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.
