迈向平衡传播中的终身学习：通过类睡眠和清醒复述增强稳定性
Toward Lifelong Learning in Equilibrium Propagation: Sleep-like and Awake Rehearsal for Enhanced Stability
AI总结：
针对平衡传播训练的循环神经网络持续学习中的灾难性遗忘问题，提出类睡眠重放巩固算法，结合类睡眠与清醒复述，显著提升稳定性，在多数据集上超越现有方法，为人工神经网络整合类人类学习行为提供新方向。
摘要：
使用平衡传播（EP）——一种具有生物学合理性的训练算法——训练的循环神经网络（RNNs）在图像分类和强化学习等各类任务中表现出强大性能。然而，这些网络在持续学习中面临关键挑战：灾难性遗忘，即学习新任务时先前获得的知识被覆盖。这一局限与人类大脑在睡眠期间通过重放学习信息进行记忆巩固等过程辅助下保留并整合新旧知识的能力形成对比。为解决RNNs中的这一挑战，我们提出一种适用于EP训练RNNs的类睡眠重放巩固（SRC）算法。研究发现，SRC显著提升了RNN在持续学习场景中对灾难性遗忘的抵御能力。在每次新任务训练后实施SRC的类增量学习中，EP训练的多层RNN模型（MRNN-EP）相比集成多种成熟正则化技术的前馈网络表现显著更优。在MNIST数据上，配备SRC的MRNN-EP与使用时间反向传播（BPTT）训练的MRNN性能相当，且在Fashion MNIST、Kuzushiji-MNIST、CIFAR10和ImageNet数据集上超越了基于BPTT的模型。将SRC与被称为“清醒复述”的复述相结合，进一步增强了网络在持续学习新任务时保留长期知识的能力。本研究揭示了类睡眠重放技术在RNNs中的适用性，并强调了将类人类学习行为整合到人工神经网络（ANNs）中的潜力。
英文摘要：
Recurrent neural networks (RNNs) trained using Equilibrium Propagation (EP), a biologically plausible training algorithm, have demonstrated strong performance in various tasks such as image classification and reinforcement learning. However, these networks face a critical challenge in continuous learning: catastrophic forgetting, where previously acquired knowledge is overwritten when new tasks are learned. This limitation contrasts with the human brain's ability to retain and integrate both old and new knowledge, aided by processes like memory consolidation during sleep through the replay of learned information. To address this challenge in RNNs, here we propose a sleep-like replay consolidation (SRC) algorithm for EP-trained RNNs. We found that SRC significantly improves RNN's resilience to catastrophic forgetting in continuous learning scenarios. In class-incremental learning with SRC implemented after each new task training, the EP-trained multilayer RNN model (MRNN-EP) performed significantly better compared to feedforward networks incorporating several well-established regularization techniques. The MRNN-EP performed on par with MRNN trained using Backpropagation Through Time (BPTT) when both were equipped with SRC on MNIST data and surpassed BPTT-based models on the Fashion MNIST, Kuzushiji-MNIST, CIFAR10, and ImageNet datasets. Combining SRC with rehearsal, also known as "awake replay", further boosted the network's ability to retain long-term knowledge while continuing to learn new tasks. Our study reveals the applicability of sleep-like replay techniques to RNNs and highlights the potential for integrating human-like learning behaviors into artificial neural networks (ANNs).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
