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将行为和感官背景纳入听觉编码的频谱-时间模型。

Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding.

作者信息

David Stephen V

机构信息

Oregon Hearing Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, MC L335A, Portland, OR 97239, United States.

出版信息

Hear Res. 2018 Mar;360:107-123. doi: 10.1016/j.heares.2017.12.021. Epub 2017 Dec 31.

DOI:10.1016/j.heares.2017.12.021
PMID:29331232
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6292525/
Abstract

For several decades, auditory neuroscientists have used spectro-temporal encoding models to understand how neurons in the auditory system represent sound. Derived from early applications of systems identification tools to the auditory periphery, the spectro-temporal receptive field (STRF) and more sophisticated variants have emerged as an efficient means of characterizing representation throughout the auditory system. Most of these encoding models describe neurons as static sensory filters. However, auditory neural coding is not static. Sensory context, reflecting the acoustic environment, and behavioral context, reflecting the internal state of the listener, can both influence sound-evoked activity, particularly in central auditory areas. This review explores recent efforts to integrate context into spectro-temporal encoding models. It begins with a brief tutorial on the basics of estimating and interpreting STRFs. Then it describes three recent studies that have characterized contextual effects on STRFs, emerging over a range of timescales, from many minutes to tens of milliseconds. An important theme of this work is not simply that context influences auditory coding, but also that contextual effects span a large continuum of internal states. The added complexity of these context-dependent models introduces new experimental and theoretical challenges that must be addressed in order to be used effectively. Several new methodological advances promise to address these limitations and allow the development of more comprehensive context-dependent models in the future.

摘要

几十年来,听觉神经科学家一直使用频谱 - 时间编码模型来理解听觉系统中的神经元如何表征声音。从系统识别工具在听觉外周的早期应用衍生而来,频谱 - 时间感受野(STRF)以及更复杂的变体已成为表征整个听觉系统中表征的有效手段。这些编码模型大多将神经元描述为静态的感觉滤波器。然而,听觉神经编码并非静态。反映声学环境的感觉背景和反映听众内部状态的行为背景,都可以影响声音诱发的活动,特别是在中枢听觉区域。这篇综述探讨了将背景整合到频谱 - 时间编码模型中的最新努力。它首先简要介绍了估计和解释STRF的基础知识。然后描述了三项最近的研究,这些研究表征了在从几分钟到几十毫秒的一系列时间尺度上出现的对STRF的背景效应。这项工作的一个重要主题不仅是背景影响听觉编码,而且背景效应跨越了很大范围的内部状态。这些依赖于背景的模型增加的复杂性带来了新的实验和理论挑战,为了有效使用这些模型必须加以解决。一些新的方法进展有望解决这些限制,并在未来允许开发更全面的依赖于背景的模型。

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