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# Analysis of fast calcium dynamics of honey bee olfactory coding

Marco Paoli, Antoine Wystrach, Brice Ronsin, Martin Giurfa

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## To cite this version:

Marco Paoli, Antoine Wystrach, Brice Ronsin, Martin Giurfa. Analysis of fast calcium dynamics of honey bee olfactory coding. eLife, 2024, 13, 10.7554/eLife.93789.3. hal-04796573

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# Smell and Aftersmell: Fast Calcium Imaging Dynamics of Honey Bee Olfactory Coding

Marco Paoli, Antoine Wystrach, Brice Ronsin, Martin Giurfa

Neuroscience Paris-Seine - Institut de biologie Paris-Seine, Sorbonne Université, INSERM, CNRS, Paris, France • Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université Paul Sabatier, CNRS, Toulouse, France • Institut Universitaire de France (IUF), Paris, France

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## Abstract

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus-specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning-induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees' response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

eLife assessment

This study presents a **valuable** set of calcium imaging data to analyze the dynamics of excitatory and inhibitory responses in the projection neurons of the honeybees during and after odor presentations. The neural circuit model fed with the imaging data recapitulated odor-specific activity in the Kenyon cells in the post-odor period and the timing shift of behavioral response in associative learning. This **solid** work will be of interest to researchers working on associative learning.
Introduction

The logic of olfactory coding and learning has been extensively studied at the behavioural and neural levels in various insect models (Adam et al., 2022 ; Galizia, 2014 ; Giurfa, 2015 ; Jefferis et al., 2007 ). Among them, the honey bee Apis mellifera has played a pivotal role in our understanding of these processes due to its behavioural accessibility and tractability of its nervous system. In bees, olfactory perception and learning have been typically investigated using the proboscis extension reflex (PER) (Giurfa, 2007 ; Menzel, 1999 ), a protocol that relies on pairing a neutral olfactory stimulus (the conditioned stimulus or CS) with a positive reinforcement of sugar solution (the unconditioned stimulus or US) (Bitterman et al., 1983 ; Giurfa and Sandoz, 2012 ; Takeda, 1961 ). In the classical version of PER conditioning, the CS precedes and partially overlaps US presentation. This results in high levels of specific memory for the conditioned odorant. Conversely, when the reward precedes the stimulus (backward conditioning), no positive association can be established (Felsenberg et al., 2014 ; Hellstern et al., 1998 ). It also enables the study of more sophisticated cognitive processes such as trace learning (Paoli et al., 2023a ; Szyszka et al., 2011 ) and patterning discrimination (Deisig et al., 2001 ; Devaud et al., 2015 ). In the former, CS and US are not overlapping but separated by a stimulus-free temporal gap, whereas in the latter, bees are trained to respond in opposite ways to a two-odorant mixture compared to its individual components (Deisig et al., 2001 ; Devaud et al., 2015 ). Overall, the olfactory conditioning of PER provides a robust read-out for investigating the dynamics of olfactory memory formation (Giurfa and Sandoz, 2012 ; Villar et al., 2020 ) and olfactory perception (Guerrieri et al., 2005 ).

Our understanding of the neural processing subtending olfactory coding and learning relies on decades of neuroanatomical and neurophysiological studies performed in honey bees (Paoli and Galizia, 2021 ). Olfactory processing starts at the peripheral level, when volatile chemicals interact with the olfactory receptors expressed on the dendritic membrane of the olfactory sensory neurons (OSNs). The biochemical nature of odorant-receptor interactions allows for a certain molecule to bind to multiple receptors with different affinities, resulting in the activation of an OSNs sub-population with odorant-specific response intensities and latencies (Münch and Galizia, 2016 ). Olfactory sensory neurons innervate the first olfactory processing centre, the antennal lobe (AL), where the neurons expressing the same olfactory receptor converge onto one of ~160 glomeruli, the anatomical and functional units of the AL (Flanagan and Mercer, 1989 ). Thus, odorant detection results in the stimulus-specific activation of a subset of glomeruli, creating a stereotypical map of odour-induced glomerular responses (Galizia et al., 1999 ; Sachse et al., 1999 ). The signal processed in the AL is forwarded by ~800 output neurons – the projection neurons (PNs) – to higher-order brain centres: the mushroom bodies (MBs), central, paired structures dedicated to multisensory integration, memory storage and retrieval (Heisenberg, 2003 ; Stopfer, 2014 ), and the lateral horn of the protocerebrum, a diffuse bilateral structure involved in valence coding of odorants (Jeanne et al., 2018 ; Roussel et al., 2014 ; Strutz et al., 2014 ). The MB architecture is defined by the layout of its ~185 000 intrinsic neurons, the Kenyon cells (KCs). Each KC extends its dendritic arborisation within the MB input regions, termed the calyces, where it receives input from multiple PNs, and projects its axon to the MB output region, termed the pedunculus, where it bifurcates into the vertical (α and γ) and the medial (β) lobes (Mobbs, 1982 ; Strausfeld, 2002 ). Kenyon cells integrate, among others, the excitatory input of olfactory PNs and the inhibitory input of recurrent GABAergic feedback neurons (Ganeshina and Menzel, 2001 ; Grünewald, 1999 ; Rybak and Menzel, 1993 ; Zwaka et al., 2018 ). The latter plays a critical role in shaping KCs olfactory responsiveness and maintaining a sparse output over a wide range of odorants and concentrations (Papadopoulou et al., 2011 ; Stopfer, 2014 ). Antennal lobe, mushroom body and lateral horn are innervated by the VUMmx1 neuron (Hammer, 1993 ), an unpaired octopaminergic neuron conveying appetitive reward-related information to the olfactory circuit and mediating reward-based olfactory memory
formation. The activity in the Kenyon cells is integrated by MB output neurons (MBONs). These neurons exhibit which show learning-related plasticity and provide valence-loaded information to pre-motor areas (Aso et al., 2014a; Okada et al., 2007; Schmalz et al., 2022; Strube-Bloss and Rössler, 2018).

The neural representation of odorants in the AL has been extensively described by means of functional calcium imaging (Paoli and Haase, 2018). *In vivo* imaging allowed observing that each stimulus is represented with a specific pattern of excitatory and inhibitory responses across the glomeruli of the AL, (Galizia et al., 1999; Sachse and Galizia, 2002; Sachse et al., 1999), and that perceptually similar odorants elicit similar glomerular response patterns (Guerrieri et al., 2005). Calcium imaging of AL activity has been typically conducted at low temporal resolutions (~100-200 ms) (Locatelli et al., 2016; Mertes et al., 2021; Nouvian et al., 2018; Sachse and Galizia, 2003). This is partially justified by the slow dynamics of fluorescent calcium sensors, which resulted – in general – in the compression of olfactory representation into a spatial vector of glomerular response intensity, with a concomitant loss of information on neural response dynamics. While calcium signal decay is relatively slow (>100 ms), its onset is fast (<10 ms) (Helassa et al., 2015; Moreaux and Laurent, 2007), and provides the possibility for investigating parameters such as glomerular response latency (Junek et al., 2010; Paoli et al., 2018), signal frequency components (Paoli et al., 2016) and interglomerular information transfer (Chen et al., 2023; Paoli et al., 2023b). In this study, we significantly improved the temporal resolution of calcium imaging recordings of olfactory neurons in the bee brain by means of a resonant scanning multiphoton microscope. This allowed us to record calcium activity at a <10-ms resolution, preserving temporal information of the olfactory code and yielding a more realistic representation of odour trajectories in the AL. First, we observed that odour representation changes dynamically during and after an olfactory stimulation, resulting in specific odour and after-odour images. Then, we showed that most glomerular response profiles presented an inhibitory component, suggesting that glomerular activity is strongly shaped by local inhibition, with only a minor influence – if any – of local excitatory interneurons.

