Predictive Computational Models of Olfactory Networks

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SUMMARY (Project 5) The nature of perceptual objects and of the neuronal mechanisms leading to their representation in the brain is one of the fundamental questions in neuroscience. Do representations of perceptual objects populate spaces of low dimensionality or do they mirror the complexity of the stimulus space? What features of the stimulus are represented by the dimensions of the perceptual space? How can objects represented in the brain retain invariance with respect to variations in stimulus features, timing, and background? Despite substantial progress in our understanding of the molecular basis of the sense of smell, for the olfactory system, these questions remain unanswered. In the eye, for example, the responses of the three types of cone photoreceptors correspond to the three dimensions sensed by human color vision. Understanding the low dimensional nature of color space was fundamental to our understanding of color vision. In the olfactory system, a similar conceptual understanding is missing. This project is a part of synergistic effort to understand the nature of olfactory coding. Based on experimental datasets collected by other projects of the same U19 program as well as publically available datasets, we will study the structure of the spaces of olfactory stimuli, responses of olfactory neurons, and perceptual qualities, build a neural network model that establishes connections between spaces, and resolve conceptual questions to make this network biologically realistic. Using state-of-the-art machine learning approaches, we will generate a predictive computational model of the olfactory system as a deliverable. Our goal is to develop, implement in a computational model, and test at least two theoretical ideas about the nature of olfactory code. First, we will test the hypothesis that olfactory spaces contain substantially fewer dimensions than the number of types of odorant receptors (OR). Our preliminary data indicates that the number of principal dimensions may be as low at 10, compared to ~103 of OR types. We will define these dimensions mathematically and relate them to the molecular properties of odorants. Second, we will test the primacy coding hypothesis, according to which identities of a small cohort of the most sensitive olfactory receptor types represent odorant identity in a concentration-invariant manner. Such representations render odor objects robust to noise. Our computational/theoretical studies will be carried out in close collaboration with the experimental groups. Our project includes three Specific Aims: Aim 1: To build predictive computational models for spaces of olfactory stimuli, responses, and percepts; Aim 2: To develop a predictive network model for mapping between olfactory spaces; and Aim 3: To build biologically realistic models of olfactory networks. Since representation of sensory objects is a fundamental problem in neuroscience, mathematical principles uncovered by our studies will elucidate the principles of sensory representation in other sensory modalities.