Sen K, Theunissen F E, Doupe A J
Sloan Center for Theoretical Neuroscience, University of California, 513 Parnassus Ave., Berkeley, CA 94720-1650, USA.
J Neurophysiol. 2001 Sep;86(3):1445-58. doi: 10.1152/jn.2001.86.3.1445.
Although understanding the processing of natural sounds is an important goal in auditory neuroscience, relatively little is known about the neural coding of these sounds. Recently we demonstrated that the spectral temporal receptive field (STRF), a description of the stimulus-response function of auditory neurons, could be derived from responses to arbitrary ensembles of complex sounds including vocalizations. In this study, we use this method to investigate the auditory processing of natural sounds in the birdsong system. We obtain neural responses from several regions of the songbird auditory forebrain to a large ensemble of bird songs and use these data to calculate the STRFs, which are the best linear model of the spectral-temporal features of sound to which auditory neurons respond. We find that these neurons respond to a wide variety of features in songs ranging from simple tonal components to more complex spectral-temporal structures such as frequency sweeps and multi-peaked frequency stacks. We quantify spectral and temporal characteristics of these features by extracting several parameters from the STRFs. Moreover, we assess the linearity versus nonlinearity of encoding by quantifying the quality of the predictions of the neural responses to songs obtained using the STRFs. Our results reveal successively complex functional stages of song analysis by neurons in the auditory forebrain. When we map the properties of auditory forebrain neurons, as characterized by the STRF parameters, onto conventional anatomical subdivisions of the auditory forebrain, we find that although some properties are shared across different subregions, the distribution of several parameters is suggestive of hierarchical processing.
尽管理解自然声音的处理过程是听觉神经科学的一个重要目标，但我们对这些声音的神经编码了解相对较少。最近我们证明，频谱时间感受野（STRF），即听觉神经元刺激-反应函数的一种描述，可以从对包括发声在内的复杂声音任意集合的反应中推导出来。在本研究中，我们使用这种方法来研究鸣禽系统中自然声音的听觉处理。我们从鸣禽听觉前脑的几个区域获取对大量鸟鸣声集合的神经反应，并使用这些数据来计算STRF，STRF是听觉神经元所反应声音的频谱-时间特征的最佳线性模型。我们发现，这些神经元对歌曲中的各种特征都有反应，从简单的音调成分到更复杂的频谱-时间结构，如频率扫描和多峰频率叠加。我们通过从STRF中提取几个参数来量化这些特征的频谱和时间特征。此外，我们通过量化使用STRF获得的对歌曲神经反应预测的质量，来评估编码的线性与非线性。我们的结果揭示了听觉前脑神经元对歌曲分析的连续复杂功能阶段。当我们将以STRF参数为特征的听觉前脑神经元特性映射到听觉前脑的传统解剖细分上时，我们发现，尽管一些特性在不同子区域中是共享的，但几个参数的分布表明存在分层处理。
