001008686 001__ 1008686
001008686 005__ 20240313103123.0
001008686 0247_ $$2doi$$a10.3389/fnint.2023.935177
001008686 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-02477
001008686 0247_ $$2pmid$$a37396571
001008686 0247_ $$2WOS$$aWOS:001018401100001
001008686 037__ $$aFZJ-2023-02477
001008686 082__ $$a610
001008686 1001_ $$0P:(DE-Juel1)171197$$aZajzon, Barna$$b0$$eCorresponding author
001008686 245__ $$aToward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning
001008686 260__ $$aLausanne$$bFrontiers Research Foundation$$c2023
001008686 3367_ $$2DRIVER$$aarticle
001008686 3367_ $$2DataCite$$aOutput Types/Journal article
001008686 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1689931058_23743
001008686 3367_ $$2BibTeX$$aARTICLE
001008686 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001008686 3367_ $$00$$2EndNote$$aJournal Article
001008686 520__ $$aTo acquire statistical regularities from the world, the brain must reliably process, and learn from, spatio-temporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable. Here we illustrate the importance of these aspects by providing a thorough investigation of a recently proposed sequence learning model. We re-implement the modular columnar architecture and reward-based learning rule in the open-source NEST simulator, and successfully replicate the main findings of the original study. Building on these, we perform an in-depth analysis of the model's robustness to parameter settings and underlying assumptions, highlighting its strengths and weaknesses. We demonstrate a limitation of the model consisting in the hard-wiring of the sequence order in the connectivity patterns, and suggest possible solutions. Finally, we show that the core functionality of the model is retained under more biologically-plausible constraints
001008686 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001008686 536__ $$0G:(DE-82)EXS-SF-neuroIC002$$aneuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002)$$cEXS-SF-neuroIC002$$x1
001008686 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x2
001008686 588__ $$aDataset connected to DataCite
001008686 7001_ $$0P:(DE-HGF)0$$aDuarte, Renato$$b1
001008686 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b2$$ufzj
001008686 773__ $$0PERI:(DE-600)2452962-X$$a10.3389/fnint.2023.935177$$gVol. 17, p. 935177$$p935177$$tFrontiers in integrative neuroscience$$v17$$x1662-5145$$y2023
001008686 8564_ $$uhttps://juser.fz-juelich.de/record/1008686/files/fnint-17-935177.pdf$$yOpenAccess
001008686 8767_ $$d2023-06-27$$eAPC$$jDeposit
001008686 909CO $$ooai:juser.fz-juelich.de:1008686$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
001008686 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171197$$aForschungszentrum Jülich$$b0$$kFZJ
001008686 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands$$b1
001008686 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b2$$kFZJ
001008686 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001008686 9141_ $$y2023
001008686 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001008686 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001008686 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-11
001008686 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001008686 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-05-11T10:28:29Z
001008686 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-05-11T10:28:29Z
001008686 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-11
001008686 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2022-11-11
001008686 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001008686 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2022-11-11
001008686 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT INTEGR NEUROSC : 2022$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2021-05-11T10:28:29Z
001008686 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-24
001008686 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-08-24
001008686 920__ $$lyes
001008686 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001008686 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001008686 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
001008686 9801_ $$aAPC
001008686 9801_ $$aFullTexts
001008686 980__ $$ajournal
001008686 980__ $$aVDB
001008686 980__ $$aUNRESTRICTED
001008686 980__ $$aI:(DE-Juel1)INM-6-20090406
001008686 980__ $$aI:(DE-Juel1)IAS-6-20130828
001008686 980__ $$aI:(DE-Juel1)INM-10-20170113
001008686 980__ $$aAPC
001008686 981__ $$aI:(DE-Juel1)IAS-6-20130828