The inferior colliculus (IC) of the midbrain is important for complex sound processing, such as discriminating conspecific vocalizations and human speech. The IC's non-lemniscal, dorsal "shell" region is likely important for this process, as neurons in these layers project to higher-order thalamic nuclei that subsequently funnel acoustic signals to the amygdala and non-primary auditory cortices; forebrain circuits important for vocalization coding in a variety of mammals, including humans. However, the extent to which shell IC neurons transmit acoustic features necessary to discern vocalizations is less clear, owing to the technical difficulty of recording from neurons in the IC's superficial layers via traditional approaches. Here we use 2-photon Ca²⁺ imaging in mice of either sex to test how shell IC neuron populations encode the rate and depth of amplitude modulation, important sound cues for speech perception. Most shell IC neurons were broadly tuned, with a low neurometric discrimination of amplitude modulation rate; only a subset were highly selective to specific modulation rates. Nevertheless, neural network classifier trained on fluorescence data from shell IC neuron populations accurately classified amplitude modulation rate, and decoding accuracy was only marginally reduced when highly tuned neurons were omitted from training data. Rather, classifier accuracy increased monotonically with the modulation depth of the training data, such that classifiers trained on full-depth modulated sounds had median decoding errors of ~0.2 octaves. Thus, shell IC neurons may transmit time-varying signals via a population code, with perhaps limited reliance on the discriminative capacity of any individual neuron.

中文翻译：

---

非丘系下丘中时变声音的群体编码

中脑的下丘 (IC) 对于复杂的声音处理非常重要，例如区分同种发声和人类语音。 IC 的非丘系、背侧“壳”区域可能对此过程很重要，因为这些层中的神经元投射到高阶丘脑核，随后将声信号传输到杏仁核和非初级听觉皮层；前脑回路对于包括人类在内的多种哺乳动物的发声编码非常重要。然而，由于通过传统方法从 IC 浅层神经元进行记录的技术困难，壳 IC 神经元传输辨别发声所需的声学特征的程度尚不清楚。在这里，我们在任一性别的小鼠中使用 2 光子 Ca ²⁺成像来测试 shell IC 神经元群体如何编码幅度调制的速率和深度，这是语音感知的重要声音线索。大多数壳 IC 神经元都经过广泛调谐，对调幅率的神经测量辨别力较低；只有一个子集对特定调制速率具有高度选择性。尽管如此，根据来自 shell IC 神经元群的荧光数据训练的神经网络分类器可以准确地分类幅度调制率，并且当从训练数据中省略高度调谐的神经元时，解码精度仅略有降低。相反，分类器精度随着训练数据的调制深度单调增加，使得在全深度调制声音上训练的分类器的中值解码误差约为 0.2 倍频程。因此，壳 IC 神经元可以通过群体代码传输时变信号，对任何单个神经元的辨别能力的依赖可能有限。