提出连续时间反馈对齐模型，解释其在生物时间尺度下为何有效或失效。
Does Feedback Alignment Work at Biological Timescales?
- 构建神经活动与突触权重协同演化的连续动态模型。
- 学习依赖前突触驱动与误差信号的时间重叠，重叠消失则学习失败。
- 揭示生物时间尺度下算法成功的关键在于时间重叠原则，适用于神经科学领域研究者。
反馈对齐及其相关无权重传输算法常被视为反向传播的生物合理性替代方案，但通常以离散阶段形式呈现，隐含前向与误差信号的同步。我们构建了一个连续时间的反馈对齐型学习模型，其中神经活动与突触权重在具有不同传播、可塑性及衰减时间常数的耦合一阶动力学下共同演化。结果表明，学习由前突触驱动与局部投影误差信号之间的时间重叠所决定，为对中等时间错位的鲁棒性以及完全消除重叠时的失败提供了分析解释。研究显示，反馈对齐型算法要在生物时间尺度上运作，必须遵循与其他生物过程（如可塑性痕迹）相同的时空重叠原则。
原文摘要 · Abstract (English)
Feedback alignment and related weight-transport-free algorithms are often proposed as biologically plausible alternatives to backpropagation, yet they are typically formulated in discrete phases with implicitly synchronized forward and error signals. We develop a continuous-time model of feedback-alignment-type learning in which neural activities and synaptic weights evolve together under coupled first-order dynamics with distinct propagation, plasticity, and decay time constants. We show that learning is governed by the temporal overlap between presynaptic drive and a locally projected error signal, providing an analytic explanation for robustness to moderate timing mismatch and for failure when mismatch eliminates overlap. Our results show that in order for feedback-alignment-type algorithms to work at biological timescales, they must obey the same temporal overlap principle that applies to other biological processes like eligibility traces.
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