Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality
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The study by Kim et al. combines extracellular recordings from olfactory sensory neurons (OSNs) in locusts with computational modelling approaches to investigate the dynamics of odour responses. The authors demonstrate that OSN responses can be grouped into four distinct response motifs, with OSNs showing different motifs in an odour-dependent manner. Using computational modelling the authors provide some evidence that these diverse response motifs expand the coding space and could facilitate odour discrimination and navigation. This study can be of high relevance to both experimental and theoretical neuroscientists investigating odour coding and odour-driven behaviours such as navigation. In its present form, while the experimental data and analysis are of the highest quality, the modelling part needs to be expanded to fully support the experimental measurements.
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Abstract
Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume’s composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.
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Author Response
Reviewer #1 (Public Review):
In order to study odor response dynamics in the olfactory peripheral organ, Kim et al. employs extracellular sensillum recording from the locust antenna to a set of 4 odors at different concentrations. Using spike sorting to assign odor responses to single olfactory sensory neurons (OSNs), the authors demonstrate that OSNs exhibit four distinct response motifs comprising two types of excitation, namely fast and delayed excitatory responses, as well as inhibitory responses in form of offset responses and inhibition. Notably, OSNs can switch between these four motifs depending on the odor applied. This finding is highly interesting and facilitates odor classification as demonstrated by computational modeling in this study. Furthermore, the authors demonstrate that each response motifs …
Author Response
Reviewer #1 (Public Review):
In order to study odor response dynamics in the olfactory peripheral organ, Kim et al. employs extracellular sensillum recording from the locust antenna to a set of 4 odors at different concentrations. Using spike sorting to assign odor responses to single olfactory sensory neurons (OSNs), the authors demonstrate that OSNs exhibit four distinct response motifs comprising two types of excitation, namely fast and delayed excitatory responses, as well as inhibitory responses in form of offset responses and inhibition. Notably, OSNs can switch between these four motifs depending on the odor applied. This finding is highly interesting and facilitates odor classification as demonstrated by computational modeling in this study. Furthermore, the authors demonstrate that each response motifs follows different adaptation profiles which further results in an increased coding space. The authors conclude and provide evidence with their model that the experimentally observed response dynamics also facilitate determining the distance to the odor source. The obtained results are novel and demonstrate a new dimension of odor response properties at the peripheral level. However, given that the authors used a very limited set of chemically similar odors and considering that the broad tuning and wiring of OSNs in the locust is special and follows different rules compared to the olfactory circuitry of OSNs in other insects (i.e. locust OSNs do not converge onto a single glomerulus but target multiple glomeruli), I wonder whether the observed distinct response motifs are a general phenomenon or a rather special case. I therefore recommend that the authors discuss their findings in the light of these key issues before general conclusions with regard to odor coding rules is being drawn. Do these response motifs also occur for highly ecologically relevant odors, such as PAN, where a rather specialized olfactory circuit would be assumed? Hence, the MS would benefit if those questions would be addressed as well. In addition, the computational modeling approach is written in specialized terms and is therefore difficult to grasp for readers lacking modeling expertise.
We thank the reviewer for this very positive and helpful assessment of our work. We agree with suggestions to expand our discussion of (1) olfactory circuitry following OSNs and of (2) responses to highly ecologically relevant odors. We have also extensively revised the description of our computational modeling approach to make it understandable to non-specialists.
In brief:
(1) The results we present here address only peripheral activity – we do not record or model responses of follower neurons. Because our conclusions do not depend to any extent upon the architecture of the locust's olfactory system, we would prefer to limit necessarily speculative discussion or analyses of these factors. We agree these factors provide interesting context for our work, so we have now expanded our discussion to include: “In other species, how exactly ORN response patterns are utilized downstream may depend on species-specific variations in connectivity between ORNs and the antennal lobe and its glomeruli” (lines 490-492). More investigation is needed to address this important question. Nevertheless, our study shows ORN response motifs provide useful information, and conveying this information to downstream circuits augments coding space.
(2) We share the reviewers’ concern that our odor set should include ecologically particularly relevant odors. Indeed, it was for this reason that our odor set includes components of the locust diet, wheat grass: 1-Octanol, 1-Hexanol, and Cyclohexanol. As above, though, we are reluctant to speculate on the responses of downstream circuits. But to acknowledge the reviewer’s important point, we have added the following text to our discussion in lines 401-405: “For these studies we used odorants known to be ecologically relevant to locusts, including several found in the head space of wheat grass. Future experiments with larger sets of odorants, including blends or locust pheromones like 4-vinylanisole (4VA) and phenylacetonitrile (PAN), may help clarify the logic of motif switching.”
