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习惯化作为一种在线气味辨别神经算法。

Habituation as a neural algorithm for online odor discrimination.

机构信息

Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724.

Computer Science and Engineering Department, University of California San Diego, La Jolla, CA 92093.

出版信息

Proc Natl Acad Sci U S A. 2020 Jun 2;117(22):12402-12410. doi: 10.1073/pnas.1915252117. Epub 2020 May 19.

Abstract

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

摘要

习惯化是一种简单的记忆形式,它抑制了对重复、中性刺激的神经活动。这个过程对于帮助生物将注意力引导到环境中最显著和新颖的特征是至关重要的。在这里,我们遵循果蝇嗅觉系统中已知的回路机制,推导出一种简单的习惯化算法。我们通过经验和分析表明,该算法能够滤除冗余信息,增强对共享相似背景的气味的区分能力,并提高对混合气味中新颖成分的检测能力。总的来说,我们提出了一种关于习惯化生物机制的算法观点,并利用这一观点来理解感觉生理学如何影响气味感知。我们的框架也可能有助于理解习惯化在其他更复杂的神经系统中的影响。

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