Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California.
Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, California.
J Neurophysiol. 2021 Jul 1;126(1):148-169. doi: 10.1152/jn.00709.2020. Epub 2021 Jun 2.
Fluctuations in the amplitude envelope of complex sounds provide critical cues for hearing, particularly for speech and animal vocalizations. Responses to amplitude modulation (AM) in the ascending auditory pathway have chiefly been described for single neurons. How neural populations might collectively encode and represent information about AM remains poorly characterized, even in primary auditory cortex (A1). We modeled population responses to AM based on data recorded from A1 neurons in awake squirrel monkeys and evaluated how accurately single trial responses to modulation frequencies from 4 to 512 Hz could be decoded as functions of population size, composition, and correlation structure. We found that a population-based decoding model that simulated convergent, equally weighted inputs was highly accurate and remarkably robust to the inclusion of neurons that were individually poor decoders. By contrast, average rate codes based on convergence performed poorly; effective decoding using average rates was only possible when the responses of individual neurons were segregated, as in classical population decoding models using labeled lines. The relative effectiveness of dynamic rate coding in auditory cortex was explained by shared modulation phase preferences among cortical neurons, despite heterogeneity in rate-based modulation frequency tuning. Our results indicate significant population-based synchrony in primary auditory cortex and suggest that robust population coding of the sound envelope information present in animal vocalizations and speech can be reliably achieved even with indiscriminate pooling of cortical responses. These findings highlight the importance of firing rate dynamics in population-based sensory coding. Fundamental questions remain about population coding in primary auditory cortex (A1). In particular, issues of spike timing in models of neural populations have been largely ignored. We find that spike-timing in response to sound envelope fluctuations is highly similar across neuron populations in A1. This property of shared envelope phase preference allows for a simple population model involving unweighted convergence of neuronal responses to classify amplitude modulation frequencies with high accuracy.
复杂声音幅度包络的波动为听觉提供了关键线索，尤其是对于言语和动物叫声。在听觉上行通路中，对幅度调制（AM）的反应主要是针对单个神经元进行描述的。即使在初级听觉皮层（A1）中，关于神经群体如何集体编码和表示 AM 信息的问题仍未得到很好的描述。我们根据在清醒的松鼠猴的 A1 神经元中记录的数据来模拟群体对 AM 的反应，并评估了对调制频率为 4 到 512 Hz 的单试次反应的解码精度，这些频率是作为群体大小、组成和相关结构的函数。我们发现，基于模拟会聚、加权相等输入的群体解码模型具有高度的准确性，并且对包括个体解码能力较差的神经元在内的准确性具有很强的鲁棒性。相比之下，基于会聚的平均率码表现不佳；只有当单个神经元的反应被分离时，例如在使用标记线的经典群体解码模型中，才能使用平均率有效地进行解码。尽管在基于率的调制频率调谐方面存在异质性，但听觉皮层中动态率编码的相对有效性可以用皮层神经元之间共享调制相位偏好来解释。我们的结果表明，初级听觉皮层中存在显著的群体基础同步性，并表明即使对皮质反应进行不加区分的汇集，也可以可靠地实现对动物叫声和言语中存在的声音包络信息的强大群体编码。这些发现强调了在群体感觉编码中放电率动态的重要性。在初级听觉皮层（A1）中，群体编码仍然存在许多基本问题。特别是，在神经群体模型中的尖峰时间问题在很大程度上被忽视了。我们发现，A1 中神经元群体对声音包络波动的反应尖峰时间高度相似。这种共享包络相位偏好的特性允许使用涉及神经元反应的无权重会聚的简单群体模型，以高精度对幅度调制频率进行分类。