Calcium imaging analysis was combined with a modelling approach to investigate how odorant representation evolves from the AL to the MB. Electrophysiology experiments have provided insight on the phasic and sparse activity of Kenyon cells as well as into its oscillatory nature (Laurent and Davidowitz, 1994; Perez-Orive et al., 2002; Stopfer, 2014). However, the unavailability of an imaging method allowing the visualisation of olfactory coding in a KC ensemble prevented us from further understanding how odorants are represented within the MB, e.g. how the neural representation of an odorant is transformed from the AL to the MB or to what extent the similarity among odorant representation is maintained in the KC space. One way to address these questions is by using neural network models constructed via the abstraction of common features of MBs across insect species (e.g. fruit fly, locust, honey bee) to reproduce cognitive tasks such as stimulus discrimination or learning. Models can be built by simulating multiple neurons interacting with each other according to physiological rules such as the dynamics of action potentials or Hebbian synaptic plasticity (Eschbach et al., 2020; Finelli et al., 2008; Gkanias et al., 2022; Huerta et al., 2004; Smith et al., 2008). Other models simulate the MB neural network by considering the statistics of connectivity within the neuropil, e.g. the ratio of PNs to KCs, the average number of synaptic connections, the neuronal firing rate (Ardin et al., 2016; Buehlmann et al., 2020; Le Moël et al., 2019; Peng and Chittka, 2017; Springer and Nawrot, 2021; Wystrach, 2023). Here, we followed the second approach and built a simplified but realistic neural network model of the MB based on the neuroanatomical and functional properties of the insect's olfactory circuit. The model comprises three layers of neurons: (1) a MB input layer to provide the model experimentally acquired time-series of PN activity; (2) a MB intrinsic layer, where the input signal is distributed to a population of modelled KCs based on neuroanatomical and physiological data; (3) a MB output layer, where one appetitive MB output neuron (MBON) receives input from the KC layer and can be subject to learning-induced plasticity. Generally, the input for this type of model is simulated based on the
spatial and temporal statistics of the odour-induced glomerular activity (Eschbach et al., 2020; Finelli et al., 2008; Gkanias et al., 2022; Huerta et al., 2004; Le Moël and Wystrach, 2020; Peng and Chittka, 2017; Smith et al., 2008; Springer and Nawrot, 2021). In this case, we fed the model with the time series of the PN responses recorded via *in vivo* calcium imaging analysis from multiple individuals exposed to three different odorants at the MB working frequency of 20 Hz (Cassenaer and Laurent, 2007; Laurent and Naraghi, 1994).

Here, we show that an MB neural network model based solely on three simple connectivity rules abstracted from insect studies – sparse connectivity, feedback inhibition, and learning-induced synaptic modulation – is sufficient to explain fundamental MB processing features such as improved olfactory discrimination and associative learning. The model also predicts that the presence of post-stimulus inhibitory and excitatory activity leads to the recruitment of a second pool of KC, resulting in a distinct after-odour representation in the MB. Additionally, the model’s performance in appetitive learning and across-stimuli generalisation was coherent with empirical measurements obtained from behavioural protocols. Finally, using real physiological datasets as input, we showed that such a model is robust to noise and biological variability across multiple stimulus repetitions.

## Results

### Response dynamics of projection neurons in high-temporal resolution

Antennal lobe PNs of eight bees were back-filled with the calcium sensor Fura-2 (Paoli and Haase, 2018; Sachse and Galizia, 2002) to measure odour-induced glomerular activity of (~25 glomeruli/AL) (Fig. 1A). Calcium signal was recorded upon stimulation with two monomolecular odorants (1-hexanol, 1-heptanol) and a complex fragrance (peppermint oil) on a 5s/25s ON/OFF conformation for 20 trials (Fig. 1B-D). Fast calcium imaging revealed the high variability of odorant-elicited glomerular response profiles.

First, we assessed the stability of odorant representations across time and trials given the duration of each imaging session (30 min) and the multiple stimulus repetitions. For each individual, we calculated the Pearson's correlation coefficients across pairs of trials of the glomerular response vectors before, during and after olfactory stimulation. Figure 1E shows that across-trial correlation is elevated during odour arrival and still ~0.5 after stimulus termination. This shows that, in our experimental conditions, odour coding was stable throughout the entire imaging period.

All odorant response traces were pooled together (Fig. 1F) and clustered according to their direction with respect to the pre-stimulus baseline (i.e., excitatory or inhibitory) and duration (Fig. 2), see Materials and Methods), allowing for a statistical description of glomerular responses across individuals and odorants, and for the identification of classes of recurrent profiles (Fig. 2A,B). The most common profiles comprised inhibitory (group 1) and excitatory (group 4) responses lasting the entire olfactory stimulations and terminating after odour offset. In some cases, they could result in prolonged inhibition (group 2) or excitation (group 6) lasting after stimulus offset or could be followed by a post-stimulus excitatory (group 3) or inhibitory activity (group 5). Approximately 15% of responses consisted of short phasic excitation followed by prompt signal termination (group 7) or even by an inhibitory response (group 8). About 15% of all recorded glomerular traces showed no detectable odorant-induced activity (group 9). Of all traces, 48% were excitatory, 37% inhibitory, and 15% unresponsive (Fig. 2C,D). Moreover, 251 glomerular responses out of 546 (46%) displayed an inhibitory component either during or after olfactory stimulation.
<figure><img src="image_6.png" /><figcaption>Figure 1.</figcaption></figure>

Projection Neurons Calcium Imaging Analysis

(A) For each AL, the response maps to all three odorants were overlaid to highlight glomerular structures. Glomeruli were hand-labelled for time-series extraction. V, ventral; L, lateral.

(B) Exemplary AL odorant responses during olfactory stimulation with three odorants. Colorbar indicates the relative change of activity during olfactory stimulation with respect to the pre-stimulus baseline. Circular areas indicate identified glomeruli according to (A).

(C,D) Temporal profiles of the glomerular regions identified in (A) for the three odorants. Each line in (C), labelled from 1 to 28, refers to the glomerular ID in (A). Temporal profiles represent the average activity of 20 stimulations. Dashed lines in (C,D) limit the begin and the end of the olfactory stimulation.

(E) Matrix of Pearson's correlations between pairs of glomerular response vectors across all repetitions. The upper right part of the matrix shows correlation scores among glomerular activity during olfactory stimulation (t = 1 to 4 s after odorant onset) across trials; the bottom left part shows correlation scores among glomerular activity before stimulation (t = -1 to 0 s) across trials. On the right, the mean (± s.e.m.) across-trial correlation before, during and after (t =6 to 9s) stimulation is shown.

