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Background noise responding neurons in the inferior colliculus of the CF-FM bat, Hipposideros pratti

下丘 神经科学 脑干 噪音(视频) 听觉皮层 耳蜗核 抑制性突触后电位 生物 物理 核心 计算机科学 图像(数学) 人工智能
作者
Guimin Zhang,Zhongdan Cui,Zihui Fan,Lijian Yang,Yijun Jia,Qi‐Cai Chen,Zi-Ying Fu
出处
期刊:Hearing Research [Elsevier BV]
卷期号:432: 108742-108742
标识
DOI:10.1016/j.heares.2023.108742
摘要

The Lombard effect, referring to an involuntary rise in vocal intensity, is a widespread vertebrate mechanism that aims to maintain signal efficiency in response to ambient noise. Previous studies showed that the Lombard effect could be sufficiently implemented at subcortical levels and operated by continuously monitoring background noise, requiring some subcortical auditory sensitive neurons to have continuous responses to background noise. However, such neurons have not been well characterized. The inferior colliculus (IC) is a major auditory integration center under the auditory cortex and provides projections to the putative vocal pattern generator in the brainstem. Thus, it is reasonable to speculate that the IC is a likely auditory nucleus candidate having background noise responding neurons (BNR neurons). In the present study, we isolated 183 sound-sensitive IC neurons in a constant frequency-frequency modulation bat, Hipposideros pratti, and found that around 19% of these IC neurons are BNR neurons when stimulated with 70 dB SPL background white noise. Their firing rates in response to noise increased with increasing noise intensity and could be suppressed by sound stimulation. Furthermore, compared to neurons with similar best frequencies, the BNR neurons had smaller Q10-dB values and lower noise-induced minimal threshold change, indicating that BNR neurons received fewer inhibitory inputs. These results suggested that the BNR neurons are ideal candidates for collecting information about background noise. We proposed that the BNR neurons synapsed with neurons in vocal-pattern-generating networks in the brainstem and initiated the Lombard effect by a feed-forward loop.

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