000256185 001__ 256185
000256185 005__ 20240313103112.0
000256185 0247_ $$2doi$$a10.1007/s10827-015-0565-5
000256185 0247_ $$2ISSN$$a0929-5313
000256185 0247_ $$2ISSN$$a1573-6873
000256185 0247_ $$2Handle$$a2128/9349
000256185 0247_ $$2WOS$$aWOS:000357488700006
000256185 0247_ $$2altmetric$$aaltmetric:3710506
000256185 0247_ $$2pmid$$apmid:26041729
000256185 037__ $$aFZJ-2015-06170
000256185 041__ $$aEnglish
000256185 082__ $$a610
000256185 1001_ $$0P:(DE-Juel1)151167$$aZaytsev, Yury$$b0$$eCorresponding author
000256185 245__ $$aReconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
000256185 260__ $$aDordrecht [u.a.]$$bSpringer Science + Business Media B.V$$c2015
000256185 3367_ $$2DRIVER$$aarticle
000256185 3367_ $$2DataCite$$aOutput Types/Journal article
000256185 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1563263073_1091
000256185 3367_ $$2BibTeX$$aARTICLE
000256185 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000256185 3367_ $$00$$2EndNote$$aJournal Article
000256185 520__ $$aDynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings.
000256185 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000256185 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$$x1
000256185 536__ $$0G:(DE-HGF)B1175.01.12$$aW2Morrison - W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (B1175.01.12)$$cB1175.01.12$$x2
000256185 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x3
000256185 588__ $$aDataset connected to CrossRef
000256185 7001_ $$0P:(DE-Juel1)151166$$aMorrison, Abigail$$b1$$ufzj
000256185 7001_ $$0P:(DE-HGF)0$$aDeger, Moritz$$b2
000256185 773__ $$0PERI:(DE-600)1473055-8$$a10.1007/s10827-015-0565-5$$gVol. 39, no. 1, p. 77 - 103$$n1$$p77 - 103$$tJournal of computational neuroscience$$v39$$x1573-6873$$y2015
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.pdf$$yOpenAccess
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.gif?subformat=icon$$xicon$$yOpenAccess
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.jpg?subformat=icon-180$$xicon-180$$yOpenAccess
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.jpg?subformat=icon-640$$xicon-640$$yOpenAccess
000256185 8564_ $$uhttps://juser.fz-juelich.de/record/256185/files/art%253A10.1007%252Fs10827-015-0565-5.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000256185 8767_ $$92015-06-08$$d2015-06-24$$eHybrid-OA$$jZahlung erfolgt
000256185 909CO $$ooai:juser.fz-juelich.de:256185$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000256185 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)151166$$aForschungszentrum Jülich GmbH$$b1$$kFZJ
000256185 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000256185 9141_ $$y2015
000256185 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000256185 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000256185 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000256185 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000256185 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ COMPUT NEUROSCI : 2014
000256185 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000256185 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000256185 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000256185 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000256185 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000256185 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000256185 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000256185 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000256185 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000256185 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000256185 9201_ $$0I:(DE-82)080012_20140620$$kJARA-HPC$$lJARA - HPC$$x2
000256185 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x3
000256185 9801_ $$aAPC
000256185 9801_ $$aFullTexts
000256185 980__ $$ajournal
000256185 980__ $$aVDB
000256185 980__ $$aI:(DE-Juel1)JSC-20090406
000256185 980__ $$aI:(DE-Juel1)IAS-6-20130828
000256185 980__ $$aI:(DE-82)080012_20140620
000256185 980__ $$aI:(DE-Juel1)INM-6-20090406
000256185 980__ $$aAPC
000256185 980__ $$aUNRESTRICTED
000256185 981__ $$aI:(DE-Juel1)IAS-6-20130828
000256185 981__ $$aI:(DE-Juel1)IAS-6-20130828
000256185 981__ $$aI:(DE-Juel1)INM-6-20090406