Neurons encode information in the timing of their spikes in addition to their firing rates. Spike timing is particularly precise in the auditory nerve, where action potentials phase lock to sound with sub- millisecond precision, but its behavioral relevance is uncertain. To investigate the role of this temporal coding, we optimized machine learning models to perform real-world hearing tasks with simulated cochlear input. We asked how precise auditory nerve spike timing needed to be to reproduce human behavior. Models with high-fidelity phase locking exhibited more human-like sound localization and speech perception than models without, consistent with an essential role in human hearing. Degrading phase locking produced task-dependent effects, revealing how the use of fine-grained temporal information reflects both ecological task demands and neural implementation constraints. The results link neural coding to perception and clarify conditions in which prostheses that fail to restore high-fidelity temporal coding could in principle restore near-normal hearing.

中文翻译：

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针对现实世界任务优化的模型揭示了听力中精确时间编码的必要性

神经元除了放电率之外还编码其尖峰时间的信息。尖峰计时在听觉神经中特别精确，其中动作电位以亚毫秒精度锁相到声音，但其行为相关性是不确定的。为了研究这种时间编码的作用，我们优化了机器学习模型，以通过模拟耳蜗输入执行现实世界的听力任务。我们询问听觉神经尖峰计时需要多精确才能重现人类行为。具有高保真锁相的模型比没有高保真锁相的模型表现出更接近人类的声音定位和语音感知，这与人类听力的重要作用一致。降级锁相产生了任务依赖性效应，揭示了细粒度时间信息的使用如何反映生态任务需求和神经实现约束。结果将神经编码与感知联系起来，并阐明了无法恢复高保真时间编码的假肢原则上可以恢复接近正常听力的条件。