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

被引:16
作者
David, Stephen V. [1 ]
机构
[1] Oregon Hlth & Sci Univ, Oregon Hearing Res Ctr, 3181 SW Sam Jackson Pk Rd,MC L335A, Portland, OR 97239 USA
关键词
SPATIOTEMPORAL RECEPTIVE-FIELDS; CORTICAL MAP REORGANIZATION; TASK-RELATED PLASTICITY; SELECTIVE ATTENTION; REVERSE CORRELATION; NEURAL ADAPTATION; SOUND FEATURES; NATURAL SOUNDS; HEARING-LOSS; CORTEX;
D O I
10.1016/j.heares.2017.12.021
中图分类号
R36 [病理学]; R76 [耳鼻咽喉科学];
学科分类号
100104 ; 100213 ;
摘要
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. (C) 2017 The Author. Published by Elsevier B.V.
引用
收藏
页码:107 / 123
页数:17
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