Strategies for encoding odor information

气味信息编码策略

• 批准号: 8260194
• 负责人: Barry W. ACHE
• 金额: $ 21.98万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-26 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8260194
• 关键词: Animal Model; Characteristics; Code; Complex; Computer Simulation; Detection; Functional disorder; Human; Literature; Odors; Olfactory Receptor Neurons; Optics; Output; Perception; Peripheral; Physiological; Process; Relative (related person); Research; Signal Transduction; Structure; System; Testing; design; relating to nervous system; research study; spatiotemporal

DESCRIPTION (provided by applicant): Odor information is generally assumed to be encoded by olfactory receptor neurons (ORNs) in which odorants evoke a tonic discharge. Exciting new evidence extends hints in the earlier literature that some ORNs are not tonically active, but rather are inherently rhythmically active or 'bursting' ORNs (bORNs) in which the rhythmic bursting is entrained by odorants. bORNs open up an entirely new possibility for encoding odor information, in particular information relative to the spatiotemporal characteristics of the odor signal. Experiments are proposed using an animal model in which both tonically active and bORNs are well characterized physiologically to compare the potential of both types of ORNs to encode odor information. This will be done by first using computational modeling to test the hypothesis that synchronization of large ensembles of bORNs selectively favors (1) the detection and amplification of weak odor signals and (2) the detection and coding of the temporal characteristics of the odor signal itself compared to tonically active ORNs. Combined optical and electrophysiological recording from small ensembles of actual ORNs will then be used to experimentally test the predictions of the computational model. This project has the capacity to identify a heretofore unappreciated way in which olfactory information is encoded. PUBLIC HEALTH RELEVANCE: This project has the potential to derive a heretofore unappreciated principle of olfactory information encoding and processing that can be used not only to better understand olfactory dysfunction in humans but also to better design artificial systems capable of analyzing and localizing complex chemosensory signals.

描述（由申请人提供）：气味信息通常被认为是由嗅觉受体神经元（orn）编码的，在嗅觉受体神经元中，气味会引起强直放电。令人兴奋的新证据扩展了早期文献中的暗示，即一些orn不具有音调活性，而是具有固有的节奏活性或“爆发”orn (born)，其中有节奏的爆发是由气味引起的。伯恩斯为气味信息的编码开辟了一种全新的可能性，特别是与气味信号的时空特征相关的信息。实验建议使用一种动物模型，其中滋补活性和伯恩斯都具有良好的生理特征，以比较两种类型的伯恩斯编码气味信息的潜力。这将通过首先使用计算建模来验证假设，即与音调活跃的气味气味相比，大型气味气味集合的同步选择性地有利于(1)微弱气味信号的检测和放大，以及(2)气味信号本身的时间特征的检测和编码。结合光学和电生理记录，从实际的小集合的orn将被用于实验测试计算模型的预测。这个项目有能力识别一种迄今为止未被认识到的嗅觉信息编码方式。