000885720 001__ 885720
000885720 005__ 20240313103122.0
000885720 0247_ $$2arXiv$$aarXiv:2009.08667
000885720 0247_ $$2Handle$$a2128/25926
000885720 037__ $$aFZJ-2020-04032
000885720 1001_ $$0P:(DE-Juel1)171475$$aStapmanns, Jonas$$b0$$eCorresponding author$$ufzj
000885720 245__ $$aEvent-based update of synapses in voltage-based learning rules
000885720 260__ $$c2020
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000885720 3367_ $$2ORCID$$aWORKING_PAPER
000885720 3367_ $$028$$2EndNote$$aElectronic Article
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000885720 3367_ $$2BibTeX$$aARTICLE
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000885720 500__ $$a42 pages, 12 figures, 7 tables
000885720 520__ $$aDue to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. Synapses therefore require continuous information to update their strength which a priori necessitates a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guiding principles for the future design of learning rules in order to make them practically usable in large-scale networks.
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000885720 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x2
000885720 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x3
000885720 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x4
000885720 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x5
000885720 588__ $$aDataset connected to arXivarXiv
000885720 7001_ $$0P:(DE-HGF)0$$aHahne, Jan$$b1
000885720 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
000885720 7001_ $$0P:(DE-HGF)0$$aBolten, Matthias$$b3
000885720 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b4$$ufzj
000885720 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b5$$ufzj
000885720 773__ $$parXiv:2009.08667 [q-bio.NC]
000885720 8564_ $$uhttps://arxiv.org/abs/2009.08667
000885720 8564_ $$uhttps://juser.fz-juelich.de/record/885720/files/arXiv%20preprint.pdf$$yOpenAccess
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000885720 9141_ $$y2020
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000885720 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000885720 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000885720 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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