听觉流产生于快速兴奋和缓慢延迟抑制。

IF 2.3 4区 医学 Q1 Neuroscience
Andrea Ferrario, James Rankin
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引用次数: 3

摘要

在听觉流范式中,纯音的交替序列可以被理解为一个单一的飞驰节奏(整合)或两个低音调和高音调分离的序列(分离)。尽管研究了几十年,强调这种声音感知分组的神经机制仍然是一个谜。为了确定捕获这种现象的合理的最小神经回路,我们提出了一个具有两个周期性强迫神经群的放电率模型,该模型由快速直接激励和缓慢延迟抑制耦合。通过分析非光滑、慢快状态下的模型,我们解析地证明了丰富的动态状态库及其参数相关跃迁的存在性。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,缓慢的抑制掩盖了对后续音调的感知(前向掩蔽),而快速的激发能够整合两个音调之间的大音高差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Auditory streaming emerges from fast excitation and slow delayed inhibition.

Auditory streaming emerges from fast excitation and slow delayed inhibition.

Auditory streaming emerges from fast excitation and slow delayed inhibition.

Auditory streaming emerges from fast excitation and slow delayed inhibition.

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.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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