通过模拟休息与刺激，提升睡眠剥夺下的前向-前向算法性能。
Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm
- 引入间歇休息与阈值调节，缓解算法因数据处理失衡的失效问题。
- 在严重睡眠剥夺下（16次清醒+1次睡眠），准确率提升2%-62%。
- 方法增强算法鲁棒性，更贴近人类认知的自适应机制，适合类脑计算研究者。
本文针对前向-前向算法中睡眠剥夺带来的学习效能灾难性下降问题展开研究。该现象源于算法两阶段分离及数据处理失衡，类比人类睡眠不足时的认知状态。为缓解此问题，本文探索了替代激活、优化损失函数与阈值调优等策略，并引入周期性休息机制——通过减少交替周期中的正向传递次数，形成短时休整阶段。此外，还研究了咖啡因刺激对睡眠剥夺条件下性能的提升潜力。在MNIST和Fashion-MNIST数据集上的实验表明，上述改进显著提升了算法在睡眠剥夺情境下的表现：在极端设置（16次正向/清醒周期 + 1次负向/睡眠周期）下，准确率提升达2%-62%。这些方法不仅增强了算法的鲁棒性，也使其更符合人类认知的自适应特性。
原文摘要 · Abstract (English)
This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.
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