Odor Stimuli: Not Just Chemical Identity

Abstract

In most sensory modalities the underlying physical phenomena are well understood, and stimulus properties can be precisely controlled. In olfaction, the situation is different. The presence of specific chemical compounds in the air (or water) is the root cause for perceived odors, but it remains unknown what organizing principles, equivalent to wavelength for light, determine the dimensions of odor space. Equally important, but less in the spotlight, odor stimuli are also complex with respect to their physical properties, including concentration and time-varying spatio-temporal distribution. We still lack a complete understanding or control over these properties, in either experiments or theory. In this review, we will concentrate on two important aspects of the physical properties of odor stimuli beyond the chemical identity of the odorants: (1) The amplitude of odor stimuli and their temporal dynamics. (2) The spatio-temporal structure of odor plumes in a natural environment. Concerning these issues, we ask the following questions: (1) Given any particular experimental protocol for odor stimulation, do we have a realistic estimate of the odorant concentration in the air, and at the olfactory receptor neurons? Can we control, or at least know, the dynamics of odorant concentration at olfactory receptor neurons? (2) What do we know of the spatio-temporal structure of odor stimuli in a natural environment both from a theoretical and experimental perspective? And how does this change if we consider mixtures of odorants? For both topics, we will briefly summarize the underlying principles of physics and review the experimental and theoretical Neuroscience literature, focusing on the aspects that are relevant to animals’ physiology and behavior. We hope that by bringing the physical principles behind odor plume landscapes to the fore we can contribute to promoting a new generation of experiments and models.

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

“…So far we have used simulated plumes corresponding to 60 m distance from the source. At different distances the maximum whiff durations will vary (Pannunzi and Nowotny, 2019). We therefore asked whether and how the efficiency of the two mechanisms depends on maximum whiff duration and hence distance from the source.…”

“…about 60 ORN types in Drosophila, and instead focused on a single sensillum with two co-housed ORNs; 3. the true complexity of the many different LN types and transmitters in the AL (Silbering et al, 2008), using only GABA A -like LNs. 4. the spatial distribution of the sensilla on the surface of the antenna or the maxillary palp; 5. the complexity of odor stimuli delivered by stimulation devices in the experiments we are mimicking for the single pulse investigation (see the corresponding Model and methods section, (Pannunzi and Nowotny, 2019)), 6. the asymmetry of NSIs where there is some evidence that the strength of the NSIs is proportional to the size of the ORN that is exerting the interaction onto another neuron Zhang et al (2019). By making these simplifications we were able to reduce the number of free parameters in the model, reasonably constrain most parameters and scan the few remaining parameters, such as the strength of LN inhibition, across a reasonable range.…”

“…However, our model also makes a clear departure from the large number of models that have been built on assumptions and data based on long, essentially constant, odor step stimuli. While these kind of stimuli are not impossible, they can be considered as the exception more than the rule; for instance, even at more than 60 m from the source, around 90% of whiffs last less than 200 ms (Justus et al, 2002;Yee et al, 1993), see (Pannunzi and Nowotny, 2019) for review. This insight is particularly difficult to reconcile with models that emphasize and depend on intrinsically generated oscillations in the antennal lobe (Bazhenov et al, 2001a,b;Linster et al, 2005Linster et al, , 1993Linster and Smith, 1997), and models that depend on comparatively slow, intrinsically generated dynamics such as the models based on the winnerless competition mechanism (Rabinovich et al, 2001;Laurent et al, 2001).…”

“…Of course it is not possible to distinguish these two possibilities from a single, instantaneous sampling, but on average the odorants coming from the same source are more correlated than odorants coming from separate sources (see panel a of Figure 7-Figure Supplement 1). To test the function of NSIs for odor source separation, we adopted long stimuli (>3 s), with statistical properties that resemble the filaments observed downwind from an odor source in an open environment (Mylne and Mason, 1991;Pannunzi and Nowotny, 2019;Yee et al, 1993Yee et al, , 1995 at zero crosswind distance. For these conditions, the distributions of whiff and clean air durations follow a power law with exponent -3/2 (see, e.g., Yee et al…”

“…A number of computational models have been developed to capture different aspects of the olfactory system of insects. However, until recently, most modeling efforts were based on the assumption of continuous constant stimuli, which are partially realistic only for non-turbulent fluid dynamics regimes (see (Pannunzi and Nowotny, 2019), and reference therein). Most commonly insects encounter turbulent regimes, in which odorant concentration fluctuates rapidly (see The model consists of a subset of the early olfactory system of insects from ORNs to the AL using only two groups of ORNs (ORN and ORN ) and their respective PNs and LNs.…”