Strategies for encoding odor information

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，其中气味引起紧张性放电。令人兴奋的新证据扩展了早期文献中的提示，即一些ORN不是紧张性活性的，而是固有的节奏性活性或“爆发”ORN（bORN），其中节奏性爆发被气味剂夹带。BORN为编码气味信息，特别是与气味信号的时空特征相关的信息开辟了全新的可能性。实验中提出使用动物模型，其中两个紧张性活性和bORN的生理特征，以比较两种类型的ORN编码气味信息的潜力。这将通过首先使用计算建模来测试这样的假设来完成，即与紧张性活性ORN相比，bORN的大集合的同步选择性地有利于（1）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ORN的小合奏，然后将用于实验测试的计算模型的预测。这个项目有能力识别一种迄今为止未被认可的嗅觉信息编码方式。 公共卫生相关性：该项目有可能推导出一种迄今为止尚未得到重视的嗅觉信息编码和处理原理，不仅可以用于更好地理解人类的嗅觉功能障碍，而且可以更好地设计能够分析和定位复杂化学感觉信号的人工系统。

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.