Reviewer #2 (Public Review):
This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.
The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.
We agree this phenomenon is of great interest, and we have now expanded our discussion section to address it.
In the cited paper (see also Su et al, 2011), PID response characteristics were indeed quite different for different odors, reflecting “fast” and “slow” intrinsic odor dynamics. We are aware of these studies and shared the reviewer’s concern, and for this reason we also made PID recordings during odor presentations. These recordings show our odor set included only “fast” odorants (please see the figure below). We also note that, across our extensive dataset, all odors could elicit all four response motifs. These observations rule out the possibility that differences in how odorants are carried by air underlie the different temporal dynamics we observed in OSN responses.
We now discuss this important point in the text, as follows: “Earlier work established that the intrinsic dynamic properties of odorants, described as “fast” or “slow,” can contribute to variations in the timing of ORN responses (Su et al., 2011; Martelli et al., 2013). However, our experiments ruled out the possibility that intrinsic odorant dynamics underly the response motifs we describe here. First, across our extensive dataset, all odors could elicit all four response motifs; second, photoionization detector recordings of our odor presentations all revealed “fast” dynamics (not shown). It seems likely that “slow” odors would elicit concentration-dependent elaborations in the response motifs we observed. In future work it will be interesting to investigate ways intrinsic odor dynamics interact with ORN response motifs. We predict such interactions would further increase ORN response dimensionality” (lines 370-380).
There are several questions that need to be addressed regarding the simulations part of the manuscript.
- There is a mismatch between the number of ORNs used in the model and in the insect system studied.
The exact number of ORNs in the locust is not known, but estimates range from 45,000 to 113,000 per antenna (Leitch & Laurent 1996; Perez-Orive et al 2002; Galizia & Sachse 2010). We chose to model a smaller but still large set of ORNs (10,000) which we believe is a reasonable compromise between the ideal size (which would be true number of ORNs in locust), and limitations needed to achieve practical computational efficiency. Indeed, almost all computational models are unavoidably scaled-down versions of the biological organisms.
- The demonstration in Figure 5 that motif switching improves odor classification includes motif switching for a given odorant, which is not observed experimentally.
We regret that our description of the experiment presented in Figure 5 was confusing, and we have revised extensively to clarify this in our revision. In brief, the simulation shown in Figure 5 was not, as the reviewer understood, an attempt to model motif switching that occurs when a given odorant is presented repeatedly; rather, it shows how responses to two different, similar odors (Odor 1 and Odor 2) become increasingly different from each other when the probability of motif switching increases.
We have now revised the text to clarify this point as follows: “With our model we could independently vary odor-elicited response motifs and response magnitudes (Figure 4E), allowing us to evaluate the extent to which motif switching benefitted odor classification in a way that cannot be tested in vivo. Thus, we simulated a realistically large number of ORNs (10,000) and compared the relative success of classifying two different odors (Odor 1 and Odor 2) with three different versions of our model in which we systematically varied the probability of motif switching. Model Version 1: the probability of switching response motif when switching from Odor 1 to Odor 2 was 0%; Version 2: 10%; Version 3: 50%. We found that the model versions that simulated higher motif switching probability made it easier to distinguish these two similar odors.” (lines 191-195, 206-209).
We have also revised the figure caption as follows: “Computational model shows response motif switching substantially improves odor classification. A) Simulated ORN spiking illustrates different motif switching probabilities. Odors 1 and 2 are similar (see Methods). Each ORN response is sorted by motifs elicited by Odor 1. Raster plots show the responses to Odor 2 become increasingly different from responses to Odor 1 as motif switching probability increases. B) ORN odor-elicited response trajectories in reduced PCA space show motif switching increases the separation between responses to similar Odors 1 and 2; response to Odor 1 (blue) is the same in each panel; response to Odor 2 (red) changes with switching probability. C) Odor classification success as a function of odor similarity and motif switching probability for 1s (top) and 4s (bottom) stimulus pulses; even low switching probabilities improve classification performance; darker shading indicates lower classification accuracy. Odor similarity is quantified by angles (degrees) between odor vectors (see Methods)” (lines 231-239).
- The methodology for estimating neural temporal dynamics needs to be corrected to apply to the natural stimuli used here.
