ArticleFrontiers in neuroscience2022
Solving the spike feature information vanishing problem in spiking deep Q network with potential based normalization.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Exploring spiking neural networks for deep reinforcement learning in robotic tasks.Scientific reports · 2024Article
- BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction.Frontiers in neuroscience · 2024Article
- Brain-inspired neural circuit evolution for spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition.Patterns (New York, N.Y.) · 2023Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
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3 authors.
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Abstract
Brain-inspired spiking neural networks (SNNs) are successfully applied to many pattern recognition domains. The SNNs-based deep structure has achieved considerable results in perceptual tasks, such as image classification and target detection. However, applying deep SNNs in reinforcement learning (RL) tasks is still a problem to be explored. Although there have been previous studies on the combination of SNNs and RL, most focus on robotic control problems with shallow networks or using the ANN-SNN conversion method to implement spiking deep Q networks (SDQN). In this study, we mathematically analyzed the problem of the disappearance of spiking signal features in SDQN and proposed a potential-based layer normalization (pbLN) method to train spiking deep Q networks directly. Experiment shows that compared with state-of-art ANN-SNN conversion method and other SDQN works, the proposed pbLN spiking deep Q networks (PL-SDQN) achieved better performance on Atari game tasks.
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