{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101655"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101655","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Polychronization as a mechanism for language acquisition in spiking neural networks","abstract":"The capacity of an intelligent agent to process complex patterns in signals such as language rests heavily on the nature of the internal representation of the relevant information. Furthermore, the acquisition of internal representation is an inherently closed-loop process in which an intelligent agent enters into a conversation with its environment. The result is the construction of a necessarily generative model of language, where semantics are grounded in an agent's sensory-motor experience by way of an associative memory. This work explores the mechanisms underlying language acquisition by investigating the function and architecture of the neocortex, with the ultimate goal of understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic plasticity. What emerges are groups of neurons exhibiting time-locked patterns of spiking, reproducible spatio-temporal stamps consisting of the precisely timed activations of their constituent neurons. At a high level, these polychronous neural groups may be thought of as a form of temporal encoding of information within the network. We propose that this representation is well suited to language acquisition, as it naturally resembles the spatio-temporal patterns found in the speech signal, as well as other real-world signals. Toward this end, we develop a supervised method for training a spiking neural network to learn and recognize patterns that are relevant to language, such as those corresponding to phonetic primitives. We show that even for a simplified model, the mechanism of polychronization is capable of processing and representing such patterns, providing a basis for language acquisition in spiking neural networks.","abstract_html":"The capacity of an intelligent agent to process complex patterns in signals such as language rests heavily on the nature of the internal representation of the relevant information. Furthermore, the acquisition of internal representation is an inherently closed-loop process in which an intelligent agent enters into a conversation with its environment. The result is the construction of a necessarily generative model of language, where semantics are grounded in an agent's sensory-motor experience by way of an associative memory. This work explores the mechanisms underlying language acquisition by investigating the function and architecture of the neocortex, with the ultimate goal of understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic plasticity. What emerges are groups of neurons exhibiting time-locked patterns of spiking, reproducible spatio-temporal stamps consisting of the precisely timed activations of their constituent neurons. At a high level, these polychronous neural groups may be thought of as a form of temporal encoding of information within the network. We propose that this representation is well suited to language acquisition, as it naturally resembles the spatio-temporal patterns found in the speech signal, as well as other real-world signals. Toward this end, we develop a supervised method for training a spiking neural network to learn and recognize patterns that are relevant to language, such as those corresponding to phonetic primitives. We show that even for a simplified model, the mechanism of polychronization is capable of processing and representing such patterns, providing a basis for language acquisition in spiking neural networks.","abstract_has_math":false,"creators":["Wang, Felix Y"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Levinson, Stephen E.","Hasegawa-Johnson, Mark","Rothganger, Fred","Smaragdis, Paris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:30:13Z","date_published":"2018-09-27T16:30:13Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Language Acquisition","Spiking Neural Network","Neural Computing","Spiking Simulation","Polychronization","Self-Organization","Associative Memory","Pattern Recognition","Supervised Learning","Multi-modal Learning","Cybernetics"],"languages":["en"],"rights":["Copyright 2018 Felix Y. Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101655","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Levinson, Stephen E.","Hasegawa-Johnson, Mark","Rothganger, Fred","Smaragdis, Paris"]},{"key":"dc:creator","label":"Author","values":["Wang, Felix Y"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:30:13Z","2020-09-28T09:15:16Z","2018-06-21","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Language Acquisition","Spiking Neural Network","Neural Computing","Spiking Simulation","Polychronization","Self-Organization","Associative Memory","Pattern Recognition","Supervised Learning","Multi-modal Learning","Cybernetics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Felix Y. 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This work explores the mechanisms underlying language acquisition by investigating the function and architecture of the neocortex, with the ultimate goal of understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic plasticity. What emerges are groups of neurons exhibiting time-locked patterns of spiking, reproducible spatio-temporal stamps consisting of the precisely timed activations of their constituent neurons. At a high level, these polychronous neural groups may be thought of as a form of temporal encoding of information within the network. We propose that this representation is well suited to language acquisition, as it naturally resembles the spatio-temporal patterns found in the speech signal, as well as other real-world signals. Toward this end, we develop a supervised method for training a spiking neural network to learn and recognize patterns that are relevant to language, such as those corresponding to phonetic primitives. We show that even for a simplified model, the mechanism of polychronization is capable of processing and representing such patterns, providing a basis for language acquisition in spiking neural networks.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Felix Wang, accepted the attached license on 2018-06-20 at 14:53.","The student, Felix Wang, submitted this Dissertation for approval on 2018-06-20 at 15:09.","This Dissertation was approved for publication on 2018-06-21 at 09:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12652 on 2018-09-27 at 11:16:07","Made available in DSpace on 2018-09-27T16:30:13Z (GMT). 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Furthermore, the acquisition of internal representation is an inherently closed-loop process in which an intelligent agent enters into a conversation with its environment. The result is the construction of a necessarily generative model of language, where semantics are grounded in an agent's sensory-motor experience by way of an associative memory. This work explores the mechanisms underlying language acquisition by investigating the function and architecture of the neocortex, with the ultimate goal of understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic plasticity. What emerges are groups of neurons exhibiting time-locked patterns of spiking, reproducible spatio-temporal stamps consisting of the precisely timed activations of their constituent neurons. At a high level, these polychronous neural groups may be thought of as a form of temporal encoding of information within the network. We propose that this representation is well suited to language acquisition, as it naturally resembles the spatio-temporal patterns found in the speech signal, as well as other real-world signals. Toward this end, we develop a supervised method for training a spiking neural network to learn and recognize patterns that are relevant to language, such as those corresponding to phonetic primitives. We show that even for a simplified model, the mechanism of polychronization is capable of processing and representing such patterns, providing a basis for language acquisition in spiking neural networks.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Felix Wang, accepted the attached license on 2018-06-20 at 14:53.","The student, Felix Wang, submitted this Dissertation for approval on 2018-06-20 at 15:09.","This Dissertation was approved for publication on 2018-06-21 at 09:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12652 on 2018-09-27 at 11:16:07","Made available in DSpace on 2018-09-27T16:30:13Z (GMT). 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Wang"],"dc:subject":["Language Acquisition","Spiking Neural Network","Neural Computing","Spiking Simulation","Polychronization","Self-Organization","Associative Memory","Pattern Recognition","Supervised Learning","Multi-modal Learning","Cybernetics"],"dc:title":["Polychronization as a mechanism for language acquisition in spiking neural networks"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:40Z"}