000866432 001__ 866432
000866432 005__ 20240313103111.0
000866432 0247_ $$2doi$$a10.3389/fncom.2019.00079
000866432 0247_ $$2Handle$$a2128/24196
000866432 0247_ $$2altmetric$$aaltmetric:72299320
000866432 0247_ $$2pmid$$apmid:31920605
000866432 0247_ $$2WOS$$aWOS:000503494900001
000866432 037__ $$aFZJ-2019-05574
000866432 082__ $$a610
000866432 1001_ $$0P:(DE-Juel1)171197$$aZajzon, Barna$$b0$$eCorresponding author
000866432 245__ $$aPassing the message: representation transfer in modular balanced networks
000866432 260__ $$aLausanne$$bFrontiers Research Foundation$$c2019
000866432 3367_ $$2DRIVER$$aarticle
000866432 3367_ $$2DataCite$$aOutput Types/Journal article
000866432 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1613576059_16659
000866432 3367_ $$2BibTeX$$aARTICLE
000866432 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000866432 3367_ $$00$$2EndNote$$aJournal Article
000866432 520__ $$aNeurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.
000866432 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000866432 536__ $$0G:(EU-Grant)241498$$aEUROSPIN - European Consortium on Synaptic Protein Networks in Neurological and Psychiatric Diseases (241498)$$c241498$$fFP7-HEALTH-2009-single-stage$$x1
000866432 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x2
000866432 536__ $$0G:(DE-Juel1)jinm60_20190501$$aFunctional Neural Architectures (jinm60_20190501)$$cjinm60_20190501$$fFunctional Neural Architectures$$x3
000866432 588__ $$aDataset connected to CrossRef
000866432 7001_ $$0P:(DE-Juel1)166250$$aMahmoudian, Sepehr$$b1
000866432 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b2
000866432 7001_ $$0P:(DE-Juel1)165640$$aDuarte, Renato$$b3
000866432 773__ $$0PERI:(DE-600)2452964-3$$a10.3389/fncom.2019.00079$$gVol. 13, p. 79$$p79$$tFrontiers in computational neuroscience$$v13$$x1662-5188$$y2019
000866432 8564_ $$uhttps://juser.fz-juelich.de/record/866432/files/2019-0207098-3.pdf
000866432 8564_ $$uhttps://juser.fz-juelich.de/record/866432/files/2019-0207098-3.pdf?subformat=pdfa$$xpdfa
000866432 8564_ $$uhttps://juser.fz-juelich.de/record/866432/files/fncom-13-00079.pdf$$yOpenAccess
000866432 8564_ $$uhttps://juser.fz-juelich.de/record/866432/files/fncom-13-00079.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000866432 8767_ $$82019-0207098-2$$92019-10-29$$d2019-11-14$$eAPC$$jDeposit$$lDeposit: Frontiers$$p492418$$z2507.50 USD
000866432 909CO $$ooai:juser.fz-juelich.de:866432$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000866432 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171197$$aForschungszentrum Jülich$$b0$$kFZJ
000866432 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich$$b2$$kFZJ
000866432 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165640$$aForschungszentrum Jülich$$b3$$kFZJ
000866432 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000866432 9132_ $$0G:(DE-HGF)POF4-899$$1G:(DE-HGF)POF4-890$$2G:(DE-HGF)POF4-800$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000866432 9141_ $$y2019
000866432 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000866432 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000866432 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000866432 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bFRONT COMPUT NEUROSC : 2017
000866432 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal
000866432 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ
000866432 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000866432 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000866432 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000866432 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000866432 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review
000866432 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000866432 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central
000866432 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000866432 920__ $$lyes
000866432 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000866432 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000866432 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x2
000866432 9801_ $$aAPC
000866432 9801_ $$aFullTexts
000866432 980__ $$ajournal
000866432 980__ $$aVDB
000866432 980__ $$aI:(DE-Juel1)INM-6-20090406
000866432 980__ $$aI:(DE-Juel1)IAS-6-20130828
000866432 980__ $$aI:(DE-82)080012_20140620
000866432 980__ $$aAPC
000866432 980__ $$aUNRESTRICTED
000866432 981__ $$aI:(DE-Juel1)IAS-6-20130828