Analysis of combinatorial olfactory coding

信息素气味剂的加工已经在所谓的标记线系统中发展，由此给定信息素共混物的单一气味剂结合到特定受体以及特定下游加工途径。信息素的气味编码问题的解决方案已经演变为响应特定的进化约束的配偶的位置和歧视的同种从那些密切相关的物种。在非信息素气味的情况下，嗅觉系统似乎已经进化出少量相对广泛调谐的受体，以及肾小球，通过以组合方式处理气味信息来编码大量短暂的气味，由此气味沿着沿着许多气味维度进行评估。事实上，就其本质而言，组合编码方案比标记线方案具有更大的气味辨别能力。然而，虽然它是越来越清楚的气味代码分布的气味维度的规格仍然是定义不清。该R03的具体目标旨在开发用于量化和分析非信息素气味的信号处理中的变化的方法，所述非信息素气味系统地沿着一组受限制的建议气味尺寸（例如碳链长度）变化，以及经验是否增强这些表示的统计显著性。目的1被设计为评估相关气味在时空重叠中被编码的程度，或者沿着沿着维度系统地变化的组合编码，所述维度编码这些气味的几何结构中的对应变化。在这里，我们将统计模型的时空相关性阵列的气味，系统地改变沿着指定的几何尺寸。研究还表明，学习可以以一些清晰且可重复的方式改变气味代码。然而，人们对这些变化所起的作用知之甚少。一种可能性是，这些变化增强了气味的可辨别性。目标2进一步发展目标1，通过观察这些重叠的时空气味代码的相关性如何随着飞蛾对食物相关气味的适应而变化。

Processing of pheromonal odorants has evolved in what is referred to as a labeled-line system, whereby single odorants of a given pheromonal blend bind to specific receptors as well as specific down stream processing pathways. The solution of the pheromonal odor-coding problem has evolved in response to specific evolutionary constrains of mate location and discrimination of conspecifics from those of closely related species. In the case of non-pheromonal odors, olfactory systems appear to have evolved with a small number of relatively broadly tuned receptors, as well as glomeruli, to code for a large number of ephemeral odors by processing odor information in a combinatorial manner whereby odors are assessed along a number of odor dimensions. Indeed, by their very nature, combinatorial coding schemes have a far greater capacity for odor discrimination than do labeled line schemes. However, while it is becoming increasingly clear that odor codes are distributed the specification of odor dimensionality is still poorly defined. The specific aims of this R03 are designed to develop methods for quantifying and analyzing variation in signal processing of non-pheromonal odors that vary systematically along a restricted set of proposed odor dimensions, such as carbon chain length, and, whether experience enhances the statistical distinctiveness of these representations. Aim 1 is designed to assess the degrees to which related odors are coded for in spatiotemporal overlapping, or combinatorial codes that vary systematically along dimensions that codify corresponding variation in the geometry of these odors. Here we will statistically model the spatiotemporal relatedness of arrays of odors that systematically vary along specified geometric dimensions. It has also been demonstrated that learning changes the odor code in some clear and reproducible ways. Yet the role these changes play are poorly understood. One likely possibility is that these changes enhance the discriminability of odors. Aim 2 further develops Aim 1 by looking at how the relatedness of these overlapping spatiotemporal odor codes changes as moths are conditioned to related odors to food.