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| 001 | 1006807 | ||
| 005 | 20240313103125.0 | ||
| 020 | _ | _ | |a 978-3-95806-693-9 |
| 024 | 7 | _ | |2 Handle |a 2128/34379 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-20230718092150862-6422765-5 |
| 037 | _ | _ | |a FZJ-2023-01860 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-Juel1)176778 |a Bouhadjar, Younes |b 0 |e Corresponding author |
| 245 | _ | _ | |a A brain inspired sequence learning algorithm and foundations of a memristive hardware implementation |f - 2023-02-13 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2023 |
| 300 | _ | _ | |a xii, 149 |
| 336 | 7 | _ | |2 DataCite |a Output Types/Dissertation |
| 336 | 7 | _ | |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |a Book |m book |
| 336 | 7 | _ | |2 ORCID |a DISSERTATION |
| 336 | 7 | _ | |2 BibTeX |a PHDTHESIS |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)11 |2 PUB:(DE-HGF) |a Dissertation / PhD Thesis |b phd |m phd |s 1683536398_21000 |
| 336 | 7 | _ | |2 DRIVER |a doctoralThesis |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich Reihe Information / Information |v 95 |
| 502 | _ | _ | |a Dissertation, RWTH Aachen University, 2023 |b Dissertation |c RWTH Aachen University |d 2023 |
| 520 | _ | _ | |a The 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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| 536 | _ | _ | |0 G:(EU-Grant)785907 |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 3 |
| 536 | _ | _ | |0 G:(EU-Grant)945539 |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 4 |
| 536 | _ | _ | |0 G:(GEPRIS)491111487 |a DFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487) |c 491111487 |x 5 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1006807/files/Information_95.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2023 |
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