(F) All glomerular responses from 8 ALs to 3 odorants were pooled together to provide an overview of the complexity of response profiles (n = 546).
<figure><img src="image_7.png" /><figcaption>Figure 2.</figcaption></figure>

Clustering of Projection Neurons’ Response Profiles

(A) All glomerular responses were clustered with supervision according to their activity (excitatory, inhibitory, non-
responsive) during and after stimulus arrival.

(B) Mean ± s.e.m. of all curves of the relative groups in (A). The green patch indicates the stimulus delivery interval.

(C) Average curves of all response groups from (B) are superimposed. The grey patch indicates the stimulus delivery interval.

(D) Relative amount of all response categories.

(E) Latency of glomerular responses for excitatory and inhibitory profiles (groups 1 to 8). For groups 7 and 8, the latency of short excitatory response's termination was calculated. Letters refer to significative groups after Kruskal-Wallis statistical test and Tukey-Kramer correction. On the left side, exemplary traces of phasic and tonic excitatory responses and an inhibitory response are shown. The orange line indicates the latency of response onset or termination.
<figure><img src="image_8.png" /><figcaption>Figure 3.</figcaption></figure>

A Simple Neural Network Model for Olfactory Coding and Learning.

(A) The synaptic connectivity between AL PNs and KCs is built as a pseudorandom logic matrix where each column represents KC dendritic arborisation and each row PN axon terminals. Each KC receives synaptic contact – here represented as a white circle – from 30% of the PN population.

(B) For each timepoint *t* of a calcium imaging recording, the measured activity level for each glomerulus (represented by a PN in the model) is projected onto all its synaptic connections with the KC population. Each KC, i.e. each column in the scheme, integrates all excitatory post-synaptic potentials. Finally, recurrent inhibitory feedback is simulated by imposing that only the 10% most active KCs will generate an action potential (AP) (red cells), while all others remain silent (grey cells).

(C) A single appetitive MBON receiving input from all KCs was modelled. Based on a synaptic plasticity threshold parameter, all synapses from KCs to MBON that are activated with a frequency greater than allowed by the defined threshold during the learning window will be switched off. This phenomenon results in a decrease AP probability in the modelled MBON upon stimulation with the learned stimulus. Abbreviations: action potential, AP; antennal lobe, AL; excitatory post-synaptic potential, ePSP; Kenyon cells, KC; mushroom body, MB; mushroom body output neuron, MBON; projection neurons, PN.
$\text{rate}_{\text{OFF}_{5-10s}} = 0.25 \pm 0.04$; turnover rate$_{\text{OFF}_{10-15s}} = 0.33 \pm 0.08$). Correlation analysis of stimulus-
induced responses (**Fig. 5D,E** ☐) showed a high correlation of neural activity across time points
during olfactory stimulation and during the post-odour window both in the recorded PN
population and in the simulated KCs. This indicates that the neural representation of an odorant is
sufficiently stable to provide a neural image, which is coherent with itself during a 5-s stimulation
window and during a ~10-s post-stimulation window. Furthermore, the across-odorants
correlation is rather high in the PN space (Pearson's correlation coefficient, *r* = 0.4), but proximal
to zero in the KC space, supporting the notion that the MB network increases stimulus
discriminability, not only during but also after odour offset.

To further corroborate this idea, we compared the temporal relative trajectories obtained for the
three odorants in the measured PN space and in the modelled KC space (Fig. 4F ☐). In both cases,
response trajectories diverged at stimulus onset and returned to the centre of space after stimulus
termination. However, the principal component analysis showed that odorants are better
separated from each other and that they remained separated for a longer time in the KC space
compared to the PN space. This indicates improved and prolonged stimulus discriminability in the
MB with respect to the AL (Fig. S1 ☐).

We next investigated the discriminatory power of the system across a larger set of odour response
profiles. For this, we pooled together all response profiles acquired during the calcium imaging
analysis (Fig. 1F ☐) and we artificially combined them to simulate 100 odorant response vectors,
each comprising 30 different glomerular profiles (see Methods). Using these artificially combined
odorant response profiles, we simulated the relative KC response time series and measured the
correlation across all response vectors in the PNs (experimental data) and KCs spaces (modelled
data) (Fig. 4G ☐). Notably, while response vectors calculated from PN data were highly correlated
(r = 0.71 ± 0.08), such a correlation significantly decreased in the modelled KC space (r = 0.52 ±
0.10; Student's t-test between r distributions of PN and KC data: *p* ≈ 0, *n* = 4774), indicating that the
signal transformation operated by the proposed MB neural architecture induces a strong
decorrelation among odour signatures.

Overall, the proposed MB network can process experimentally acquired input data and produce
physiologically plausible KC response patterns coherent with *in vivo* KC activity measurements
(Lüdke et al., 2018 ☐). Moreover, it accounts for the expansion of the coding space from the AL to
the MB (Lin et al., 2014 ☐; Papadopoulou et al., 2011 ☐; Stopfer, 2014 ☐), enhancing inter-
stimuli decorrelation and confirming previous theoretical works based on artificial datasets
(Olshausen and Field, 2004 ☐; Peng and Chittka, 2017 ☐). Importantly, these emergent
properties of the model architecture are based on the known neuroanatomy and physiology of the
insect brain and have been obtained without any optimisation aiming at reproducing specific
features of olfactory coding.

The neural network model predicts appetitive behaviour

Appetitive classical conditioning relies on the coincidental activation of the neural elements
representing the conditioned stimulus (CS) and the unconditioned stimulus (US). The latter is
mediated by various neuromodulators, such as specific dopaminergic, as shown in the fly (Burke
et al., 2012 ; Liu et al., 2012 ), or octopaminergic neurons, as demonstrated in the honey bee
(Hammer, 1993 ).

The coincidental activation of CS and US neural elements induces plastic modulations in the MB
output neurons (MBONs) (Hige et al., 2015 ; Okada et al., 2007 ; Owald et al., 2015 ). Here,
we assessed if the proposed MB model could reproduce empirical measurements of appetitive
learning by introducing a time window, during which the weights of the recruited KC-to-MBON
synapses could be downregulated from 1 to 0 (Ardin et al., 2016 ). This rule reflects MBON
learning-induced plasticity, according to which they display a broadly tuned response, which is
reduced in presence of a learned stimulus (Amin and Lin, 2019 ; Aso et al., 2014a ; Aso et al.,
<figure><img src="image_9.png" /><figcaption>Figure 4.</figcaption></figure>

Olfactory Representation in Modelled Kenyon Cells

(A) The first row displays the recorded PN responses of one honey bee to three odorants. The second row shows the transformation of the PN activity operated by the model to simulate the activity of 1000 KCs. In each plot, KCs are ordered for response strength during the onset and offset to better visualise activity clusters.

(B) Turnover rate of recruited KCs across adjacent time points. Light blue traces refer to individual bees (mean of 10 MB simulations). Thick, dark blue traces indicate average curves across bees.

(C) Cumulative recruitment of KC shows two main recruitment events at stimulus onset and offset. To observe odorant-related KC recruitment dynamics, the cumulative sum was initiated at stimulus onset. Light blue traces refer to individual bees (mean of 10 MB simulations). Thick, dark blue traces indicate average curves across bees.

(D,E) Correlation matrix among time points of measured PN activity (C) and simulated KC activity

(E). Mean responses to the three odorants were concatenated to allow observing within and across odorant correlations.

