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000865505 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
000865505 1112_ $$aBernstein conference$$cBerlin$$d2019-09-17 - 2019-09-21$$wGermany
000865505 245__ $$aConstraints on sequence processing speed in biological neuronal networks
000865505 260__ $$c2019
000865505 3367_ $$033$$2EndNote$$aConference Paper
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000865505 520__ $$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 parameters such as cell-intrinsic time constants and synaptic weights constrain the sequence-processing speed.
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000865505 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x2
000865505 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000865505 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b1
000865505 7001_ $$0P:(DE-Juel1)131022$$aWaser, R.$$b2
000865505 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk J.$$b3
000865505 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b4
000865505 8564_ $$uhttps://juser.fz-juelich.de/record/865505/files/poster_younes_bouhadjar_sequence_learning.pdf$$yOpenAccess
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000865505 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
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