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| 001 | 916364 | ||
| 005 | 20240313103121.0 | ||
| 024 | 7 | _ | |a 10.48550/arXiv.2211.16592 |2 doi |
| 024 | 7 | _ | |a 2128/33347 |2 Handle |
| 037 | _ | _ | |a FZJ-2022-06165 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Bouhadjar, Younes |0 P:(DE-Juel1)176778 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Sequence learning in a spiking neuronal network with memristive synapses |
| 260 | _ | _ | |c 2022 |b ArXiv |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1672815674_11195 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They 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 learning and prediction. 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 is not only important to advance neuroscience but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that holds promise for efficiently running this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural simulator NEST. We investigate the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate resilience with respect to different on-off ratios, conductance resolutions, device variability, and synaptic failure. |
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| 650 | _ | 7 | |a Neural and Evolutionary Computing (cs.NE) |2 Other |
| 650 | _ | 7 | |a Emerging Technologies (cs.ET) |2 Other |
| 650 | _ | 7 | |a FOS: Computer and information sciences |2 Other |
| 700 | 1 | _ | |a Siegel, Sebastian |0 P:(DE-Juel1)174486 |b 1 |u fzj |
| 700 | 1 | _ | |a Tetzlaff, Tom |0 P:(DE-Juel1)145211 |b 2 |
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| 700 | 1 | _ | |a Wouters, Dirk J. |0 P:(DE-HGF)0 |b 5 |
| 773 | _ | _ | |a 10.48550/arXiv.2211.16592 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/916364/files/2211.16592.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2022 |
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