Saddler Mark R, McDermott Josh H
Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.
Nat Commun. 2024 Dec 4;15(1):10590. doi: 10.1038/s41467-024-54700-5.
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 remains uncertain. We optimized machine learning models to perform real-world hearing tasks with simulated cochlear input, assessing the precision of auditory nerve spike timing needed 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. However, the temporal precision needed to reproduce human-like behavior varied across tasks, as did the precision that benefited real-world task performance. These effects suggest that perceptual domains incorporate phase locking to different extents depending on the demands of real-world hearing. The results illustrate how optimizing models for realistic tasks can clarify the role of candidate neural codes in perception.
神经元除了通过放电率来编码信息外，还通过其尖峰的时间来编码信息。尖峰时间在听神经中特别精确，在听神经中动作电位以亚毫秒级精度与声音相位锁定，但其行为相关性仍不确定。我们优化了机器学习模型，以便利用模拟的耳蜗输入执行现实世界中的听力任务，评估再现人类行为所需的听神经尖峰时间精度。与没有高保真相位锁定的模型相比，具有高保真相位锁定的模型表现出更像人类的声音定位和语音感知，这与在人类听力中起重要作用一致。然而，再现类人行为所需的时间精度因任务而异，对现实世界任务表现有益的精度也是如此。这些效应表明，感知领域根据现实世界听力的需求在不同程度上纳入了相位锁定。结果说明了为现实任务优化模型如何能够阐明候选神经编码在感知中的作用。
