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嗅不变气味编码。

Sniff Invariant Odor Coding.

机构信息

Sagol Department of Neurobiology, University of Haifa, Haifa 3498838, Israel.

Institute of Neuroscience, University of Oregon, Eugene, OR 97403.

出版信息

eNeuro. 2018 Dec 26;5(6). doi: 10.1523/ENEURO.0149-18.2018. eCollection 2018 Nov-Dec.

Abstract

Sampling regulates stimulus intensity and temporal dynamics at the sense organ. Despite variations in sampling behavior, animals must make veridical perceptual judgments about external stimuli. In olfaction, odor sampling varies with respiration, which influences neural responses at the olfactory periphery. Nevertheless, rats were able to perform fine odor intensity judgments despite variations in sniff kinetics. To identify the features of neural activity supporting stable intensity perception, in awake mice we measured responses of mitral/tufted (MT) cells to different odors and concentrations across a range of sniff frequencies. Amplitude and latency of the MT cells' responses vary with sniff duration. A fluid dynamics (FD) model based on odor concentration kinetics in the intranasal cavity can account for this variability. Eliminating sniff waveform dependence of MT cell responses using the FD model allows for significantly better decoding of concentration. This suggests potential schemes for sniff waveform invariant odor concentration coding.

摘要

采样调节感觉器官的刺激强度和时间动态。尽管采样行为存在差异,但动物必须对外部刺激做出真实的感知判断。在嗅觉中,气味采样随呼吸而变化,这会影响嗅觉外围的神经反应。然而,老鼠能够在嗅探动力学变化的情况下进行精细的气味强度判断。为了确定支持稳定强度感知的神经活动特征,在清醒的小鼠中,我们测量了嗅球/丛(MT)细胞对不同气味和浓度在一系列嗅探频率下的反应。嗅球/丛(MT)细胞的反应幅度和潜伏期随嗅探持续时间而变化。基于鼻内腔中气味浓度动力学的流体动力学(FD)模型可以解释这种可变性。使用 FD 模型消除嗅球/丛(MT)细胞反应对嗅探波形的依赖性,可以显著提高浓度的解码能力。这表明了嗅探波形不变的气味浓度编码的潜在方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/641c/6325545/bb8fc5f395eb/enu0061828060001.jpg

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