音乐神经编码中可靠性与维度之间的权衡

文献信息

中文摘要

聆听音乐所带来的丰富体验依赖于早期听觉通路对其组成元素的神经整合。在此，我们开展了首个针对复杂音乐神经反应的大规模研究，以表征在正常听力与轻度至中度听力损失情况下对单个乐器及混合音乐的神经编码。通过相干性和流形分析以及深度学习，我们发现了混合音乐中强烈的非线性相互作用，这些作用影响了神经编码的保真度和几何结构，且可靠性和维度在不同音乐类型间呈反向变化。这种权衡在对言语的反应中也很明显，并且在听力损失后对音乐仍然存在，而听力损失的影响通过适当的放大得到了很大程度的纠正。这些结果表明，音乐的神经编码涉及一种内在的权衡，并凸显了在处理具有不同频谱时间特性的声音时保持保真度所面临的基本挑战。

英文摘要

The rich experience of listening to music depends on the neural integration of its constituent elements within the early auditory pathway. Here, we performed the first large-scale study of neural responses to complex music to characterize the neural coding of individual instruments and mixtures with both normal hearing and mild-to-moderate hearing loss. Using coherence and manifold analyses along with deep learning, we identified strong nonlinear interactions in mixed music that impacted the fidelity and geometry of the neural code, with reliability and dimensionality varying inversely across music types. This tradeoff was also evident in responses to speech and persisted for music even after hearing loss, the effects of which were largely corrected with suitable amplification. These results suggest that the neural coding of music involves an inherent tradeoff and highlight a fundamental challenge in maintaining fidelity while processing sounds with varying spectrotemporal properties.