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000889280 0247_ $$2doi$$a10.1145/3381755.3381769
000889280 0247_ $$2Handle$$a2128/26744
000889280 037__ $$aFZJ-2021-00185
000889280 041__ $$aEnglish
000889280 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
000889280 1112_ $$aNeuro-inspired Computational Elements Workshop$$cHeidelberg$$d2020-03-17 - 2020-03-20$$gNICE$$wGermany
000889280 245__ $$aThe speed of sequence processing in biological neuronal networks
000889280 260__ $$c2020
000889280 300__ $$a1-3
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000889280 520__ $$aSequence processing has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) model provides a mechanistic implementation of this form of processing. While the model accounts for a number of neocortical features, it is based on networks of highly abstract neuron and synapse models updated in discrete time. Here, we reformulate the model in terms of a network of spiking neurons with continuous-time dynamics to investigate how neuronal and synaptic parameters constrain the sequence-processing speed.
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000889280 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x1
000889280 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000889280 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000889280 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b1
000889280 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk J.$$b2
000889280 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3
000889280 773__ $$a10.1145/3381755.3381769
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000889280 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000889280 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
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000889280 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x3
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