Escabí Monty A, Read Heather L
Biomedical Engineering Program, University of Connecticut, Electrical and Computer Engineering, 371 Fairfield Road U-1157, Storrs, CT 06269-2157, USA.
Biol Cybern. 2003 Nov;89(5):350-62. doi: 10.1007/s00422-003-0440-8. Epub 2003 Dec 4.
The representation of sound information in the central nervous system relies on the analysis of time-varying features in communication and other environmental sounds. How are auditory physiologists and theoreticians to choose an appropriate method for characterizing spectral and temporal acoustic feature representations in single neurons and neural populations? A brief survey of currently available scientific methods and their potential usefulness is given, with a focus on the strengths and weaknesses of using noise analysis techniques for approximating spectrotemporal response fields (STRFs). Noise analysis has been used to foster several conceptual advances in describing neural acoustic feature representation in a variety of species and auditory nuclei. STRFs have been used to quantitatively assess spectral and temporal transformations across mutually connected auditory nuclei, to identify neuronal interactions between spectral and temporal sound dimensions, and to compare linear vs. nonlinear response properties through state-dependent comparisons. We propose that noise analysis techniques used in combination with novel stimulus paradigms and parametric experiment designs will provide powerful means of exploring acoustic feature representations in the central nervous system.
中枢神经系统中声音信息的表征依赖于对交流声和其他环境声音中时变特征的分析。听觉生理学家和理论家应如何选择一种合适的方法来表征单个神经元和神经群体中的频谱和时间声学特征表征呢？本文简要介绍了目前可用的科学方法及其潜在用途，重点关注使用噪声分析技术来近似频谱时间响应场（STRF）的优缺点。噪声分析已被用于推动在描述各种物种和听觉核团中的神经声学特征表征方面的多项概念进展。STRF已被用于定量评估相互连接的听觉核团之间的频谱和时间转换，识别频谱和时间声音维度之间的神经元相互作用，并通过状态依赖比较来比较线性与非线性响应特性。我们认为，将噪声分析技术与新颖的刺激范式和参数实验设计相结合，将为探索中枢神经系统中的声学特征表征提供强大的手段。
