Olfactory Perception in Relation to the Physicochemical Odor Space

A growing body of research aims at solving what is often referred to as the stimulus-percept problem in olfactory perception. Although computational efforts have made it possible to predict perceptual impressions from the physicochemical space of odors, studies with large psychophysical datasets from non-experts remain scarce. Following previous approaches, we developed a physicochemical odor space using 4094 molecular descriptors of 1389 odor molecules. For 20 of these odors, we examined associations with perceived pleasantness, intensity, odor quality and detection threshold, obtained from a dataset of 2000 naïve participants. Our results show significant differences in perceptual ratings, and we were able to replicate previous findings on the association between perceptual ratings and the first dimensions of the physicochemical odor space. However, the present analyses also revealed striking interindividual variations in perceived pleasantness and intensity. Additionally, interactions between pleasantness, intensity, and olfactory and trigeminal qualitative dimensions were found. To conclude, our results support previous findings on the relation between structure and perception on the group level in our sample of non-expert raters. In the challenging task to relate olfactory stimulus and percept, the physicochemical odor space can serve as a reliable and helpful tool to structure the high-dimensional space of olfactory stimuli. Nevertheless, human olfactory perception in the individual is not an analytic process of molecule detection alone, but is part of a holistic integration of multisensory inputs, context and experience.

Increased accuracy of signaling by hyperbolic odorant mixtures in a beneficial insect-plant relationship

Animals use odors in many natural contexts, for example, for finding mates or food, or signaling danger. Analyses of natural odors search for either the most meaningful components of a natural odor mixture, or they use linear metrics to analyze the mixture compositions. Both analyses assume that the odor space itself is Euclidian, like visual and auditory spaces. However, we have recently shown that the physical space for complex mixtures is ‘hyperbolic’ – curved – because of the correlations that arise in biosynthetic pathways. Here we shown that the shape of the space for flowers (Brassica rapa) using an existing data set can also be better described with a hyperbolic rather than a linear shape, and that components in the space correlate to the nectar and pollen resource sought by bee pollinators. We also show that honey bee and bumble bee antennae can detect most components of the B. rapa odor space. We argue that further investigation of the implications of hyperbolic space can have important implications for how sensory systems have evolved to encode the space.

Modular structure of human olfactory receptor codes reflects the bases of odor perception

The circuits of olfactory signaling are reminiscent of complex computational devices. The olfactory receptor code, which represents the responses of receptors elicited by olfactory stimuli, is effectively an input code for the neural computation of odor sensing. Here, analyzing a recent dataset of the odorant-dependent responses of human olfactory receptors (ORs), we show that the space of human olfactory receptor codes is partitioned into a modular structure where groups of receptors are “labeled” for key olfactory features. Our analysis reveals a low-dimensional structure in the space of human odor perception, with the receptor groups as the bases to represent major features in the perceptual odor space. These findings provide a novel evidence that some fundamental olfactory features are already hard-coded at the level of ORs, separately from the higher-level neural circuits.

Mosaic representations of odors in the input and output layers of the mouse olfactory bulb

The elementary stimulus features encoded by the olfactory system remain poorly understood. We examined the relationship between 1,666 physical-chemical descriptors of odors and the activity of olfactory bulb inputs as well as outputs in awake mice. Glomerular and M/T cell responses were sparse and locally heterogeneous, with only a coarse dependence of glomerular positions on physical-chemical properties. Odor features represented by ensembles of M/T cells were overlapping, but distinct from those represented in glomeruli, consistent with extensive interplay between feedforward and feedback inputs to the bulb. This reformatting was well-described as a rotation in odor space. The descriptors accounted for a small fraction in response variance, and the similarity of odors in physical-chemical space was a poor predictor of similarity in neuronal representations. Our results suggest that commonly used physical-chemical properties are not systematically represented in bulbar activity and encourage further search for better descriptors of odor space.

Disorder and the neural representation of complex odors: smelling in the real world

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose a new interpretation of how the architecture of olfactory circuits is adapted to meet these immense complementary challenges. First, the diffuse binding of receptors to many molecules compresses a vast odor space into a tiny receptor space, while preserving similarity. Next, lateral interactions “densify” and decorrelate the response, enhancing robustness to noise. Finally, disordered projections from the periphery to the central brain reconfigure the densely packed information into a format suitable for flexible learning of associations and valences. We test our theory empirically using data from Drosophila. Our theory suggests that the neural processing of olfactory information differs from the other senses in its fundamental use of disorder.

On the dimensionality of odor space

There is great interest in understanding human olfactory experience from a principled and quantitative standpoint. The comparison is often made to color vision, where a solid framework with a three-dimensional perceptual space enabled a rigorous search for the underlying neural pathways, and the technological development of lifelike color display devices. A recent, highly publicized report claims that humans can discriminate at least 1 trillion odors, which exceeds by many orders of magnitude the known capabilities of color discrimination. This claim is wrong. I show that the failure lies in the mathematical method used to infer the size of odor space from a limited experimental sample. Further analysis focuses on establishing how many dimensions the perceptual odor space has. I explore the dimensionality of physical, neural, and perceptual spaces, drawing on results from bacteria to humans, and propose some experimental approaches to better estimate the number of discriminable odors.