000864173 001__ 864173
000864173 005__ 20240313103134.0
000864173 0247_ $$2doi$$a10.3934/mbe.2019351
000864173 0247_ $$2Handle$$a2128/23040
000864173 0247_ $$2WOS$$aWOS:000487331700043
000864173 0247_ $$2pmid$$apmid:31698600
000864173 037__ $$aFZJ-2019-04039
000864173 041__ $$aEnglish
000864173 082__ $$a510
000864173 1001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b0$$eCorresponding author$$ufzj
000864173 1112_ $$aNeural Coding 2018$$cTurin$$d2018-09-09 - 2018-09-14$$wItaly
000864173 245__ $$aDetection and evaluation of bursts in terms of novelty and surprise
000864173 260__ $$aSpringfield, Mo.$$bInst.$$c2019
000864173 3367_ $$2DRIVER$$aarticle
000864173 3367_ $$2DataCite$$aOutput Types/Journal article
000864173 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1569913690_16323
000864173 3367_ $$2BibTeX$$aARTICLE
000864173 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000864173 3367_ $$00$$2EndNote$$aJournal Article
000864173 520__ $$aThe detection of bursts and also of response onsets is often of relevance in understanding neurophysiological data, but the detection of these events is not a trivial task. We build on a method that was originally designed for burst detection using the so-called burst surprise as a measure. We extend this method and provide a proper significance measure. Our method consists of two stages. In the first stage we model the neuron’s interspike interval (ISI) distribution and make an i.i.d. assumption to formulate our null hypothesis. In addition we define a set of ’surprising’ events that signify deviations from the null hypothesis in the direction of ’burstiness’. Here the so-called (strict) burst novelty is used to measure the size of this deviation. In the second stage we determine the significance of this deviation. The (strict) burst surprise is used to measure the significance, since it is the negative logarithm of the significance probability. After showing the consequences of a non-proper null hypothesis on burst detection performance, we apply the method to experimental data. For this application the data are divided into a period for parameter estimation to express a proper null hypothesis (model of the ISI distribution), and the rest of the data is analyzed by using that null hypothesis. We find that assuming a Poisson process for experimental spike data from motor cortex is rarely a proper null hypothesis, because these data tend to fire more regularly and thus a gamma process is more appropriate. We show that our burst detection method can be used for rate change onset detection, because a deviation from the null hypothesis detected by (strict) burst novelty also covers an increase of firing rate.
000864173 536__ $$0G:(DE-HGF)POF2-89571$$a89571 - Connectivity and Activity (POF2-89571)$$cPOF2-89571$$fPOF II T$$x0
000864173 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000864173 536__ $$0G:(GEPRIS)238707842$$aDFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842)$$c238707842$$x2
000864173 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x3
000864173 536__ $$0G:(GEPRIS)238707842$$aDFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842)$$c238707842$$x4
000864173 7001_ $$0P:(DE-Juel1)169779$$aLucrezia, Emanuele$$b1
000864173 7001_ $$0P:(DE-Juel1)172768$$aPalm, Günther$$b2$$ufzj
000864173 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b3$$ufzj
000864173 770__ $$aNeural Coding 2018
000864173 773__ $$0PERI:(DE-600)2265126-3$$a10.3934/mbe.2019351$$n6$$p6990-7008$$tMathematical biosciences and engineering$$v16$$x1547-1063$$y2019
000864173 8564_ $$uhttps://juser.fz-juelich.de/record/864173/files/Invoice_APC_MBE2019223.pdf
000864173 8564_ $$uhttps://juser.fz-juelich.de/record/864173/files/Invoice_APC_MBE2019223.pdf?subformat=pdfa$$xpdfa
000864173 8564_ $$uhttps://juser.fz-juelich.de/record/864173/files/mbe-16-06-351.pdf$$yOpenAccess
000864173 8564_ $$uhttps://juser.fz-juelich.de/record/864173/files/mbe-16-06-351.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000864173 8767_ $$92019-07-18$$d2019-07-24$$eAPC$$jZahlung erfolgt$$pMBE2019223$$z800 USD, FZJ-2019-03985
000864173 909CO $$ooai:juser.fz-juelich.de:864173$$pdnbdelivery$$popenCost$$pec_fundedresources$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000864173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144576$$aForschungszentrum Jülich$$b0$$kFZJ
000864173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172768$$aForschungszentrum Jülich$$b2$$kFZJ
000864173 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b3$$kFZJ
000864173 9131_ $$0G:(DE-HGF)POF2-89571$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vConnectivity and Activity$$x0
000864173 9141_ $$y2019
000864173 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000864173 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000864173 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bMATH BIOSCI ENG : 2017
000864173 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000864173 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000864173 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000864173 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000864173 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000864173 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000864173 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000864173 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x0
000864173 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
000864173 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
000864173 9801_ $$aAPC
000864173 9801_ $$aFullTexts
000864173 980__ $$ajournal
000864173 980__ $$aVDB
000864173 980__ $$aUNRESTRICTED
000864173 980__ $$aI:(DE-Juel1)INM-10-20170113
000864173 980__ $$aI:(DE-Juel1)INM-6-20090406
000864173 980__ $$aI:(DE-Juel1)IAS-6-20130828
000864173 980__ $$aAPC
000864173 981__ $$aI:(DE-Juel1)IAS-6-20130828