Computational mechanisms in the olfactory bulb

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In sensory systems, much of the task of extracting useful information about stimuli of interest falls to the neural circuitry downstream from the primary receptor complement. It is critical to understand the physiological mechanisms by which these postsensory neural circuits receive and process incoming sensory information in order to properly interpret secondary neural signals, such as the responses of mitral cells within the olfactory bulb to odor stimuli. As a well-described and delimited neural network close to the sensory periphery, the olfactory bulb is a clear candidate structure within which to develop an integrated understanding of neural coding and information processing across levels of analysis, from intracellular cascades and membrane properties through their behavioral consequences. Such a vertically integrated understanding of neural processing mechanics will be invaluable for the development of clinical psychiatric applications in which gene therapeutic or pharmacological agents must be specifically delivered to appropriate effector sites in order to effect the intended,systemic or behavioral changes. Physiologically constrained computational modeling of olfactory bulb neural circuitry, the subject of this proposal, is an important tool for achieving an integrated understanding of systemic function. Briefly, well-designed and constrained models enable exploration of the capabilities of multivariate systems that are too complex to be immediately intuitive. The specific aims of this proposal concern a set of models of the olfactory bulb glomerular layer based on the "non-topographical contrast enhancement" principle. The first aim is to implement this model mechanism in a full-scale network simulation (incorporating up to 2000 glomeruli) and challenge its predictions with glomerular imaging data, while the second is to develop a series of cellular compartmental models of glomerular layer olfactory bulb neurons (mitral, external tufted, and periglomerular cells) that can accurately reflect their intrinsic dynamical and pharmacological properties. The long-term goal of this project is to merge these two threads into a unified network model of the olfactory bulb containing sufficient cellular detail to enable the quantitative integration of existing data from different levels of analysis: for example, to interpret the effects of bulbar neuromodulators on membrane properties, field phenomena, and behavior. Quantitative, integrative results such as these are unlikely to be achieved without the use of computational modeling.