000893728 001__ 893728
000893728 005__ 20240313094844.0
000893728 037__ $$aFZJ-2021-02783
000893728 041__ $$aEnglish
000893728 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
000893728 1112_ $$aNEST Conference$$cOnline$$d2021-06-28 - 2021-06-29$$wOnline
000893728 245__ $$aSequence learning, prediction, and generation in networks of spiking neurons
000893728 260__ $$c2021
000893728 3367_ $$033$$2EndNote$$aConference Paper
000893728 3367_ $$2BibTeX$$aINPROCEEDINGS
000893728 3367_ $$2DRIVER$$aconferenceObject
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000893728 3367_ $$2DataCite$$aOutput Types/Conference Poster
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000893728 520__ $$aSequence learning, prediction and generation has been proposed to be the universal computation performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes this form of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context-specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific feedforward subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context-specific prediction of future sequence elementsas well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities.The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role ofsequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effectof the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.
000893728 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000893728 536__ $$0G:(DE-HGF)POF4-5232$$a5232 - Computational Principles (POF4-523)$$cPOF4-523$$fPOF IV$$x1
000893728 536__ $$0G:(DE-Juel1)aca_20190115$$aAdvanced Computing Architectures (aca_20190115)$$caca_20190115$$fAdvanced Computing Architectures$$x2
000893728 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$x3
000893728 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b1
000893728 7001_ $$0P:(DE-HGF)0$$aWouters, Dirk J.$$b2
000893728 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b3
000893728 909CO $$ooai:juser.fz-juelich.de:893728$$pec_fundedresources$$pVDB$$popenaire
000893728 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176778$$aForschungszentrum Jülich$$b0$$kFZJ
000893728 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b1$$kFZJ
000893728 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145211$$aForschungszentrum Jülich$$b3$$kFZJ
000893728 9130_ $$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$$x0
000893728 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5232$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000893728 9141_ $$y2021
000893728 920__ $$lyes
000893728 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000893728 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000893728 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
000893728 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x3
000893728 9201_ $$0I:(DE-Juel1)PGI-10-20170113$$kPGI-10$$lJARA Institut Green IT$$x4
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000893728 980__ $$aI:(DE-Juel1)INM-10-20170113
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000893728 980__ $$aI:(DE-Juel1)PGI-10-20170113
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