Rapid learning of complex sequences with time constraints: A dynamic neural field model
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/69418 |
Resumo: | Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This article presents a neurocomputational model based on the theoretical framework of dynamic neural fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimensions. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions. |
id |
RCAP_9c102cab03b3867f4ffd096014bf57bf |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/69418 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Rapid learning of complex sequences with time constraints: A dynamic neural field modelSequence learningInterval TimingDynamic fieold theoryRobioticsNeurocomputational modelHuman-robot interactionsAdaptation modelsColorComputational modelingRobotsSociologyStatisticsDynamic field theoryTimingEngenharia e Tecnologia::Outras Engenharias e TecnologiasScience & TechnologyMany of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This article presents a neurocomputational model based on the theoretical framework of dynamic neural fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimensions. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions.This work was supported in part by FCT (Portuguese Foundation for Science and Technology) through the Ph.D. Fellowship under Grant PD/BD/128183/2016; in part by the European Structural and Investment Funds in the FEDER Component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) and National Funds, through the FCT under Project PTDC/MAT-APL/31393/2017 (NEUROFIELD) and Project POCI-01-0247FEDER-039334; and in part by Research and Development Units Project Scope under Project UIDB/00319/2020 and Project UIDB/00013/2020.IEEEUniversidade do MinhoFerreira, Flora José RochaWojtak, WeronikaSousa, EmanuelLouro, LuisBicho, EstelaErlhagen, Wolfram20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/69418engFerreira, F., Wojtak, W., Sousa, E., Louro, L., Bicho, E., & Erlhagen, W. (2021, December). Rapid Learning of Complex Sequences With Time Constraints: A Dynamic Neural Field Model. IEEE Transactions on Cognitive and Developmental Systems. Institute of Electrical and Electronics Engineers (IEEE). http://doi.org/10.1109/tcds.2020.29917892379-89202379-893910.1109/TCDS.2020.2991789https://ieeexplore.ieee.org/abstract/document/9085956info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:33:14Zoai:repositorium.sdum.uminho.pt:1822/69418Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:28:44.157493Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
title |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
spellingShingle |
Rapid learning of complex sequences with time constraints: A dynamic neural field model Ferreira, Flora José Rocha Sequence learning Interval Timing Dynamic fieold theory Robiotics Neurocomputational model Human-robot interactions Adaptation models Color Computational modeling Robots Sociology Statistics Dynamic field theory Timing Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
title_short |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
title_full |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
title_fullStr |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
title_full_unstemmed |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
title_sort |
Rapid learning of complex sequences with time constraints: A dynamic neural field model |
author |
Ferreira, Flora José Rocha |
author_facet |
Ferreira, Flora José Rocha Wojtak, Weronika Sousa, Emanuel Louro, Luis Bicho, Estela Erlhagen, Wolfram |
author_role |
author |
author2 |
Wojtak, Weronika Sousa, Emanuel Louro, Luis Bicho, Estela Erlhagen, Wolfram |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ferreira, Flora José Rocha Wojtak, Weronika Sousa, Emanuel Louro, Luis Bicho, Estela Erlhagen, Wolfram |
dc.subject.por.fl_str_mv |
Sequence learning Interval Timing Dynamic fieold theory Robiotics Neurocomputational model Human-robot interactions Adaptation models Color Computational modeling Robots Sociology Statistics Dynamic field theory Timing Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
topic |
Sequence learning Interval Timing Dynamic fieold theory Robiotics Neurocomputational model Human-robot interactions Adaptation models Color Computational modeling Robots Sociology Statistics Dynamic field theory Timing Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
description |
Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This article presents a neurocomputational model based on the theoretical framework of dynamic neural fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimensions. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2021-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/69418 |
url |
https://hdl.handle.net/1822/69418 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ferreira, F., Wojtak, W., Sousa, E., Louro, L., Bicho, E., & Erlhagen, W. (2021, December). Rapid Learning of Complex Sequences With Time Constraints: A Dynamic Neural Field Model. IEEE Transactions on Cognitive and Developmental Systems. Institute of Electrical and Electronics Engineers (IEEE). http://doi.org/10.1109/tcds.2020.2991789 2379-8920 2379-8939 10.1109/TCDS.2020.2991789 https://ieeexplore.ieee.org/abstract/document/9085956 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799132784361472000 |