000886075 001__ 886075
000886075 005__ 20240313103126.0
000886075 0247_ $$2doi$$a10.12751/NNCN.BC2020.0095
000886075 0247_ $$2Handle$$a2128/26349
000886075 037__ $$aFZJ-2020-04261
000886075 041__ $$aeng
000886075 1001_ $$0P:(DE-Juel1)176920$$aKleinjohann, Alexander$$b0$$eCorresponding author$$ufzj
000886075 1112_ $$aBernstein Conference 2020$$cBerlin$$d2020-09-29 - 2020-10-01$$wGermany
000886075 245__ $$aModeling Temporally Precise Spike Artefacts to Study Their Impact on Spike Correlation Analyses
000886075 260__ $$c2020
000886075 3367_ $$033$$2EndNote$$aConference Paper
000886075 3367_ $$2BibTeX$$aINPROCEEDINGS
000886075 3367_ $$2DRIVER$$aconferenceObject
000886075 3367_ $$2ORCID$$aCONFERENCE_POSTER
000886075 3367_ $$2DataCite$$aOutput Types/Conference Poster
000886075 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1607093187_15664$$xOther
000886075 520__ $$aDue to technical advances, the number of neurons recorded in parallel increases drastically. This development reveals new types of artefacts: Common noise and cross-talk are observed in the raw parallel recording signals [1-3], as well as hyper-synchronous spike events at sampling rate precision in sorted spike data (‚synchrofacts‘; 3-5). These likely originate from environmental electromagnetic signals that couple into the recording signals. Here we concentrate on synchrofacts and their effects on results of spike data analyses, such as cross-correlation and higher-order synchronous or spatio-temporal spike patterns.In experimental data sets, e.g. recorded in macaque M1/PM1 with 100-electrode Utah arrays and manually spike sorted, we noticed synchrofacts in population histograms at a bin size matching the sampling rate (30 kHz). The complexity distribution [6], i.e. the histogram of synchronous events of a certain size (number of spikes across neurons) and their counts, shows entries up to 60, far larger than predicted by independent data of the same rate. Not all channels participate equally, but this is not related to the spatial electrode distribution.To systematically study the effects of the synchrofacts on analysis results, we formulate a stochastic data generation model in which we have control over synchrofacts, ‚neuronal‘ correlations and firing rates. We model background activity as independent Poisson processes and inject ‚neuronal‘ correlations and synchrofacts each formulated by separate Compound Poisson Processes (CPPs, 7-8). A CPP generates synchronous events with event sizes given by its amplitude distribution and inserts these spikes randomly into the neuronal spike trains. To model the observed synchrofacts we adapt the spike assignment with a non-uniform assignment distribution.In the next step, we apply various analysis methods to the artificial data to determine how synchrofacts affect the analysis results. Questions we are going to address are: a) does the presence of synchrofacts decrease the detectability of neuronal correlation activity, b) which type of correlation activity (pairwise or higher-order) is more diluted, and c) which synchrofact parameters (rate, correlation order, distribution over neurons, distribution over time) are mostly affecting the results. In order to do this we compare the analysis results from data with and without synchrofacts. This allows us to propose suitable methods of dealing with them.ReferencesMusial P, Baker S, Gerstein G, King E, Keating J (2002) Signal-to-noise ratio improvement in multiple electrode recording, Journal of Neuroscience Methods, Volume 115, Issue 1, Pages 29-43, ISSN 0165-0270, 10.1016/S0165-0270(01)00516-7Dann B, Michaels JA, Schaffelhofer S, Scherberger H (2016) Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates, eLife 2016;5:e15719, 10.7554/eLife.15719Essink S, Kleinjohann A, Barthélemy F, Ito J, Riehle A, Brochier T, Grün S (2019) Detection and Removal of Artefacts in Multi-Channel Electrophysiology Recordings, Bernstein Conference 2019, 10.12751/nncn.bc2019.0068Sprenger J (2014) Spatial Dependence of the Spike-Related Component of the Local Field Potential in Motor Cortex (Master’s thesis, RWTH Aachen).Torre E, Quaglio P, Denker M, Brochier T, Riehle A, Grün S (2016) Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task, J. Neurosci. 