000837670 001__ 837670
000837670 005__ 20240313094904.0
000837670 037__ $$aFZJ-2017-06532
000837670 1001_ $$0P:(DE-Juel1)164187$$aKrishnan, Jeyashree$$b0$$ufzj
000837670 245__ $$aPerfect spike detection via time reversal
000837670 260__ $$c2017
000837670 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1669388162_20906
000837670 3367_ $$2ORCID$$aWORKING_PAPER
000837670 3367_ $$028$$2EndNote$$aElectronic Article
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000837670 3367_ $$2BibTeX$$aARTICLE
000837670 3367_ $$2DataCite$$aOutput Types/Working Paper
000837670 520__ $$aSpiking neuronal networks are usually simulated with three main simulation schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but faster than an alternative perfect tests based on bisection or root-finding methods. Comparison confirms earlier results that the imperfect test rarely misses spikes (less than a fraction $1/10^8$ of missed spikes) in biologically relevant settings. This study offers an alternative geometric point of view on neuronal dynamics.
000837670 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000837670 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x1
000837670 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x2
000837670 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x3
000837670 536__ $$0G:(DE-Juel1)HGF-SMHB-2013-2017$$aSMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017)$$cHGF-SMHB-2013-2017$$fSMHB$$x4
000837670 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x5
000837670 7001_ $$0P:(DE-Juel1)165939$$aMana, PierGianLuca$$b1
000837670 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
000837670 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b3$$ufzj
000837670 7001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b4$$ufzj
000837670 909CO $$ooai:juser.fz-juelich.de:837670$$pec_fundedresources$$pVDB$$popenaire
000837670 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164187$$aForschungszentrum Jülich$$b0$$kFZJ
000837670 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b2$$kFZJ
000837670 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b3$$kFZJ
000837670 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144723$$aForschungszentrum Jülich$$b4$$kFZJ
000837670 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
000837670 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$$vTheory, modelling and simulation$$x1
000837670 9141_ $$y2017
000837670 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x0
000837670 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x1
000837670 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000837670 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x3
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000837670 980__ $$aI:(DE-Juel1)INM-6-20090406
000837670 980__ $$aI:(DE-Juel1)INM-10-20170113
000837670 980__ $$aI:(DE-Juel1)JSC-20090406
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