Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding.
Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding.
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DOI：
10.1016/j.heares.2017.12.021
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发表时间：
2018-03
期刊：
影响因子：
2.8
通讯作者：
David SV
中科院分区：
文献类型：
--
作者：
David SV
几十年来，听觉神经科学家一直使用光谱-时间编码模型来理解听觉系统中的神经元是如何表达声音的。从早期系统识别工具在听觉外围的应用中衍生出来，光谱-时间接受野（STRF）和更复杂的变体已经成为表征整个听觉系统表征的有效手段。这些编码模型大多将神经元描述为静态感觉过滤器。然而，听觉神经编码不是静态的。反映声音环境的感觉环境和反映听者内部状态的行为环境都可以影响声音诱发的活动，特别是在中央听觉区域。这篇综述探讨了最近将上下文整合到光谱-时间编码模型中的努力。它以一个关于估计和解释strf的基础知识的简短教程开始。然后，它描述了最近的三项研究，这些研究描述了环境对strf的影响，这些影响在一系列时间尺度上出现，从几分钟到几十毫秒不等。这项工作的一个重要主题不仅仅是上下文影响听觉编码，而且上下文效应跨越内部状态的大连续体。这些与环境相关的模型的复杂性带来了新的实验和理论挑战，必须解决这些挑战才能有效地使用。一些新的方法进展有望解决这些限制，并允许在未来开发更全面的依赖于上下文的模型。
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.
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影响因子：
25
作者：
Baruni, Jalal K.;Lau, Brian;Salzman, C. Daniel
通讯作者：
Salzman, C. Daniel
影响因子：
3.5
作者：
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通讯作者：
Malmierca MS
DOI：
10.1523/jneurosci.5249-08.2009
发表时间：
2009-03-18
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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