(F) PCA of odorants' trajectories in the measured PN space (top) and in the modelled KC space (bottom) for four exemplary bees. Trajectories comprise a 10-s interval, ranging from odour onset (t = 0 s, light) to 5 s after offset (t = 10 s, dark).

(G) The raster plot shows a set of 100 artificially combined glomerular responses (top), each simulating the neural representation of 100 similar odorants in the PN space. A second raster plot (middle) shows the representation of the same odorants in the modelled KC space. Note that odorant representation was considered as the mean activity of PN (or KC) during stimulus arrival (0.5 to 5 s). The histogram shows the distributions of between-odorant correlations computed among all pairs of odour vectors in the PN and KC space. A significant difference between the two distributions was tested with a Student's t test (*p* ≈ 0, *n* = 4774).
<figure><img src="image_10.png" /><figcaption>Figure 5.</figcaption></figure>

Modelled Mushroom Body Output Neuron Predicts Appetitive Behavior

(A) Three experimental protocols differing for CS/US inter stimulus interval (ISI) were used for training the model (B-D) and for behavioural measurements of the proboscis extension reflex conditioning (E,F). Trained simulated MBONs, as well as conditioned honey bees were tested against the CS (blue) and a novel odorant (NOd, green).

(B) Time course of action potential (AP) probability of a trained MBON upon stimulation with the CS (blue) or with the novel odorant (NOd, green). Olfactory learning was modelled according to *backward* (top), *early* (middle) and *delay* (bottom) CS/US contingencies. Glomerular responses for 8 bees to 1-hexanol and peppermint oil were used alternatively as CS and NOd. Data for responses to CS and NOd were pooled together (n = 16 traces for each condition. Blue traces, CS; green traces, NOd). The thick trace shows the average AP probability profile; dotted vertical lines delimit the stimulation interval; grey bar delimits the simulated learning window.

(C) Distribution of the mean values of traces in (B) during CS and NOd stimulation. Because appetitive learning produces a decrease in MBON firing rate, the complementary value of the probability fraction is used as a proxy for a learned appetitive response ($p_{backward} = 0.06$; $p_{early} = 1.41*10^{-6}$; $p_{delay} = 1.04*10^{-5}$; Kruskal-Wallis statistical test; n = 16).

(D) Latency of 90% of minimal MBON activity in response to the CS+ for early and delay conditioning protocols (Kruskal-Wallis test for difference in distribution across protocols, *p* = 0.002; Barlett's test for variance difference across protocols, *p* = 0.0109).

(E) Memory retention test of honey bees 1 h after absolute conditioning. Bars indicate the percentage of individuals showing proboscis extension reflex (PER) when presented with the conditioned or the novel odorant. Error bars indicate 95% confidence intervals. (Responses to the CS/NOd were compared with a McNemar test for binomial distribution; $p_{early} = 0.003$, $p_{delay} = 0.004$)

(F) Latency of the PER to the conditioned stimulus at the 1 h memory retention test (Kruskal-Wallis test for difference in distribution across protocols, *p* = 0.036; Barlett's test for variance difference across protocols, *p* = 5*10⁻⁶; $n_{early} = 26$; $n_{delay} = 20$).
2014b ⌜; Cognigni et al., 2018 ⌜; Cohn et al., 2015 ⌜; Hige et al., 2015 ⌜; Lyutova et al., 2019 ⌜; Okada et al., 2007 ⌜; Owald et al., 2015 ⌜). To implement such a rule, we introduce a synaptic plasticity threshold (*spt*) parameter, which defines the number of firing events of a given KC upon which its synaptic output weight is reduced from 1 to 0. In other words, it determines how active a synapse should be during the learning window before being switched off.

It is well known that the inter-stimulus interval (ISI) – i.e. the time elapsed from CS to US onsets – can influence the efficiency of Pavlovian learning (Domjan, 2015 ⌜; Holland, 1980 ⌜). The calcium signal dynamics and modelling analysis presented here (Fig. 4A-F ⌜) suggest that such an ISI-dependent effect is the consequence of the strong temporal dynamics of the odorant neural representation. As the neural representation of the CS changes in time, a reward system recruited at different time points will interact with a different population of CS-recruited KCs. Because learning results from the coincidental activation of CS and US elements (Pavlov, 1927 ⌜), an individual should respond to the different time points of an olfactory stimulation with different strengths, depending on the US arrival time experienced during conditioning. To test this hypothesis, learning-related MBON plasticity was modelled for three different CS/US temporal contingencies reflecting three conditioning paradigms, well-studied in honey bee (Giurfa and Sandoz, 2012 ⌜) (Fig. 5A ⌜). Having a fixed CS stimulation interval from *t* = 0 to 5 s, and a learning window (US) of 3 s, we simulated MBON learning with three different ISIs. *Backward pairing* occurred when the US initiated at *t* = -2 s (i.e. 2 s before CS onset), *early pairing* when it started at *t* = 1 s (1 s after CS onset), and *delay pairing* when it started at *t* = 4 s (4 s after CS onset). First, we trained the model with the experimental PN response profile recorded for either 1-hexanol or peppermint oil (see Fig. 1 ⌜). One odorant was used as CS and the other as a novel odorant to test the specificity of the learning obtained under these experimental conditions. After learning, we modelled the MBON action potential (AP) probability upon the presentation of the CS (e.g. 1-hexanol) and the novel odorant (e.g. peppermint) and repeated this operation for the response profiles of all individuals. To account for the biological variability in the neural activities elicited by repetitions of the same stimulus, learning was assessed against the KC response profiles resulting from five CS and five novel odorant presentations, which were not included in the training dataset. As expected, we found that a trained MBON showed a larger decrease in firing probability when presented with the CS than with the unfamiliar stimulus, demonstrating learning (Fig. 5B ⌜ and C). Interestingly, the model predicts weak learning in the case of *backward pairing* – that is when US and CS are partially overlapping, but the US begins before the CS – whereas both *early* and *delay* conditioning produced a CS-specific down-regulation of MBON firing probability. The model also predicts that high synaptic plasticity sensitivity (low *spt* values) facilitates non-specific learning, resulting in a strong generalisation effect. Conversely, MBON plasticity under high *spt* values requires a robust and stable KC activation, a situation that is not compatible with a learning interval that is only partially overlapping with the stimulation window (note that measurements are provided at 20 Hz, and in this context, a *spt* = 40 requires a synapse to be activated during two whole seconds – although not continuously – to be switched off) (Fig. S2 ⌜).

