Concentration invariance—the capacity to recognize a given odorant (analyte) across a range of concentrations—is an unusually difficult problem in the olfactory modality. Nevertheless, humans and other animals are able to recognize known odors across substantial concentration ranges, and this concentration invariance is a highly desirable property for artificial systems as well. Several properties of olfactory systems have been proposed to contribute to concentration invariance, but none of these alone can plausibly achieve full concentration invariance. We here propose that the mammalian olfactory system uses at least six computational mechanisms in series to reduce the concentration-dependent variance in odor representations to a level at which different concentrations of odors evoke reasonably similar representations, while preserving variance arising from differences in odor quality. We suggest that the residual variance then is treated like any other source of stimulus variance, and categorized appropriately into “odors” via perceptual learning. We further show that naïve mice respond to different concentrations of an odorant just as if they were differences in quality, suggesting that, prior to odor categorization, the learning-independent compensatory mechanisms are limited in their capacity to achieve concentration invariance.

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Many microorganisms such as bacteria and fungi possess so-called capsules made of polysaccharides which protect these microorganisms from environmental insults and host immune defenses. The polysaccharide capsule of Cryptococcus neoformans, a human pathogenic yeast, is capable of self-assembly, composed mostly of glucuronoxylomannan (GXM), a polysaccharide with a molecular weight of approximately 2,000,000, and has several layers with different densities. The objective of this study was to model pore-hindered diffusion and binding of the GXM-specific antibody within the C. neoformans capsule. Using the finite-element method (FEM), we created a model which represents the in vivo binding of a GXM-specific antibody to a C. neoformans cell taking into account the intravenous infusion time of antibody, antibody diffusion through capsular pores, and Michaelis-Menten kinetics of antibody binding to capsular GXM. The model predicted rapid diffusion of antibody to all regions of the capsule where the pore size was greater than the Stokes diameter of the antibody. Binding occurred primarily at intermediate regions of the capsule. The GXM concentration in each capsular region was the principal determinant of the steady-state antibody-GXM complex concentration, while the forward binding rate constant influenced the rate of complex formation in each region. The concentration profiles predicted by the model closely matched experimental immunofluorescence data. Inclusion of different antibody isotypes (IgG, IgA, and IgM) into the modeling algorithm resulted in similar complex formation in the outer capsular regions, but different depths of binding at the inner regions. These results have implications for the development of new antibody-based therapies.