Simultaneous detection and estimation in olfactory sensing

Abstract

The mammalian olfactory system shows an exceptional ability for rapid and accurate decoding of both the identity and concentration of odorants. Previous works have used the theory of compressed sensing to elucidate the algorithmic basis for this capability: decoding odor information from the responses of a restricted repertoire of receptors is possible because only a few relevant odorants are present in any given sensory scene. However, existing circuit models for olfactory decoding still cannot contend with the complexity of naturalistic olfactory scenes; they are limited to detection of a handful of odorants. Here, we propose a model for olfactory compressed sensing inspired by simultaneous localization and mapping algorithms in navigation, in which the set of present odors and their concentrations are inferred separately. We implement this split inference in a biologically-plausible recurrent circuit by introducing separate dynamics matched to the distinct nature of presence and concentration, and drawing on the framework of Mirrored Langevin Dynamics. This model can accurately infer presence and concentration at scale. Moreover, its circuit structure can be mapped onto the primary cell types of the olfactory bulb, giving a possible normative account for functional differences between mitral and tufted cells. Our approach offers a general path towards circuit algorithms for probabilistic inference--in olfactory sensing and beyond--that both perform well in naturalistic environments and make experimentally-testable predictions for neural response dynamics.