Stimulus representation is dynamic (see Fig. 1 ⌜ and 2 ⌜). Hence, our model predicts that an MBON exposed to an *early* protocol should learn to identify the early components of the stimulus neural signature. Indeed, the model shows that the learned response (a down-regulation of MBON firing probability) occurs earlier in the *early* than in the *delay* contingency (Wilcoxon test for non-parametric paired data, *p* < 0.001; *n* = 16) and with lower response latency variability (Barlett's test, *p* < 0.011) (Fig. 5D ⌜). To test the model's predictions, we measured the timing of real behavioural responses (i.e. the proboscis extension) of honey bees trained under the same CS/US conditions and tested 1 hour later with the CS and a novel odorant. Because the model's predictions were generated based on the odour response maps elicited in the AL by 1-hexanol and peppermint oil, the same odorants were used for the behavioural experiments. In agreement with the model's predictions and previous reports (Felsenberg et al., 2014 ⌜), backward conditioning led to scarce and unspecific responses (Fig. 5E ⌜). In contrast, positive ISIs of 1 or 4 seconds (*early*
and 2 ☐). Hence, our model predicts that an MBON exposed to an *early* protocol should learn to identify the early components of the stimulus neural signature. Indeed, the model shows that the learned response (a down-regulation of MBON firing probability) occurs earlier in the *early* than in the *delay* contingency (Wilcoxon test for non-parametric paired data, *p* < 0.001; *n* = 16) and with lower response latency variability (Barlett's test, *p* < 0.011) (**Fig. 5D ☐**). To test the model's predictions, we measured the timing of real behavioural responses (i.e. the proboscis extension) of honey bees trained under the same CS/US conditions and tested 1 hour later with the CS and a novel odorant. Because the model's predictions were generated based on the odour response maps elicited in the AL by 1-hexanol and peppermint oil, the same odorants were used for the behavioural experiments. In agreement with the model's predictions and previous reports (**Felsenberg et al., 2014 ☐**), backward conditioning led to scarce and unspecific responses (**Fig. 5E ☐**). In contrast, positive ISI's of 1 or 4 seconds (*early* and *delay* protocols) induced specific memory formation (McNemar test, *p*<sub>backward</sub> = n.s., *p*<sub>early</sub> = 0.003, *p*<sub>late</sub> = 0.004), with ~90 to 70% of the trained bees showing conditioned responses to the CS and ~40% showing a generalised response to the unfamiliar stimulus during the test. Video tracking of the proboscis extension response showed that bees receiving the reward 1 s after CS onset extended the proboscis earlier and with less variability than those that received the reward towards the end of CS delivery (Kruskal-Wallis test, *p* = 0.036; Barlett's test for data variance comparison, *p* = 5e10⁻⁶; *n*<sub>early</sub> = 26, *n*<sub>delay</sub> = 20) (**Fig. 5F ☐**).

Discussion

Our results show that a simple but biologically realistic MB neural network model simulating
essential aspects of MB connectivity can process real neurophysiological input and generate
robust predictions that agree with empirical observations. Our model was fed with experimental
PN responses acquired at high temporal resolution (Bestea et al., 2022 ; Paoli and Haase,
2018 ). This approach provided a better description of the temporal dynamics of olfactory coding
and a more realistic modelling of odour signal transformation and learning.

Olfactory trajectories in the glomerular
space are shaped by local inhibition

Because of the high temporal resolution of the functional imaging recordings, we could reliably
measure odorant-induced latencies of excitatory and inhibitory PN responses. Excitatory
responses occurred earlier than inhibitory ones and exhibited a smaller temporal variability. This
suggests that excitation of PNs occurs in a single wave upon cholinergic input from the OSNs to the
glomeruli of the antennal lobe, with a minor contribution – if any – of second-order excitatory
local interneurons within the AL, which have been reported for *Drosophila* (Chou et al., 2010 ;
Olsen et al., 2007 ) but so far not for honey bees (Girardin et al., 2013 ; Krofczik et al.,
2009 ; Schäfer and Bicker, 1986 ). This finding also advocates that odour responses of OSNs
may not display onset-latency variability as shown in the locusts (Kim et al., 2023 ). On the other
hand, inhibitory PN responses and the termination of early excitatory PN responses occurred
within the same time window, approx. 40 ms after the onset of excitatory responses, suggesting
that lateral inhibition within the AL is responsible for triggering PN inhibition and terminating
early excitatory responses (see Fig. 2 , groups 7 and 8). This is consistent with a significant
presence of GABAergic local interneurons in the AL (Schäfer and Bicker, 1986 ), which reshape
the olfactory message conveyed to higher-order brain structures such as the MBs and the lateral
horn (Paoli and Galizia, 2021 ). Notably, we observed that almost 50% of PN responses recorded
contain an inhibitory component (see Fig. 2A , groups 1-3,5,7,8), underlining the dominant role
of inhibitory AL interneurons in shaping odour trajectories in the glomerular space. These
observations are in contrast with previous calcium imaging analyses, where only 2-10% of
responses had an inhibitory nature (Krofczik et al., 2009 ; Szyszka et al., 2005 ), but coherent
with electrophysiological recordings showing a rich response diversity and prominence of
inhibitory activity (Wilson et al., 2004). Differently from *Drosophila*, where both local inhibitory and excitatory interneurons contribute to the olfactory tuning (Chou et al., 2010; Huang et al., 2010; Shang et al., 2007; Yaksi and Wilson, 2010), our analysis indicates that, in the honey bee AL, glomerular responses are modulated mainly by a local inhibitory network. This conclusion is supported by a previous study showing that pharmacological activation of a single glomerulus by topical acetylcholine application provokes inhibitory but not excitatory responses in other glomeruli (Girardin et al., 2013). Indeed, while the presence of excitatory local interneurons have been predicted (Malaka et al., 1995), such prediction has never been supported by experimental evidence.

Overall, these findings highlight possible differences between the ALs of bees and fruit flies regarding the nature of the local interneuron populations modulating glomerular activity. Thus, they cast caution in prioritising a single species as a representative general model of insect olfaction.

## Smell and aftersmell: An odorant-specific afterimage

Almost 50% of all PN responses in our database displayed either a prolonged post-stimulus activity (see Fig. 2, groups 2 and 6) or a change in response direction after stimulus termination (groups 3, 5, 7, 8). This results in the recruitment of an offset-specific population of KC – both in our model and in the literature (Ito et al., 2008; Lüdke et al., 2018) – that creates a neural image of the post-odour in the MB. Thus, the post-odour representation is different from the neural image of the odorant itself and provides a stable, slowly decaying after-image, which is stimulus-specific and temporally associated with the neural correlate of the odorant itself. This is in agreement with the analysis conducted by Patterson et al. (Patterson et al., 2013) on the olfactory representation in M/T cells in mice, which shows that the post-odorant activity is rather conserved and stimulus-specific even across concentrations and for about a 10-s window. Our modelling analysis predicts that the after-stimulus signature of an odorant is more distinct and stable across time in the MB than in the AL (Fig. S1). This could occur because the activity across the KC population is normalised by the MB feedback neurons operating a gain control, which stabilises odorant-responses signature in the MB – including the post-odorant activity – while the input signal in the PN is fading.

