Neuroinformatic Analysis of Olfactory Coding

嗅觉编码的神经信息学分析

• 批准号: 7068069
• 负责人: Michael Leon
• 金额: $ 25.73万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7068069
• 关键词: artificial intelligence; automated data processing; bioinformatics; chemoreceptors; computer program /software; computer system design /evaluation; data collection methodology /evaluation; image processing; information retrieval; laboratory mouse; laboratory rat; mathematical model; meta analysis; neural information processing; nucleic acid sequence; olfactions; olfactory lobe; olfactory stimulus; respiratory epithelium; sensory mechanism; stimulus /response

DESCRIPTION (provided by applicant): Our goal is to use neuroinformatics to help resolve the conflicting findings from which two models of olfactory coding have emerged. One model proposes that very many low-specificity neural responses represent each odorant and the other model suggests that fewer, more specific olfactory receptors bind to particular molecular features and that the combination of these specific responses characterizes each odorant. Since much of the data supporting the low-specificity model has been collected without regard for the exquisite spatial heterogeneity of the olfactory system, it is possible that the differences in conclusions could be resolved if the distinct types of data that are collected by various laboratories were placed into spatial register with one another. To that end, we have been building an archive of the spatial patterns of glomerular responses evoked by a wide range of odorants, and we have been able to test hypotheses regarding strategies of olfactory coding by calculating homologies across glomerular-layer response patterns. To facilitate our analytical task, and to make it feasible for others to place their data in register with this odorant response archive, we propose to continue to develop analytical and visualization software for olfactory bulb data. We also propose to extend this approach to both the olfactory epithelium and olfactory cortex to be able to understand both the initial coding and synthetic levels of the olfactory system. These efforts will be freely available via the web site on which our olfactory activity archive is posted. We propose to improve the site by incorporating meta-data, as well as data from labs using other species and other types of data, such as lesions and neurophysiological data that can be located in space. Finally, the wide range of odorants that we must test to capture a sense of the system also will necessitate the use of an informatics approach to allow us to test hypotheses regarding the complex means by which chemical structure is represented in the system. The combination of these approaches should help resolve the differences between the conflicting models of olfactory coding.

描述（由申请人提供）：我们的目标是使用神经信息学来帮助解决相互矛盾的发现，从中出现了两种嗅觉编码模型。一个模型提出，许多低特异性神经反应代表了每种气味，另一个模型表明，更少，更具体的嗅觉受体与特定的分子特征结合，并且这些特定响应的组合表征了每种气味。由于已经收集了许多支持低特异性模型的数据，而不必考虑嗅觉系统的精美空间异质性，因此如果将各种实验室收集的不同类型的数据置于空间寄存器中，则可以解决结论的差异。为此，我们一直在建立一个由多种气味剂引起的肾小球响应的空间模式的存档，并且我们能够通过计算跨肾小球层响应模式的同源性来测试有关嗅觉编码策略的假设。为了促进我们的分析任务，并使其他人在此气味响应存档中的注册中可行，我们建议继续为嗅球数据开发分析和可视化软件。我们还建议将这种方法扩展到嗅觉上皮和嗅觉皮层，以便能够理解嗅觉系统的初始编码和合成水平。 这些努力将通过我们的嗅觉活动存档的网站免费获得。我们建议使用其他物种和其他类型的数据，例如可以位于太空中的病变和神经生理学数据，通过合并元数据以及实验室的数据来改善站点。最后，我们必须测试的广泛的气味剂量以捕获系统的感觉，还需要使用信息学方法，以使我们能够测试有关系统中化学结构的复杂方式的假设。这些方法的组合应有助于解决嗅觉编码模型之间的差异。