通过先天机制学习空间听力。

IF 3.6 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yang Chu, Wayne Luk, Dan F M Goodman
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引用次数: 0

摘要

人类和其他动物用来定位声音的声音线索是微妙的,并且在我们的一生中都会发生变化。这意味着我们需要不断地重新学习或重新校准我们的声音定位电路。这通常被认为是一个“监督”的学习过程,其中“老师”(例如,父母或你的视觉系统)告诉你你是否猜对了位置,然后你使用这些信息来更新你的定位器。然而,并不总是有一个明显的老师(例如婴儿或盲人)。使用计算模型,我们发现来自简单先天回路的近似反馈,例如可以区分左右的回路(例如听觉定向响应),足以学习准确的全范围声音定位器。此外,将该机制与监督学习相结合,可以更稳健地保持自适应神经表征。我们发现了几种可能的神经机制,可以作为这种学习的基础,并假设可能存在多种机制,并提供了这些机制可以相互作用的例子。我们的结论是,在研究空间听觉时,我们不应该假设学习的唯一来源是视觉系统或其他监督信号。对所提出的机制的进一步研究可以使我们设计更好的康复方案,以加速空间听力的再学习/重新校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning spatial hearing via innate mechanisms.

The acoustic cues used by humans and other animals to localise sounds are subtle, and change throughout our lifetime. This means that we need to constantly relearn or recalibrate our sound localisation circuit. This is often thought of as a "supervised" learning process where a "teacher" (for example, a parent, or your visual system) tells you whether or not you guessed the location correctly, and you use this information to update your localiser. However, there is not always an obvious teacher (for example in babies or blind people). Using computational models, we showed that approximate feedback from a simple innate circuit, such as that can distinguish left from right (e.g. the auditory orienting response), is sufficient to learn an accurate full-range sound localiser. Moreover, using this mechanism in addition to supervised learning can more robustly maintain the adaptive neural representation. We find several possible neural mechanisms that could underlie this type of learning, and hypothesise that multiple mechanisms may be present and provide examples in which these mechanisms can interact with each other. We conclude that when studying spatial hearing, we should not assume that the only source of learning is from the visual system or other supervisory signals. Further study of the proposed mechanisms could allow us to design better rehabilitation programmes to accelerate relearning/recalibration of spatial hearing.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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