The existence of a post-odour signature has important implications for understanding the neurobiological basis of trace learning (Dylla et al., 2013; Paoli et al., 2023a), a conditioning protocol in which a stimulus-free time window separates CS and US. Previous studies reported that an odorant cannot be predicted by its post-odour signature (Lüdke et al., 2018; Szyszka et al., 2011). Nevertheless, the post-odour image is stimulus-specific and always temporally contiguous to the neural representation of the odorant itself. As shown in previous studies (Galili et al., 2011; Lüdke et al., 2018; Patterson et al., 2013; Szyszka et al., 2011), the neural activity associated with the OFF response is stimulus-specific and reproducible, suggesting that the post-odour could be physiologically similar to the onset of a second odorant (in the same way as the back-taste of wine could remind us of an unrelated ingredient). If so, the odour/post-odour pattern could be processed as an individual neural object, effectively extending the duration of the nominal, conditioned odour. Interestingly, the duration of the post-odour neural signature reported by us and by previous studies (Galili et al., 2011; Patterson et al., 2013; Szyszka et al., 2011) is about 10 to 15 s. As such, it is compatible with the duration of the CS/US gap in trace learning experiments (Galili et al., 2011; Paoli et al., 2023a; Szyszka et al., 2011), suggesting the possibility of its implication in this form of learning.
Mushroom body modelling predicts associative learning scores
and response latency for different conditioning protocols

To investigate if the proposed neural network model explains the temporal features of associative
learning, we simulated CS delivery by feeding the model with experimental PN calcium imaging
time series and applied a learning rule based on the downregulation of recurrently recruited
synapses in the MB. A learning time window of 3 s was defined according to three well-studied
protocols for associative conditioning, which correspond to three different ISI situations (-2s, +1s
and +4s) (Bitterman et al., 1983 ; Giurfa and Sandoz, 2012 ). A simulation of the action
potential probability of an MBON after olfactory conditioning revealed that differentiation
between the CS and a novel odorant was observable whenever the CS anticipated the US (ISIs of
+1s and +4s) but less successful in the backward protocol (IS of – 2s). Generalisation errors were
also observed in a similar way as in behavioural experiments. Notably, in this study, we adopted a
backward contingency with a 1-second overlap between the US and the CS (Fig. 5A ). This differs
from the more traditional configuration, where the CS is delivered after US termination, that is,
without any temporal overlap. In this case, the model would predict no learning at all, as observed
in honey bee behavioural experiments (Felsenberg et al., 2014 ; Hellstern et al., 1998 ).
Hence, we tested the model's prediction in the form of backward conditioning where US and CS
are partially overlapping. In this case, the model predicts a weak appetitive association (see Fig.
5B,C,E ). This result is coherent with a previous behavioural report, where bees subject to a
similar protocol showed a weak response to the conditioned odorant (Felsenberg et al., 2014 ).
In the early and delay configurations, a CS-specific associative memory was formed, both in the
model (Fig. 5C ) and in the behavioural tests (Fig. 5E ). However, the model predicts a
difference in response latency and latency variability depending on relative CS/US arrival (ISI of
+1 or +4 s): a learning window placed closer to stimulus onset (early protocol) resulted in early and
temporally precise MBON responses, whereas a delayed learning window produced a similar
learning score, but with higher response latencies and temporal uncertainty

Experimental results based on the same odorants that were fed to the model confirmed the
model's predictions. They showed that the two forward CS/US contingencies (i.e. *early* and delay
protocols, with the CS anticipating the US) yielded comparable results in terms of learning score
and CS-specificity while differing in terms of CS latency and precision (Fig. 5C-F). These
observations support the idea that associative learning occurs upon coincident detection of the CS
and US neural elements. In fact, because the CS neural representation changes dynamically during
the olfactory stimulation, a positive association with the initial odour signature will trigger an
earlier and temporally precise conditioned response. Conversely, the association with the later
part of the olfactory signature will elicit a delayed response with greater temporal variability.

Encoding time without encoding time

It has been reported that mammals can encode the CS/US interval time, allowing them to modulate
the latency of a conditioned response (Kirkpatrick and Balsam, 2016). However, it is still
unclear whether also smaller, less complex insect brains are capable of time tracking. Previous
studies have shown that the duration of the inter-stimulus interval experienced during learning
can influence the conditioned response's latency in bumble bees (Boisvert and Sherry, 2006)
and honey bees (Szyszka et al., 2011). Both studies indicate that bees trained with a longer ISI
exhibited a larger latency in their response to the conditioned stimulus. This provided clear
evidence that CS-related information can modulate response timing. However, the appetitive
response largely anticipated the expected reward time, and a clear interpretation of time tracking
in insect brains is still missing

Our model shows that this time contingency is naturally encoded, simply because the olfactory
representation in the KCs evolves through time in a deterministic fashion. Thus, what is learnt at
the time of the US presentation is the KC configuration of the CS at this particular time. In other
words, the MBON learns which subpopulation of KC is active during reward arrival, rather than
the expected reward time. Interestingly, because the KC representation of the CS does not evolve linearly with time (**Fig. 4A-C** ☐), the CS/US time contingency is not encoded perfectly. Remarkably, the model captures the experimental measurements of the proboscis response latency in bees, which indeed tend to respond earlier than the expected reward (**Fig. 5D,F** ☐).

As often in insect research, heuristics can provide simple solutions to apparently complex cognitive problems. In this case, a mechanism based on the temporal dynamics of the conditioned stimulus, explains the bees' systematic errors at encoding proper CS/US contingency. Whether, in addition to this process, other mechanisms enable bees to track time contingencies remains to be seen.

## Conclusive remarks

In this study, we adopted a fast calcium imaging approach to investigate the dynamical representation of odorants in the antennal lobe. Our findings revealed that about 50% of glomerular responses had an inhibitory component and that local inhibition played a crucial role in shaping the initial PN activity and defining odour trajectories in the glomerular space. We observed that half of the PN response profiles prolonged their activity or changed response direction after stimulus termination, generating a stable, odorant-specific after-smell. Although the after-smell might be very different from the odour signature, it is specific and temporally contiguous, thus providing the means to prolong the neural representation of the stimulus itself. To investigate the logic of olfactory transformation and learning in the mushroom body, we constructed an MB neural network model based on the known connectivity in the insect brain. We showed that the model can process experimentally acquired input data and produce realistic KC response patterns. Our analysis suggests that the mushroom bodies enhance olfactory discrimination ability and stabilise the olfactory after-image, effectively prolonging the stimulus signature after odour offset.

Finally, we implemented a learning rule to modulate MB output neurons firing rate based on associative learning. We verified the model's predictions with behavioural measurements on real honey bees subjected to different learning protocols. The coherence between measured and predicted behavioural scores suggested that the rules behind this neural model – *i.e.* random connectivity, feedback inhibition, and MBON plasticity – are sufficient to generate relevant predictions for associative learning upon different learning protocols. In addition, by testing the model against physiological recordings of different stimulations, we showed that the model's predictions are robust to noise and biological variability.

Because the model incorporates not only neural assumptions pertaining to the honey bee olfactory system but also to the fruit fly and locust olfactory systems, similar principles may guide odour signal transformation at the level of the MB in appetitive associative learning across insects. This generality does not apply, however, to the nature of odour reshaping by local interneurons observed in the AL of bees, which seems to differ from that occurring in flies, thus highlighting the fact that convergence, but also differences, can characterise the functional architectures of olfactory systems in different insect species.

