Closing the loop

• Verfasst von: Stradmann, Yannik [VerfasserIn]
• Verfasst von: Schemmel, Johannes [VerfasserIn]
• Titel: Closing the loop
• Titelzusatz: high-speed robotics with accelerated neuromorphic hardware
• Verf.angabe: Yannik Stradmann and Johannes Schemmel
• E-Jahr: 2024
• Jahr: 26 March 2024
• Umfang: 6 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 27.05.2024
• Titel Quelle: Enthalten in: Frontiers in neuroscience
• Ort Quelle: Lausanne : Frontiers Research Foundation, 2007
• Jahr Quelle: 2024
• Band/Heft Quelle: 18(2024) vom: März, Artikel-ID 1360122, Seite 1-6
• ISSN Quelle: 1662-453X
• DOI: doi:10.3389/fnins.2024.1360122
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: analog neuromorphic computing
• Sach-SW: Closed-loop controller
• Sach-SW: High-speed robotics
• Sach-SW: motor control
• Sach-SW: neurorobotics
• Sach-SW: spiking neural networks
• K10plus-PPN: 1889918121
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

<p>The BrainScaleS-2 system is an established analog neuromorphic platform with versatile applications in the diverse fields of computational neuroscience and spike-based machine learning. In this work, we extend the system with a configurable realtime event interface that enables a tight coupling of its distinct analog network core to external sensors and actuators. The 1,000-fold acceleration of the emulated nerve cells allows us to target high-speed robotic applications that require precise timing on a microsecond scale. As a showcase, we present a closed-loop setup for commuting brushless DC motors: we utilize PyTorch to train a spiking neural network emulated on the analog substrate to control an electric motor from a sensory event stream. The presented system enables research in the area of event-driven controllers for high-speed robotics, including self-supervised and biologically inspired online learning for such applications.</p>