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Summary
This summary is machine-generated.

The BrainScaleS-2 system now integrates a real-time event interface, enabling high-speed robotic control. This advancement facilitates research in event-driven, self-supervised learning for robotics.

Keywords:
analog neuromorphic computingclosed-loop controllerhigh-speed roboticsmotor controlneuroroboticsspiking neural networks

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Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Robotics

Background:

  • The BrainScaleS-2 system is a versatile analog neuromorphic platform.
  • Existing systems have limitations in real-time external coupling for high-speed applications.

Purpose of the Study:

  • To enhance the BrainScaleS-2 system with a configurable real-time event interface.
  • To enable tight coupling of the analog network core with external sensors and actuators.
  • To target high-speed robotic applications requiring microsecond-level timing precision.

Main Methods:

  • Integration of a configurable real-time event interface with the BrainScaleS-2 system.
  • Utilizing PyTorch for training a spiking neural network (SNN).
  • Emulating the SNN on the analog substrate to control a brushless DC motor in a closed-loop setup.

Main Results:

  • Successful extension of BrainScaleS-2 with a real-time event interface.
  • Demonstration of microsecond-scale precise timing for robotic control.
  • A closed-loop system controlling a brushless DC motor using an emulated SNN from sensory input.

Conclusions:

  • The enhanced BrainScaleS-2 system facilitates research in event-driven controllers for high-speed robotics.
  • The system supports self-supervised and biologically inspired online learning for robotic applications.
  • This work opens new avenues for real-time, adaptive neuromorphic control in robotics.