## Methods

### Experimental Model

Experiments were performed on honey bees *Apis mellifera* reared in outdoor hives at the experimental apiary of the Research Centre on Animal Cognition (CNRS, Toulouse, France) situated in the campus of the University Paul Sabatier. In all cases, honey bee foragers (>3-week old) were used. No institutional permission is required for experimental research on honeybees
## Projection neurons labelling

Honey bee foragers were collected the day before the experiment at an artificial feeder, to which they were previously trained, and projection neurons were labelled for calcium imaging analysis as previously described (Paoli et al., 2017 ⚙️; Sachse and Galizia, 2002 ⚙️). Shortly, bees were fixed in a 3D-printed holder with soft dental wax, antennae were blocked frontally with a drop of eicosane (Sigma Aldrich, CAS: 112-95-8). A small window was opened in the head cuticle, and glands and tracheas were displaced to expose the injection site. The tip of a borosilicate glass needle coated with Fura-2-dextran (Thermo Fisher Scientific Inc.) was inserted between the medial and lateral mushroom body calyces, where medial (m-ACTs) and lateral antenna-cerebral tracts (l-ACTs) cross. After dye injection, the head capsule was closed to prevent brain desiccation, and bees were fed *ad libitum* with a 50% sucrose/water solution. On the following day, antennal lobes were exposed to allow optical access, and the brain was covered in transparent two-component silicon (Kwik-Sil, WPI). Although the injection procedure may result in variable levels of PN labelling, the reproducibility of response amplitudes and the lack of labelling bias for specific glomeruli indicate that the loading procedure is reproducible.

## Calcium imaging analysis and signal processing

Undiluted solutions of 1-hexanol, 1-heptanol and peppermint oil (Sigma-Aldrich) were delivered to the bees using an Arduino Uno®-controlled automated olfactometer (Bestea et al., 2022 ⚙️; Raiser et al., 2017 ⚙️). Odorants were alternated and presented 20 times on a 5/25 second ON/OFF configuration. Calcium imaging recordings were conducted with a straight Leica SP8 scanning microscope (Leica Microsystems, Germany) equipped with a SpectraPhysics InSight X3 multiphoton laser tuned at 780 nm for Fura-2 excitation. All images were acquired with a water immersion 16x objective (Leica HC FLUOTAR 16x/0.6 IMM CORR, Leica Microsystems, Germany), at 64×64 pixel resolution and ~127 Hz.

Calcium imaging data were analysed with custom-made MATLAB (MathWorks Inc.) scripts. The baseline signal, calculated as the mean fluorescence during the one second before stimulus onset, was used to calculate baseline-subtracted and normalised stimulus-induced glomerular activity ($\Delta F/F$). The normalised activity was multiplied by -1 to display excitatory/inhibitory responses as positive/negative relative changes ($-\Delta F/F$). Time series of glomerular activity were averaged across stimulus repetitions for response profile clustering analysis. Glomeruli were hand-selected based on a morphological and functional map. Glomerular response profiles were calculated as the mean intensity of an area of 5-by-5 pixels around the centre of the glomerulus across time. Stimulus representation stability (Fig. 1E ⚙️) was assessed with a Person's correlation analysis between vectors of the mean glomerular response of different trials. Glomerular response vectors' stability was calculated before, during and after olfactory stimulation.

## Mushroom Body Modelling

We used Matlab R2018a to investigate how the recorded PN activity would affect KC responses of a MB network model.

## Projection neurons to Kenyon cells connectivity

Real projection neurons' activity acquired *via* calcium imaging analysis provided the input to a simple model of the mushroom body neural circuits. For each experimentally measured honey bee, approx. 26 projection neurons (this number depends on the number of detected glomeruli in the relative calcium imaging experiment) converge onto 1000 modelled KCs. Connections between PNs and KCs were modelled as pseudo-random, with each KC receiving connections from approx. 8 PNs (30% of the input PN population), as observed in bees (Szyszka et al., 2005 ⚙️) and flies (Ashok Litwin-Kumar et al., 2017 ⚙️; Caron et al., 2013 ⚙️). PN-to-KC connections were assumed to be binary: either 0 (not connected), or 1 (connected), and the connectivity scheme is stable in a given MB model and not subject to plastic changes. This was implemented as a logic connectivity
matrix (W_PN_to_KC) of size number_of_PNs × number_of_KCs. Experimental PNs activity (PNactivity) was resampled at 20 Hz for modelling analysis (50 ms temporal resolution) to match the 20-Hz oscillatory cycle detected in the MB (Cassenaer and Laurent, 2007 ; Laurent and Naraghi, 1994 ; Popov and Szyszka, 2020 ). For every time step t, the integrated input to each KCs (KCepsp) was calculated as the matrix product of the input neuron activity vector (PNactivity) and the logical connectivity matrix. This integrated input of each KC is analogous to its excitatory post-synaptic potential (EPSP).

$$
KC_{epsp}(t) = PN_{activity}(t) \times W_{PN\_to\_KC}.
$$

Winner-takes-it-all inhibitory feedback element

Whether a KC will fire an action potential depends not only on the sum of its input signals but also on the inhibitory activity of the MB feedback neurons (Rybak and Menzel, 1993 ; Zwaka et al., 2018 ). These neurons act on the whole KC population, providing that the KCs receiving the highest summed input will produce an action potential (AP). To account for such effect, and based on known MB neurophysiology (Honegger et al., 2011 ; Peng and Chittka, 2017 ; Turner et al., 2008 ), we imposed that only the 10% most active KCepsp (i.e. receiving the strongest summed input) will fire an action potential.

$$
\begin{align*}
& \text{If } KC(i)\text{epsp}(t) > 10^{th} \text{ percentile of } KC\text{epsp}(t), \text{ then } KC(i)\text{activity}(t) = 1; \text{ else } KC(i)\text{activity}(t) \\
& = 0
\end{align*}
$$

This outputs a binary vector of firing pattern across the whole KC population (KCactivity), where ones correspond to action potential and zeroes to silent KC. This operation is repeated for all epochs to obtain the time course of the activity of the KC population before, during and after odour arrival. To ensure the robustness of the model and account for the variability of the initial parameters, the MB simulation was run 10 times for each experimental AL input dataset. The results were then averaged over the 10 MB network simulations.

KC-to-MBON model and learning-induced plasticity

Evidence in *Drosophila* and in the honey bee suggests that the default state of an appetitive MBON – i.e. an MBON departing from an MB lobe innervated by a dopaminergic neuron conveying reward information – is to be broadly responsive to neutral stimuli (Okada et al., 2007 ; Owald et al., 2015 ). Under the assumption that similar principles are highly conserved among insects, we modelled an MBON receiving connections from all KCs and with all connections having an initial synaptic weight = 1 to reflect a generalised odour tuning. This provides a broadly-tuned MBON that responds equally to any pattern of KC activity. Upon learning, MBON synapses that are repeatedly recruited are switched off (= 0). As a consequence, a trained MBON retains a high AP probability in the presence of an unfamiliar stimulus, but a lower one in response to the conditioned odorant (or to an odorant that is represented by a similar KC population). Two parameters guide learning: a synaptic plasticity threshold (spt) and a learning window. The variable spt defines the number of firing events needed for a given KC to induce its output synaptic depression (i.e. KC(i)_to_MBON synaptic weight switch from 1 to 0). For the learning tests, a spt value of 15 was adopted. This value falls in the middle of the range tested in our simulations (from 1 to 40). It is stringent enough to hinder plastic modulation of rarely recruited KCs, while still requiring repetitive activation (i.e. a synapse must be active at least 750 ms during a 3-s learning window) before allowing learning-induced plasticity.

