CRCNS Data Sharing: Pyrfume: A library for mammalian olfactory psychophysics

Project: Research project

Project Details


CRCNS Data Sharing: Pyrfume: A library for mammalian olfactory psychophysics CRCNS Data Sharing: Pyrfume: A library for mammalian olfactory psychophysics Overview: Progress on a theory of olfactory perception is limited by obstacles to obtaining and using datasets reported in the academic literature or in industrial settings. This proposal creates a convenient entrypoint for olfactory psychophysics work by 1) curating numerous datasets spanning perceptual, behavioral, and neural responses to identified odorants in humans and animal models; and by 2) creating a framework for testing models according to their agreement with those datasets, permitting a frank assessment of the state of the field by revealing gaps in current understanding, and enabling model-guided experimental design. These tests will serve as benchmarks for new models to meet and exceed. This work disseminated using Python, web APIs, and a web application. Intellectual Merit: Olfaction is critical to the enjoyment of food, the avoidance of danger, emotional memory, and social interaction. Yet 150 years after Helmholz and Young first developed a good working theory of color vision, and 100 after years Alexander Graham Bell asked, Can you measure the difference between one kind of smell and another? we do not yet have a comparably adequate theory of olfactory perception. The design and interpretation of future olfactory neuroscience experiments would greatly benefit from a better understanding of olfactory psychophysics. What is the relationship between the physical properties of the stimulus and the percept it evokes? How do monomolecular odorants add to produce mixture perception? What is the size, shape, and structure of odor space? How does neural activation differ between odorants? There are many datasets and models probing these questions, and yet these are still all open questions in olfaction. By curating and aligning datasets, and facilitating the development of models to account for them, this proposal enables a wide range of specific hypotheses about olfactory perception to be tested against a wide range of experimental data. By illustrating the strengths, weaknesses, and blind spots in past research, the results of these tests can motivate, guide, and constrain the next generation of experimental and theoretical investigations into the olfactory system. Specific Aims are to 1a) Create a database containing all identifiable quantitative data on human olfactory psychophysics, a Python library for interacting with them, a website for exploring them, and a web API for querying the database remotely; 1b) Extend this resource by including neural and behavioral data from animal models that describe responses to large sets of identified odorants; 2) Create a testing infrastructure for olfactory models that assess each models performance on explaining features of the data curated in Aim 1, facilitating the development and evaluation of such models. Test results will be publicly accessible online and reproducible by any interested researcher. Broader Impacts: This project goes beyond model and data sharing by facilitating the dissemination of information about the performance and applicability of specific models in the context of a vast collection of specific datasets, complementing the existing dissemination mode of manuscript publication. By transparently illustrating the strengths and weakness of models, and the degree to which various datasets have been explained by those models, this work highlights the current state of the field without the need for a deep literature search, the installation of new tools, or continuous re-implementation of models. The project also serves neuroscience educators by providing an interactive platform for visualization of olfactory psychophysics research accessible to any student. This work broadly transforms olfactory perception research: by rigorously identifying the models best-suited for further research efforts and by helping experimentalists enhance the impact of their work. By leveraging the Kaggle platform, we also expect thousands of non-olfaction researchers to explore and model olfactory data. Optometry and audiology are refined clinical tools that identify specific perceptual deficits likely to map onto medical and surgical targets in the nervous system. In contrast, olfactometry is only sparsely used due to its relative lack of development. This work also provides a platform for the creation of a more clinically valuable olfactometry.
Effective start/end date8/1/197/31/23


  • HHS: National Institutes of Health (NIH): $435,426.00


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