We agree and thank the reviewer for raising this important point. To appropriately account for natural correlations present in the stimuli we used in experiments, we have now completely redone our analysis, substantially revised Figure 6, and rewritten the Methods section titled “Temporal filters using linear non-linear models.” Using methods appropriate for strongly correlated and natural odorant stimuli delivered experimentally, we obtained results consistent with those in the previous version of our manuscript.
Reviewer #3 (Public Review):
In this contribution, the authors align an extensive analysis of in vivo recordings of olfactory receptor neuron (ORN) responses to odors in the locust with a data-driven mathematical model of ORN population coding. Their results provide novel insights into the temporal dynamics of peripheral encoding of time-varying and naturalistic olfactory input.
The manuscript presents three central experimental results: 1) ORNs odor responses can be grouped into 4 distinct response motifs (response profiles). This has partly been known with respect to the typical excitatory phasic-tonic motif and odor offset responses. The exhaustive data set here is however unprecedented. 2) Individual ORNs can switch their response motif, e.g. from excitatory to inhibitory responses. To my knowledge, this is entirely new, highly interesting, and has strong implications. For one it implies an increased coding space and odor separability, which is supported by the authors' model study. It also bears implications for our understanding of processing in the antennal lobe where projection neurons were shown to exhibit property but this has largely been attributed to network processing within the AL. The authors discuss ephaptic interactions as a possible underlying mechanism. 3) ORNs not only show classical within and across pulse adaptation where the response amplitude reduces, but also the novel result that the offset response can increase across repeated pulses with short inter-stimulus intervals. The data-driven model reproduces the experimental observations and a population model that confirms the assumed increase in coding space. In the temporal domain, the authors then perform simulations that mimic realistic stimulus statistics with stochastic arrival of odor packets of variably short duration. The model with a trained linear filter and a non-linear transfer function faithfully predicts the experimental firing rates.
These results, based on an exhaustive set of experimental data, provide a novel view of peripheral odor coding in insects and they will have a particularly strong impact on biologically realistic computational (spiking) circuit models of sensory processing and sensory-to-motor transformations during odor source navigation in naturalistic simulated odor environments where conclusive data and analysis of ORN signaling has thus far been lacking.
We thank the reviewer for this very thoughtful and positive assessment of our work.
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eLife assessment
The study by Kim et al. combines extracellular recordings from olfactory sensory neurons (OSNs) in locusts with computational modelling approaches to investigate the dynamics of odour responses. The authors demonstrate that OSN responses can be grouped into four distinct response motifs, with OSNs showing different motifs in an odour-dependent manner. Using computational modelling the authors provide some evidence that these diverse response motifs expand the coding space and could facilitate odour discrimination and navigation. This study can be of high relevance to both experimental and theoretical neuroscientists investigating odour coding and odour-driven behaviours such as navigation. In its present form, while the experimental data and analysis are of the highest quality, the modelling part needs to be expanded to …
eLife assessment
The study by Kim et al. combines extracellular recordings from olfactory sensory neurons (OSNs) in locusts with computational modelling approaches to investigate the dynamics of odour responses. The authors demonstrate that OSN responses can be grouped into four distinct response motifs, with OSNs showing different motifs in an odour-dependent manner. Using computational modelling the authors provide some evidence that these diverse response motifs expand the coding space and could facilitate odour discrimination and navigation. This study can be of high relevance to both experimental and theoretical neuroscientists investigating odour coding and odour-driven behaviours such as navigation. In its present form, while the experimental data and analysis are of the highest quality, the modelling part needs to be expanded to fully support the experimental measurements.
