Abstract
We describe an integrated theory of olfactory systems operation that incorporates experimental findings across scales, stages, and methods of analysis into a common framework. In particular, we consider the multiple stages of olfactory signal processing as a collective system, in which each stage samples selectively from its antecedents. We propose that, following the signal conditioning operations of the nasal epithelium and glomerular-layer circuitry, the plastic external plexiform layer of the olfactory bulb effects a process of category learning—the basis for extracting meaningful, quasi-discrete odor representations from the metric space of undifferentiated olfactory quality. Moreover, this early categorization process also resolves the foundational problem of how odors of interest can be recognized in the presence of strong competitive interference from simultaneously encountered background odorants. This problem is fundamentally constraining on early-stage olfactory encoding strategies and must be resolved if these strategies and their underlying mechanisms are to be understood. Multiscale general theories of olfactory systems operation are essential in order to leverage the analytical advantages of engineered approaches together with our expanding capacity to interrogate biological systems.
Highlights
Theoretical models of neural systems serve as proofs of concept, tests of sufficiency, and quantitative embodiments of working hypotheses (Cleland and Linster, 2005)
It is axiomatic in engineering that new designs are likely to require revision once physical prototypes are built, often because neglected minor variables generate significant unforeseen effects. This same principle applies to the reverse engineering of biological systems
Optical imaging studies have demonstrated that the dynamic ranges of glomerular activation extend across many orders of magnitude of odorant concentration, even as the corresponding postsynaptic activity in the apical dendrites of second-order principal neurons remains relatively stable (Storace and Cohen, 2017). These results reveal that the primary olfactory sensory neuron (OSN) population can successfully encode an extensive intensity range of environmental variance, encompassing diverse qualities, and concentrations of some number of odorous ligands
Summary
We propose that, following the signal conditioning operations of the nasal epithelium and glomerular-layer circuitry, the plastic external plexiform layer of the olfactory bulb effects a process of category learning—the basis for extracting meaningful, quasi-discrete odor representations from the metric space of undifferentiated olfactory quality. This early categorization process resolves the foundational problem of how odors of interest can be recognized in the presence of strong competitive interference from simultaneously encountered background odorants.
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