Our consortium will reveal the computational logic of olfaction by collecting the first system-wide datasets of neural and perceptual responses to a large, principled set of odorants, and by applying a unified statistical and theoretical approach to its interpretation. The proposed project capitalizes on emerging new technologies and on the complementary expertise of multiple experimental and theoretical groups.
Project 1: Peripheral Representation of Odor Space
Collaborators: Thomas Bozza
We propose a new framework in which the structure of olfactory space is defined by olfactory-receptor affinities. We will characterize this space by gathering the most comprehensive data sets yet obtained to define odorant receptor responses in vivo to a large, diverse odor set. These data will allow us to test our models and will catalyze progress in understanding odor coding.
Project 2: Transformation of Olfactory Information from Bulb to Cortex
Collaborators: Kevin Franks
Elemental representations of chemical space in the olfactory bulb are transformed into holistic odor representations in the cortex. Combining odorant delivery, patterned optogenetic bulb stimulation, targeted circuit perturbations, and cortical population recordings, we will reveal and dissect the transfer functions for odor information from periphery to cortex.
Project 3: Representation of Perceived Odor Intensity
Collaborators: Joel Mainland
Intensity is a universal feature of all sensory stimuli that must be encoded orthogonally to stimulus identity. Using human psychophysics, novel mouse behavioral paradigms, optogenetic and genetic manipulations, and cortical physiology, we will identify, for the first time, how specific neural coding features contribute to odorant intensity perception.
Project 4: The Structure of Olfactory Neural and Perceptual Spaces
Collaborators: Bob Datta
We lack a metric for odor quality that allows us to measure similarities among odors. Here we use high-throughput psychophysics to comprehensively define human and mouse odor perceptual spaces, and then use imaging approaches to systematically explore neural representations for odors, thereby revealing how the cortex defines and constrains perceptual space.
Project 5: Predictive Computational Models of Olfactory Networks
Collaborators: Alex Koulakov
Based on data collected across all projects, we will develop theories of olfactory processing, model the structure of the spaces of olfactory stimuli, neural responses, and perceptual qualities, and use a combination of theory with state-of-the-art machine learning approaches to generate a predictive biologically realistic computational model of the olfactory system.
Data Science Core:
Collaborators: Rick Gerkin
The Data Science Core is coordinating data integration across the five scientific projects; adopting technology for reproducibility, availability, and intelligibility of all findings; optimizing data collection using statistical models; and standardizing odorant stimuli across projects to ensure that current and future research efforts can be mutually informative.