocument.wordprocessingml.documentapplication/pdfhttp://hdl.handle.net/11336/107553Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George K.; Pérez Cesaretti, Mauricio David; Wainselboim, Alejandro Javier; et al.; A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics; Frontiers Research Foundation; Frontiers in Neural Circuits; 14; 4-2020; 1-691662-5110CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fncir.2020.00012info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fncir.2020.00012/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:08:43Zoai:ri.conicet.gov.ar:11336/107553instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:08:43.846CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
title A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
spellingShingle A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
Dematties, Dario Jesus
CORTICAL DYNAMICS
UNSUPERVISED LEARNING
BRAIN-INSPIRED ARTIFICIAL NEURAL NETWORKS
GRAMMAR EMERGENCE
title_short 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_sort A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
dc.creator.none.fl_str_mv Dematties, Dario Jesus
Rizzi, Silvio
Thiruvathukal, George K.
Pérez Cesaretti, Mauricio David
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author Dematties, Dario Jesus
author_facet Dematties, Dario Jesus
Rizzi, Silvio
Thiruvathukal, George K.
Pérez Cesaretti, Mauricio David
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author_role author
author2 Rizzi, Silvio
Thiruvathukal, George K.
Pérez Cesaretti, Mauricio David
Wainselboim, Alejandro Javier
Zanutto, Bonifacio Silvano
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CORTICAL DYNAMICS
UNSUPERVISED LEARNING
BRAIN-INSPIRED ARTIFICIAL NEURAL NETWORKS
GRAMMAR EMERGENCE
topic CORTICAL DYNAMICS
UNSUPERVISED LEARNING
BRAIN-INSPIRED ARTIFICIAL NEURAL NETWORKS
GRAMMAR EMERGENCE
dc.description.none.fl_txt_mv 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.
Fil: Dematties, Dario Jesus. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Fil: Rizzi, Silvio. Argonne National Laboratory; Estados Unidos
Fil: Thiruvathukal, George K.. University of Chicago; Estados Unidos. Argonne National Laboratory; Estados Unidos
Fil: Pérez Cesaretti, Mauricio David. Uppsala Universitet.; Suecia
Fil: Wainselboim, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Ciencias Humanas, Sociales y Ambientales; Argentina
Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
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.
publishDate 2020
dc.date.none.fl_str_mv 2020-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/107553
Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George K.; Pérez Cesaretti, Mauricio David; Wainselboim, Alejandro Javier; et al.; A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics; Frontiers Research Foundation; Frontiers in Neural Circuits; 14; 4-2020; 1-69
1662-5110
CONICET Digital
CONICET
url http://hdl.handle.net/11336/107553
identifier_str_mv Dematties, Dario Jesus; Rizzi, Silvio; Thiruvathukal, George K.; Pérez Cesaretti, Mauricio David; Wainselboim, Alejandro Javier; et al.; A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics; Frontiers Research Foundation; Frontiers in Neural Circuits; 14; 4-2020; 1-69
1662-5110
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3389/fncir.2020.00012
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/article/10.3389/fncir.2020.00012/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/pdf
dc.publisher.none.fl_str_mv Frontiers Research Foundation
publisher.none.fl_str_mv Frontiers Research Foundation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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