001037900 001__ 1037900
001037900 005__ 20250203103256.0
001037900 037__ $$aFZJ-2025-01038
001037900 1001_ $$0P:(DE-Juel1)198927$$aFeiler, Florian$$b0
001037900 1112_ $$aInternational Conference on Neuromorphic Systems (ICONS)$$cArlington, Virginia$$d2024-07-30 - 2024-08-02$$wUSA
001037900 245__ $$aUnsupervised Learning of Spatio-Temporal Patterns in Spiking Neuronal Networks
001037900 260__ $$c2024
001037900 300__ $$a366 - 370
001037900 3367_ $$2ORCID$$aCONFERENCE_PAPER
001037900 3367_ $$033$$2EndNote$$aConference Paper
001037900 3367_ $$2BibTeX$$aINPROCEEDINGS
001037900 3367_ $$2DRIVER$$aconferenceObject
001037900 3367_ $$2DataCite$$aOutput Types/Conference Paper
001037900 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1738230155_8094
001037900 520__ $$aThe ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain management. While modern supervised Machine Learning approaches excel at such sequential tasks, they are computationally expensive and require large training data. A previous work presented a biologically plausible sequence learning model, developed through a bottom-up approach, consisting of a spiking neural network and unsupervised local learning rules. The model in its original formulation identifies only a specific type of sequence elements composed of synchronous spikes by activating a subset of neurons with identical stimulus preference. In this work, we extend the model to detect and learn sequences of various spatio-temporal patterns (STPs) by incorporating plastic connections in the input synapses. We showcase that the model is able to learn and predict high-order sequences. We further study the robustness of the model against different input settings and parameters.
001037900 536__ $$0G:(DE-HGF)POF4-5234$$a5234 - Emerging NC Architectures (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001037900 536__ $$0G:(DE-82)BMBF-16ME0398K$$aBMBF 16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)$$cBMBF-16ME0398K$$x1
001037900 536__ $$0G:(DE-82)BMBF-16ME0399$$aBMBF 16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0399)$$cBMBF-16ME0399$$x2
001037900 7001_ $$0P:(DE-Juel1)188273$$aNeftci, Emre$$b1
001037900 7001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b2$$eCorresponding author
001037900 909CO $$ooai:juser.fz-juelich.de:1037900$$pVDB
001037900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188273$$aForschungszentrum Jülich$$b1$$kFZJ
001037900 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176778$$aForschungszentrum Jülich$$b2$$kFZJ
001037900 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5234$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001037900 9141_ $$y2024
001037900 920__ $$lyes
001037900 9201_ $$0I:(DE-Juel1)PGI-15-20210701$$kPGI-15$$lNeuromorphic Software Eco System$$x0
001037900 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x1
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001037900 980__ $$aVDB
001037900 980__ $$aI:(DE-Juel1)PGI-15-20210701
001037900 980__ $$aI:(DE-Juel1)PGI-7-20110106
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