| |  Online-Ressource |
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| Verfasst von: | Billaudelle, Sebastian [VerfasserIn]  |
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| | Cramer, Benjamin [VerfasserIn] |
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| | Petrovici, Mihai A. [VerfasserIn] |
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| | Schreiber, Korbinian [VerfasserIn] |
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| | Kappel, David [VerfasserIn] |
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| | Schemmel, Johannes [VerfasserIn] |
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| | Meier, Karlheinz [VerfasserIn] |
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| Titel: | Structural plasticity on an accelerated analog neuromorphic hardware system |
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| Verf.angabe: | Sebastian Billaudelle, Benjamin Cramer, Mihai A. Petrovici, Korbinian Schreiber, David Kappel, Johannes Schemmel, Karlheinz Meier |
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| Jahr: | 2021 |
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| Jahr des Originals: | 2020 |
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| Umfang: | 10 S. |
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| Fussnoten: | Available online: 12 October 2020 ; Gesehen am 01.02.2021 |
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| Titel Quelle: | Enthalten in: Neural networks |
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| Ort Quelle: | Amsterdam : Elsevier, 1988 |
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| Jahr Quelle: | 2021 |
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| Band/Heft Quelle: | 133(2021), Seite 11-20 |
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| ISSN Quelle: | 1879-2782 |
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| Abstract: | In computational neuroscience, as well as in machine learning, neuromorphic devices promise an accelerated and scalable alternative to neural network simulations. Their neural connectivity and synaptic capacity depend on their specific design choices, but is always intrinsically limited. Here, we present a strategy to achieve structural plasticity that optimizes resource allocation under these constraints by constantly rewiring the pre- and postsynaptic partners while keeping the neuronal fan-in constant and the connectome sparse. In particular, we implemented this algorithm on the analog neuromorphic system BrainScaleS-2. It was executed on a custom embedded digital processor located on chip, accompanying the mixed-signal substrate of spiking neurons and synapse circuits. We evaluated our implementation in a simple supervised learning scenario, showing its ability to optimize the network topology with respect to the nature of its training data, as well as its overall computational efficiency. |
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| DOI: | doi:10.1016/j.neunet.2020.09.024 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag: https://doi.org/10.1016/j.neunet.2020.09.024 |
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| | Volltext: http://www.sciencedirect.com/science/article/pii/S0893608020303555 |
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| | DOI: https://doi.org/10.1016/j.neunet.2020.09.024 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | BrainScaleS |
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| | Neural networks |
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| | Receptive fields |
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| | Spiking |
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| | Structural plasticity |
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| K10plus-PPN: | 1746319127 |
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| Verknüpfungen: | → Zeitschrift |
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Structural plasticity on an accelerated analog neuromorphic hardware system / Billaudelle, Sebastian [VerfasserIn]; 2021 (Online-Ressource)
