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001022226 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
001022226 1112_ $$aNeuromorphic, Natural and Physical Computing$$cHannover$$d2023-10-25 - 2023-10-27$$gNNPC$$wGermany
001022226 245__ $$aBio-inspired sequence learning mechanisms and their implementation in a memristive neuromorphic hardware
001022226 260__ $$c2023
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001022226 520__ $$aWe present a sequence learning model that explains how biological networks learn to predict upcoming elements, signal non-anticipated events, and recall sequences in response to a cue signal. The model accounts for anatomical and electrophysiological properties of cortical neuronal circuits and learns complex sequences in an unsupervised manner using known biological plasticity and homeostatic control mechanisms. We further investigate the feasibility of implementing the sequence learning model on dedicated hardware mimicking brain properties, specifically focusing on memristive crossbar arrays. Finally, we apply the model to sequence classification and anomaly detection in streams of real-world data, and discuss the role of dendritic branches for the sequence learning capacity.
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001022226 7001_ $$0P:(DE-Juel1)174486$$aSiegel, Sebastian$$b1$$ufzj
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001022226 7001_ $$0P:(DE-Juel1)145211$$aTetzlaff, Tom$$b6
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