Clemens Jan, Ozeri-Engelhard Nofar, Murthy Mala
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08540, USA.
European Neuroscience Institute, Grisebachstrasse 5, 37077, Göttingen, Germany.
Nat Commun. 2018 Jan 9;9(1):134. doi: 10.1038/s41467-017-02453-9.
To faithfully encode complex stimuli, sensory neurons should correct, via adaptation, for stimulus properties that corrupt pattern recognition. Here we investigate sound intensity adaptation in the Drosophila auditory system, which is largely devoted to processing courtship song. Mechanosensory neurons (JONs) in the antenna are sensitive not only to sound-induced antennal vibrations, but also to wind or gravity, which affect the antenna's mean position. Song pattern recognition, therefore, requires adaptation to antennal position (stimulus mean) in addition to sound intensity (stimulus variance). We discover fast variance adaptation in Drosophila JONs, which corrects for background noise over the behaviorally relevant intensity range. We determine where mean and variance adaptation arises and how they interact. A computational model explains our results using a sequence of subtractive and divisive adaptation modules, interleaved by rectification. These results lay the foundation for identifying the molecular and biophysical implementation of adaptation to the statistics of natural sensory stimuli.
为了忠实地编码复杂刺激，感觉神经元应该通过适应性来校正那些破坏模式识别的刺激特性。在这里，我们研究果蝇听觉系统中的声音强度适应性，该系统主要致力于处理求偶歌曲。触角中的机械感觉神经元（JONs）不仅对声音引起的触角振动敏感，而且对影响触角平均位置的风或重力也敏感。因此，歌曲模式识别除了需要适应声音强度（刺激方差）外，还需要适应触角位置（刺激均值）。我们在果蝇JONs中发现了快速的方差适应性，它能在行为相关的强度范围内校正背景噪声。我们确定了均值和方差适应性产生的位置以及它们如何相互作用。一个计算模型使用一系列减法和除法适应性模块，并通过整流进行交错，来解释我们的结果。这些结果为识别适应自然感觉刺激统计特性的分子和生物物理机制奠定了基础。
