Suppr超能文献

听觉流分离源于快速兴奋和缓慢延迟抑制。

Auditory streaming emerges from fast excitation and slow delayed inhibition.

作者信息

Ferrario Andrea, Rankin James

机构信息

Department of Mathematics, College of Engineering, Mathematics & Physical Sciences, University of Exeter, Exeter, UK.

出版信息

J Math Neurosci. 2021 May 3;11(1):8. doi: 10.1186/s13408-021-00106-2.

Abstract

In the auditory streaming paradigm, alternating sequences of pure tones can be perceived as a single galloping rhythm (integration) or as two sequences with separated low and high tones (segregation). Although studied for decades, the neural mechanisms underlining this perceptual grouping of sound remains a mystery. With the aim of identifying a plausible minimal neural circuit that captures this phenomenon, we propose a firing rate model with two periodically forced neural populations coupled by fast direct excitation and slow delayed inhibition. By analyzing the model in a non-smooth, slow-fast regime we analytically prove the existence of a rich repertoire of dynamical states and of their parameter dependent transitions. We impose plausible parameter restrictions and link all states with perceptual interpretations. Regions of stimulus parameters occupied by states linked with each percept match those found in behavioural experiments. Our model suggests that slow inhibition masks the perception of subsequent tones during segregation (forward masking), whereas fast excitation enables integration for large pitch differences between the two tones.

摘要

在听觉流范式中,纯音的交替序列可以被感知为单一的奔腾节奏(整合),或者被感知为具有分离的低音和高音的两个序列(分离)。尽管已经研究了几十年,但这种声音感知分组背后的神经机制仍然是个谜。为了识别一个合理的最小神经回路来捕捉这一现象,我们提出了一个发放率模型,该模型有两个周期性受迫的神经群体,通过快速直接兴奋和缓慢延迟抑制相互耦合。通过在非光滑、快慢 regime 中分析该模型,我们解析地证明了存在丰富的动态状态库及其依赖参数的转变。我们施加合理的参数限制,并将所有状态与感知解释联系起来。与每个感知相关联的状态所占据的刺激参数区域与行为实验中发现的区域相匹配。我们的模型表明,在分离过程中(前向掩蔽),缓慢抑制掩盖了对后续音调的感知,而快速兴奋则使得在两个音调之间存在大音高差异时能够进行整合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d5b/8093365/c5927741a449/13408_2021_106_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验