Neuromorphic dreaming as a pathway to efficient learning in artificial agents
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Date
2025-12
Publication Type
Journal Article
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yes
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Abstract
The computational substrate of biological systems exhibits remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we implement model-based reinforcement learning using spiking neural networks directly on mixed-signal neuromorphic hardware. This approach combines energy-efficient electronic circuits with high sample efficiency through alternating online (‘awake’) and offline (‘dreaming’) learning phases. Our model features two networks: an agent network that learns from real and simulated experiences and a world model network that generates simulated experiences. We validate this by training the system to play Atari Pong. First, we establish a baseline using only real experiences. Then, by ‘dreaming’, the required real experiences decrease significantly. The network dynamics runs in real-time on the analog neuromorphic circuits, with only the readout layers implemented and trained on a computer-in-the-loop. We present results that demonstrate the robustness and potential of energy-efficient mixed-signal neuromorphic processors for real-world applications.
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published
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Journal / series
Volume
5 (4)
Pages / Article No.
44005
Publisher
IOP Publishing
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Software
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Subject
neuromorphic computing; spiking neural networks; model-based reinforcement learning; offline learning; sample efficiency; energy-efficient learning