Effects of spectral and temporal disruption on cortical encoding of gerbil vocalizations.
Effects of spectral and temporal disruption on cortical encoding of gerbil vocalizations.
复制标题
频谱和时间干扰对沙鼠发声皮质编码的影响。
DOI：
10.1152/jn.00645.2012
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发表时间：
2013
影响因子：
2.5
通讯作者：
Sanes,DanH
中科院分区：
文献类型：
--
作者：
Ter-Mikaelian,Maria;Semple,MalcolmN;Sanes,DanH
动物交流的声音包含频谱时间波动，为检测和辨别提供了有力的线索。人的语音感知受频谱和时间声学特征的影响，但最关键的是依赖于包络信息。为了研究通信声音感知的神经编码原理，我们探讨了五种不同沙鼠呼叫类型的频谱或时间内容对清醒沙鼠初级听觉皮层（AI）神经反应的影响。通过减少到4或16个带通噪声通道，使发声在频谱上变得贫乏。对于这种声学操纵，神经元的平均放电率没有携带足够的信息来区分呼叫类型。相比之下，单个AI神经元的放电模式可靠地将仅由四个光谱带组成的发声与适当的自然标记进行分类。人工智能细胞的小群体的汇总响应将光谱中断和自然呼叫分类，其准确性超过了人类在类似语音任务中的表现。为了评估放电模式是否对单个呼叫的时间扰动具有鲁棒性，通过可变持续时间的时间反转段破坏发声。对于这种声学操作，皮层神经元相对不敏感短反转长度。与人类对语音的感知一致，这些结果表明，AI中通信声音的稳定表示更依赖于对慢速时间包络的敏感性，而不是频谱细节。
Animal communication sounds contain spectrotemporal fluctuations that provide powerful cues for detection and discrimination. Human perception of speech is influenced both by spectral and temporal acoustic features but is most critically dependent on envelope information. To investigate the neural coding principles underlying the perception of communication sounds, we explored the effect of disrupting the spectral or temporal content of five different gerbil call types on neural responses in the awake gerbil's primary auditory cortex (AI). The vocalizations were impoverished spectrally by reduction to 4 or 16 channels of band-passed noise. For this acoustic manipulation, an average firing rate of the neuron did not carry sufficient information to distinguish between call types. In contrast, the discharge patterns of individual AI neurons reliably categorized vocalizations composed of only four spectral bands with the appropriate natural token. The pooled responses of small populations of AI cells classified spectrally disrupted and natural calls with an accuracy that paralleled human performance on an analogous speech task. To assess whether discharge pattern was robust to temporal perturbations of an individual call, vocalizations were disrupted by time-reversing segments of variable duration. For this acoustic manipulation, cortical neurons were relatively insensitive to short reversal lengths. Consistent with human perception of speech, these results indicate that the stable representation of communication sounds in AI is more dependent on sensitivity to slow temporal envelopes than on spectral detail.