通过扰动和对抗性梦境学习皮层表征
Learning cortical representations through perturbed and adversarial dreaming
摘要：
人类和其他动物学会从感官经验中提取一般概念，而无需大量的教学。这种能力被认为是由离线状态（如睡眠）促进的，在这种状态下，以前的经历会系统地重演。然而，梦的创造性特征表明，学习语义表征可能不仅仅是重复以前的经验。我们通过实现受生成对抗网络（GANs）启发的皮层架构来支持这一假设。在我们的模型中，学习是在三种不同的全球大脑状态中组织起来的，模拟了觉醒、NREM和REM睡眠，优化了不同但互补的目标函数。我们在标准的自然图像数据集上训练模型，并评估学习到的表示的质量。我们的研究结果表明，在REM睡眠期间通过对抗性做梦产生新的虚拟感觉输入对于提取语义概念至关重要，而在NREM睡眠期间通过扰动做梦重放情景记忆可以提高潜在表征的鲁棒性。该模型为睡眠状态、记忆回放和梦境提供了一个新的计算视角，并提出了GANs的皮层实现。
英文摘要：
Humans and other animals learn to extract general concepts from sensory experience without extensive teaching. This ability is thought to be facilitated by offline states like sleep where previous experiences are systemically replayed. However, the characteristic creative nature of dreams suggests that learning semantic representations may go beyond merely replaying previous experiences. We support this hypothesis by implementing a cortical architecture inspired by generative adversarial networks (GANs). Learning in our model is organized across three different global brain states mimicking wakefulness, NREM and REM sleep, optimizing different, but complementary objective functions. We train the model on standard datasets of natural images and evaluate the quality of the learned representations. Our results suggest that generating new, virtual sensory inputs via adversarial dreaming during REM sleep is essential for extracting semantic concepts, while replaying episodic memories via perturbed dreaming during NREM sleep improves the robustness of latent representations. The model provides a new computational perspective on sleep states, memory replay and dreams and suggests a cortical implementation of GANs.
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。
