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Neuromorphic Hardware Learns to Learn.

Thomas Bohnstingl1, Franz Scherr1, Christian Pehle2

  • 1Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.

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|June 11, 2019
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Summary
This summary is machine-generated.

Gradient-free optimization tools and meta-plasticity enhance neuromorphic hardware learning. This approach enables efficient reward-based learning and demonstrates Learning-to-Learn capabilities for accelerated task acquisition.

Keywords:
HICANN-DLSlearning-to-learnmarkov decision processesmeta-plasticitymulti-armed banditsneuromorphic hardwarespiking neural networkstransfer learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Neuromorphic hardware typically relies on manually selected hyperparameters and learning algorithms.
  • Biological neural networks are optimized through evolution and development for diverse tasks.
  • Existing emulation methods like genetic algorithms have limitations in scope and require manual design.

Purpose of the Study:

  • To adapt biological optimization processes for neuromorphic hardware using advanced gradient-free tools.
  • To enhance the reward-based learning efficiency of neuromorphic agents.
  • To demonstrate Learning-to-Learn capabilities on neuromorphic hardware.

Main Methods:

  • Employing gradient-free optimization tools, specifically cross-entropy methods and evolutionary strategies.
  • Implementing meta-plasticity to optimize learning rules within the hardware.
  • Utilizing accelerated neuromorphic hardware for large-scale network computations.

Main Results:

  • Optimization algorithms significantly improve neuromorphic agents' learning efficiency from rewards.
  • Meta-plasticity substantially enhances the reward-based learning capabilities of the hardware.
  • Demonstrated Learning-to-Learn benefits, including abstract knowledge extraction for faster learning on related tasks.

Conclusions:

  • Gradient-free optimization and meta-plasticity are effective for neuromorphic hardware.
  • Neuromorphic hardware can achieve efficient reward-based learning and Learning-to-Learn.
  • This approach holds promise for advancing AI and neuromorphic computing capabilities.