Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston, Massachusetts 02114, Department of Otology and Laryngology, Harvard Medical School, Boston, Massachusetts 02115, and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.
J Neurosci. 2013 Oct 2;33(40):15837-47. doi: 10.1523/JNEUROSCI.2034-13.2013.
The strategies by which the central nervous system decodes the properties of sensory stimuli, such as sound source location, from the responses of a population of neurons are a matter of debate. We show, using the average firing rates of neurons in the inferior colliculus (IC) of awake rabbits, that prevailing decoding models of sound localization (summed population activity and the population vector) fail to localize sources accurately due to heterogeneity in azimuth tuning across the population. In contrast, a maximum-likelihood decoder operating on the pattern of activity across the population of neurons in one IC accurately localized sound sources in the contralateral hemifield, consistent with lesion studies, and did so with a precision consistent with rabbit psychophysical performance. The pattern decoder also predicts behavior in response to incongruent localization cues consistent with the long-standing "duplex" theory of sound localization. We further show that the pattern decoder accurately distinguishes two concurrent, spatially separated sources from a single source, consistent with human behavior. Decoder detection of small amounts of source separation directly in front is due to neural sensitivity to the interaural decorrelation of sound, at both low and high frequencies. The distinct patterns of IC activity between single and separated sound sources thereby provide a neural correlate for the ability to segregate and localize sources in everyday, multisource environments.
中枢神经系统如何从神经元群体的反应中解码感觉刺激（如声源位置）的特性，这是一个有争议的问题。我们使用清醒兔子下丘脑中神经元的平均发放率进行研究，结果表明，由于群体方位调谐的异质性，流行的声音定位解码模型（群体活动总和和群体向量）无法准确定位声源。相比之下，在一个下丘脑中的神经元群体活动模式上运行的最大似然解码器，准确地定位了对侧半视野中的声源，与损伤研究一致，并且其精度与兔子的心理物理表现一致。模式解码器还预测了与声音定位的长期“双工”理论一致的不相符定位线索的行为。我们进一步表明，模式解码器可以准确地区分两个同时发生的、空间上分开的声源和单个声源，这与人类的行为一致。解码器对直接位于前方的少量声源分离的检测归因于对声音的耳间去相关的神经敏感性，无论是在低频还是高频。单个和分离声源之间下丘脑中的明显活动模式为在日常多声源环境中分离和定位声源的能力提供了神经相关性。