-
Reviewer #1 (Public Review):
In order to study odor response dynamics in the olfactory peripheral organ, Kim et al. employs extracellular sensillum recording from the locust antenna to a set of 4 odors at different concentrations. Using spike sorting to assign odor responses to single olfactory sensory neurons (OSNs), the authors demonstrate that OSNs exhibit four distinct response motifs comprising two types of excitation, namely fast and delayed excitatory responses, as well as inhibitory responses in form of offset responses and inhibition. Notably, OSNs can switch between these four motifs depending on the odor applied. This finding is highly interesting and facilitates odor classification as demonstrated by computational modeling in this study. Furthermore, the authors demonstrate that each response motifs follows different …
Reviewer #1 (Public Review):
In order to study odor response dynamics in the olfactory peripheral organ, Kim et al. employs extracellular sensillum recording from the locust antenna to a set of 4 odors at different concentrations. Using spike sorting to assign odor responses to single olfactory sensory neurons (OSNs), the authors demonstrate that OSNs exhibit four distinct response motifs comprising two types of excitation, namely fast and delayed excitatory responses, as well as inhibitory responses in form of offset responses and inhibition. Notably, OSNs can switch between these four motifs depending on the odor applied. This finding is highly interesting and facilitates odor classification as demonstrated by computational modeling in this study. Furthermore, the authors demonstrate that each response motifs follows different adaptation profiles which further results in an increased coding space. The authors conclude and provide evidence with their model that the experimentally observed response dynamics also facilitate determining the distance to the odor source. The obtained results are novel and demonstrate a new dimension of odor response properties at the peripheral level. However, given that the authors used a very limited set of chemically similar odors and considering that the broad tuning and wiring of OSNs in the locust is special and follows different rules compared to the olfactory circuitry of OSNs in other insects (i.e. locust OSNs do not converge onto a single glomerulus but target multiple glomeruli), I wonder whether the observed distinct response motifs are a general phenomenon or a rather special case. I therefore recommend that the authors discuss their findings in the light of these key issues before general conclusions with regard to odor coding rules is being drawn. Do these response motifs also occur for highly ecologically relevant odors, such as PAN, where a rather specialized olfactory circuit would be assumed? Hence, the MS would benefit if those questions would be addressed as well. In addition, the computational modeling approach is written in specialized terms and is therefore difficult to grasp for readers lacking modeling expertise.
-
Reviewer #2 (Public Review):
This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.
The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.
There are several questions that need to be addressed regarding the simulations part of the manuscript.
There is a mismatch …
Reviewer #2 (Public Review):
This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.
The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.
There are several questions that need to be addressed regarding the simulations part of the manuscript.
There is a mismatch between the number of ORNs used in the model and in the insect system studied.
The demonstration in Figure 5 that motif switching improves odor classification includes motif switching for a given odorant, which is not observed experimentally.
The methodology for estimating neural temporal dynamics needs to be corrected to apply to the natural stimuli used here.
-
Reviewer #3 (Public Review):
In this contribution, the authors align an extensive analysis of in vivo recordings of olfactory receptor neuron (ORN) responses to odors in the locust with a data-driven mathematical model of ORN population coding. Their results provide novel insights into the temporal dynamics of peripheral encoding of time-varying and naturalistic olfactory input.
The manuscript presents three central experimental results: 1) ORNs odor responses can be grouped into 4 distinct response motifs (response profiles). This has partly been known with respect to the typical excitatory phasic-tonic motif and odor offset responses. The exhaustive data set here is however unprecedented. 2) Individual ORNs can switch their response motif, e.g. from excitatory to inhibitory responses. To my knowledge, this is entirely new, highly …
Reviewer #3 (Public Review):
In this contribution, the authors align an extensive analysis of in vivo recordings of olfactory receptor neuron (ORN) responses to odors in the locust with a data-driven mathematical model of ORN population coding. Their results provide novel insights into the temporal dynamics of peripheral encoding of time-varying and naturalistic olfactory input.
The manuscript presents three central experimental results: 1) ORNs odor responses can be grouped into 4 distinct response motifs (response profiles). This has partly been known with respect to the typical excitatory phasic-tonic motif and odor offset responses. The exhaustive data set here is however unprecedented. 2) Individual ORNs can switch their response motif, e.g. from excitatory to inhibitory responses. To my knowledge, this is entirely new, highly interesting, and has strong implications. For one it implies an increased coding space and odor separability, which is supported by the authors' model study. It also bears implications for our understanding of processing in the antennal lobe where projection neurons were shown to exhibit property but this has largely been attributed to network processing within the AL. The authors discuss ephaptic interactions as a possible underlying mechanism. 3) ORNs not only show classical within and across pulse adaptation where the response amplitude reduces, but also the novel result that the offset response can increase across repeated pulses with short inter-stimulus intervals. The data-driven model reproduces the experimental observations and a population model that confirms the assumed increase in coding space. In the temporal domain, the authors then perform simulations that mimic realistic stimulus statistics with stochastic arrival of odor packets of variably short duration. The model with a trained linear filter and a non-linear transfer function faithfully predicts the experimental firing rates.
These results, based on an exhaustive set of experimental data, provide a novel view of peripheral odor coding in insects and they will have a particularly strong impact on biologically realistic computational (spiking) circuit models of sensory processing and sensory-to-motor transformations during odor source navigation in naturalistic simulated odor environments where conclusive data and analysis of ORN signaling has thus far been lacking.
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