36(32):8329-8340, 10.1523/JNEUROSCI.4375-15.2016Grün S, Abeles M, Diesmann M (2008) Impact of Higher-Order Correlations on Coincidence Distributions of Massively Parallel Data, in: Marinaro M, Scarpetta S, Yamaguchi Y (eds) Dynamic Brain - from Neural Spikes to Behaviors, NN 2007, Lecture Notes in Computer Science, vol 5286. Springer, Berlin, 10.1007/978-3-540-88853-6_8Kuhn A, Aertsen A, Rotter S (2003) Higher-Order Statistics of Input Ensembles and the Response of Simple Model Neurons, Neural Computation 15:1, 67-101, 10.1162/089976603321043702Staude B, Grün S, Rotter S (2010) Higher-Order Correlations and Cumulants, in: Grün S, Rotter S (eds) Analysis of Parallel Spike Trains, Springer Series in Computational Neuroscience, vol 7, Springer, Boston, MA, 10.1007/978-1-4419-5675-0_12
000886075 536__ $$0G:(DE-HGF)POF3-571$$a571 - Connectivity and Activity (POF3-571)$$cPOF3-571$$fPOF III$$x0
000886075 536__ $$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
000886075 536__ $$0G:(GEPRIS)238707842$$aDFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842)$$c238707842$$x2
000886075 536__ $$0G:(GEPRIS)322093511$$aDFG project 322093511 - Kognitive Leistung als Ergebnis koordinierter neuronaler Aktivität in unreifen präfrontal-hippokampalen Netzwerken (322093511)$$c322093511$$x3
000886075 536__ $$0G:(GEPRIS)238707842$$aDFG project 238707842 - Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination (238707842)$$c238707842$$x4
000886075 536__ $$0G:(GEPRIS)237833830$$aDFG project 237833830 - Optogenetische Analyse der für kognitive Fähigkeiten zuständigen präfrontal-hippokampalen Netzwerke in der Entwicklung (237833830)$$c237833830$$x5
000886075 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x6
000886075 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x7
000886075 588__ $$aDataset connected to DataCite
000886075 7001_ $$0P:(DE-Juel1)161295$$aSprenger, Julia$$b1$$ufzj
000886075 7001_ $$0P:(DE-Juel1)176777$$aEssink, Simon$$b2$$ufzj
000886075 7001_ $$0P:(DE-HGF)0$$aRotter, Stefan$$b3
000886075 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b4$$ufzj
000886075 773__ $$a10.12751/NNCN.BC2020.0095
000886075 8564_ $$uhttps://juser.fz-juelich.de/record/886075/files/Poster.pdf$$yOpenAccess
000886075 8564_ $$uhttps://juser.fz-juelich.de/record/886075/files/Poster.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000886075 909CO $$ooai:juser.fz-juelich.de:886075$$pec_fundedresources$$pdriver$$pVDB$$popen_access$$popenaire
000886075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176920$$aForschungszentrum Jülich$$b0$$kFZJ
000886075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161295$$aForschungszentrum Jülich$$b1$$kFZJ
000886075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176777$$aForschungszentrum Jülich$$b2$$kFZJ
000886075 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b4$$kFZJ
000886075 9131_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000886075 9141_ $$y2020
000886075 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000886075 920__ $$lno
000886075 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000886075 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000886075 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000886075 9801_ $$aFullTexts
000886075 980__ $$aposter
000886075 980__ $$aVDB
000886075 980__ $$aUNRESTRICTED
000886075 980__ $$aI:(DE-Juel1)INM-6-20090406
000886075 980__ $$aI:(DE-Juel1)IAS-6-20130828
000886075 980__ $$aI:(DE-Juel1)INM-10-20170113
000886075 981__ $$aI:(DE-Juel1)IAS-6-20130828