Abstract
Sleep is known to play a crucial role in memory and learning processes. During sleep, the replay of neural activity patterns associated with recent memories is believed to contribute to memory consolidation. Inspired by the sleep replay process in the biological system, we proposed a brain-like memory replay algorithm consisting of three phases. First, an artificial neural network (ANN) underwent learning, mimicking the cognitive processes of the awake stage. Next, we conducted a simulation where slow oscillation sleep or awake stages were simulated in a spiking neural network (SNN). The network connections and initial synaptic weights are identical to those of the trained ANN. Lastly, the performance of the ANN is evaluated and validated during the awake phase using the synaptic weights converted from the SNN. We applied this brain-inspired memory replay algorithm to a two-digit MNIST classification task. We found that as the level of noise increases, the model exhibits a significant improvement in performance after a period of sleep compared to the awake state. These findings provide compelling evidence that sleep-dependent memory consolidation can significantly enhance the network performance and improve its the robustness. Overall, this study sheds light on the potential advantages of incorporating sleep-like processes into neural networks, offering valuable insights into the field.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (12101570), Zhejiang Lab & Pujiang Lab (K2023KA1BB01), and Scientific Projects of Zhejiang Lab (K2023KI0AA02, 2021KE0PI03).
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Xie, S., Tang, T., Feng, L., Lin, F., Wei, Y. (2023). Sleep-Dependent Memory Replay Enables Brain-Like Robustness in Neural Networks. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_19
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DOI: https://doi.org/10.1007/978-981-99-6483-3_19
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