001006807 001__ 1006807
001006807 005__ 20240313103125.0
001006807 0247_ $$2Handle$$a2128/34379
001006807 0247_ $$2URN$$aurn:nbn:de:0001-20230718092150862-6422765-5
001006807 020__ $$a978-3-95806-693-9
001006807 037__ $$aFZJ-2023-01860
001006807 041__ $$aEnglish
001006807 1001_ $$0P:(DE-Juel1)176778$$aBouhadjar, Younes$$b0$$eCorresponding author
001006807 245__ $$aA brain inspired sequence learning algorithm and foundations of a memristive hardware implementation$$f- 2023-02-13
001006807 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2023
001006807 300__ $$axii, 149
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001006807 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1683536398_21000
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001006807 4900_ $$aSchriften des Forschungszentrums Jülich Reihe Information / Information$$v95
001006807 502__ $$aDissertation, RWTH Aachen University, 2023$$bDissertation$$cRWTH Aachen University$$d2023
001006807 520__ $$aThe brain uses intricate biological mechanisms and principles to solve a variety of tasks. These principles endow systems with self-learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence prediction and replay.  This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation advances neuroscience and paves the way for new technological brain-inspired applications. In the first part of this thesis, we propose 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 by means of known biological plasticity and homeostatic control mechanisms. After learning, it self-organizes into a configuration characterized by a high degree of sparsity in connectivity and activity allowing for both high storage capacity and efficient energy usage. In the second part, we extend the sequence learning model such that it permits probabilistic sequential memory recall in response to ambiguous cues. In the absence of noise, the model deterministically recalls the sequence shown most frequently during training. We investigate how different forms of noise give rise to more exploratory behavior. We show that uncorrelated noise averages out in population based encoding leading to non-exploratory dynamics. Locally coherent noise in the form of random stimulus locking to spatiotemporal oscillations addresses this issue.  Our results show that depending on the amplitude and frequency of oscillation, the network can recall learned sequences according to different strategies: either always replay the most frequent sequence, or replay sequences according to their occurrence probability during training. The study contributes to an understanding of the neuronal mechanisms underlying different decision strategies in the face of ambiguity, and highlights the role of coherent network activity during sequential memory recall. Finally, we investigate the feasibility of implementing the sequence learning model on dedicated hardware mimicking brain properties. Here, we focus on a type of hardware where synapses are emulated by memristive devices. As a first step in this direction, we replace the synapse dynamics of the original model with dynamics describing the phenomenological behavior of memristive elements, and demonstrate resilience with respect to different device characteristics. In this thesis, we further describe how the sequence learning model can be adapted at the algorithmic level to foster an implementation in a full electronic circuit centered around a memristive crossbar array. Overall, this thesis sheds light on the key mechanisms underlying sequence learning, prediction, and replay in biological networks and demonstrates the feasibility of implementing this type of computation on neuromorphic hardware.
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001006807 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x3
001006807 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x4
001006807 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x5
001006807 8564_ $$uhttps://juser.fz-juelich.de/record/1006807/files/Information_95.pdf$$yOpenAccess
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001006807 9141_ $$y2023
001006807 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176778$$aForschungszentrum Jülich$$b0$$kFZJ
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001006807 9131_ $$0G:(DE-HGF)POF3-574$$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$$vTheory, modelling and simulation$$x1
001006807 920__ $$lyes
001006807 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
001006807 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
001006807 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
001006807 9201_ $$0I:(DE-Juel1)PGI-10-20170113$$kPGI-10$$lJARA Institut Green IT$$x3
001006807 9201_ $$0I:(DE-Juel1)PGI-7-20110106$$kPGI-7$$lElektronische Materialien$$x4
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