The learning window provides a temporal restriction of such a learning rule, analogous to the reward/US delivery window in an appetitive conditioning protocol. Different CS/US contingencies were tested. Inter-stimulus intervals (ISI) of -2, 1 and 4 were used, where such values indicate the onset of a 3-s US window with respect to the onset of a 5-s CS window. Throughout the manuscript,
we referred to the three learning protocols as *backward* (ISI = -2), *early* (ISI = 1), and *delay* (ISI = 4). The training was modelled using the mean glomerular responses to the first 5 stimulations of 1-hexanol or peppermint oil; memory retention was tested against the next 5 stimulations of both odorants (one acting as the conditioned stimulus, the other one as the novel odorant).

## Odorant response profiles simulation

The response profile database (Fig. 1F ⚙️) was repeatedly subsampled to generate 100 odorant response maps, each comprising 30 glomerular responses. The proportion of response types (inhibitory, excitatory, non-responsive, etc.) was maintained constant and equal to the mean proportions across the entire population to generate a batch of similar glomerular response maps (representing perceptually similar odorants (Guerrieri et al., 2005 ⚙️)) (Fig. 4G ⚙️). Projection neurons (or KC) response vectors used for the Pearson correlation analysis are constituted by the average response for each PN (or KC) during olfactory stimulation (t = 1 to 4 s).

## Proboscis extension response latency

Bees were collected in the afternoon at the institute's outdoor beehives, kept in custom-printed plastic cages in groups of 16 individuals, and provided with 240 microliters of 50% sugar/water solution (an average of 15 µl for each individual). On the next morning, they were harnessed in plastic tubes, blocked in place with tape, and fed 3 µl of sugar solution. Three hours later, they were exposed to an absolute olfactory conditioning protocol (Villar et al., 2020 ⚙️). During conditioning, bees were placed in front of the odour delivery device (the same one used for the calcium imaging analysis) and exposed to clean air for 15 s (familiarisation phase), to the odorant for 5 s, and to clean air again for another 20 s. Three different intervals were selected for delivering the sugar reward: either from 2 s before odour onset to 1 s after (*backward conditioning*), from 1 to 4 s from odour onset (*early conditioning*) or from 4 to 7 s from odour onset (*delay conditioning*). Bees were exposed to four rewarded trials, with a 10-minute inter-trial-interval. Memory was tested 1 hour after the last conditioning trial. During the memory test, videos were acquired with a commercial video camera at 30 fps. A LED connected to the odour delivery device and in synch with the olfactory stimulation was used as a marker for the starting timepoint for the measurement of proboscis extension latency. Peppermint oil and 1-hexanol were used as olfactory stimuli. They were used non-diluted and their role as conditioned stimulus (CS) or novel odorant (NOd) was balanced between bees. During the test, half of the bees were exposed first to the CS and then to the NOd, while the remaining half was tested in the opposite order. A 50% sugar/water solution (w/w) was used as unconditioned stimulus (US).

## Statistical Analysis

For MBON learning simulations, a total of 16 MB networks were generated and trained with the PN response profiles to 1-hexanol (n = 8) or peppermint oil (n = 8). After training, each MBON was tested against the response profiles of the CS and of the novel odorant to assess AP firing probability to the learned and the unfamiliar stimulus. For each simulated MBON, 5 memory tests against the 5 repetitions of the CS and of the NOd were performed. To quantify stimulus response to the conditioned/novel stimulus, the mean firing probability during the time-window of stimulus arrival was calculated. The difference between the responses to the CS and to the NOd was tested with a Kruskal-Wallis statistical test (Fig. 5C ⚙️). The latency of MBON response to the CS was calculated as the latency to reach the 90% of minimal firing probability upon stimulation. The difference in latency between protocols was tested with a Kruskal-Wallis test; the difference in latency variance was assessed with a Barlett's test (Fig. 5D ⚙️).

Proboscis extension response was used to assess memory retention 1 h after the last absolute conditioning trial. Conditioned stimulus specificity was tested with a McNemar test. The latency of CS-specific PER was measured as the first frame after stimulus onset, where the proboscis
trespasses the imaginary line between the open mandibles (Fig. 4E 🐘). Differences between response latencies after early and delay conditioning were assessed with a Kruskal-Wallis test as well as with Barlett's test for difference in variance among datasets (Fig. 4F 🐘).

## Acknowledgements

This work was supported by the MSCA long-term fellowship to M.P. and by an ERC Advanced Grant ('Cognibrains') to M.G., who also thanks the Institut Universitaire de France (IUF), the CNRS, and Sorbonne University for generous support.

## Supplementary Figures
<figure><img src="image_11.png" /><figcaption>Supplementary Figure S1.</figcaption></figure>

Odorant-Specific Activity Lasts Longer in the Kenyon Cells Space.

Time traces of the normalized Euclidean distance from the centre of coordinates (where the centre of coordinates was set to 0 and the maximum distance from it was set to 1) in the PN and KC space for the three odorants. This transformation allowed performing a direct comparison between the temporal dynamics of the representations of one odorant in the PN and KC space. Discriminability – that is distance from 0 – peaks during olfactory stimulation, but is maintained longer in the KC space. Notably, such long-lasting odorant-specific signature is not achieved by maintaining onset-recruited neurons activity for a longer period of time, but by recruiting two pools of KCs (both odorant-specific), one for the ON phase, and one during the OFF phase (see also **Figure 4**). Grey patches indicate stimulation windows. Traces represent mean ± s.e.m. of 8 animals (PN data, red) and relative MB simulations (KC data, blue).
<figure><img src="image_12.png" /><figcaption>Supplementary Figure S2.</figcaption></figure>

Dependence of Simulated MBON Activity on Synaptic Plasticity Threshold.

Synaptic plasticity threshold (*spt*) dictates how many times a KC-to-MBON synapse has to be recruited during the learning window before being switched off. The influence of *spt* on MBON learning was tested for all CS/US protocols. The extreme values of the parameters (*spt* = 1 and 40) indicate that a certain synapse should be recruited only once or 40 times before being turned off (note that to be activated 40 times in a 3-s learning window and with 20 Hz sampling rate, a synapse should fire for two entire seconds). With the exceptions of the extreme parameters, the MBON AP probability upon CS and NOd presentation is only weakly affected by *spt* value. Notably, small *spt* results in higher generalized learning, while a large *spt* require a stable KC signal for synapses to be modulated and produces weaker learning scores.
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## Article and author information

**Marco Paoli**

Neuroscience Paris-Seine - Institut de biologie Paris-Seine, Sorbonne Université, INSERM, CNRS, Paris, France, Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université Paul Sabatier, CNRS, Toulouse, France

**For correspondence:** marco.paoli@sorbonne-universite.fr

**Antoine Wystrach**

Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université Paul Sabatier, CNRS, Toulouse, France
**Brice Ronsin**

Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université Paul Sabatier, CNRS, Toulouse, France

**Martin Giurfa**

Neuroscience Paris-Seine - Institut de biologie Paris-Seine, Sorbonne Université, INSERM, CNRS, Paris, France, Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, Université Paul Sabatier, CNRS, Toulouse, France, Institut Universitaire de France (IUF), Paris, France

ORCID iD: 0000-0001-7173-769X

**Copyright**

© 2024, Paoli et al.

This article is distributed under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.

**Editors**

Reviewing Editor

**Albert Cardona**

University of Cambridge, Cambridge, United Kingdom

Senior Editor

**Albert Cardona**

University of Cambridge, Cambridge, United Kingdom

**Reviewer #1 (Public Review):**

This study by Paoli et al. used a resonant scanning multiphoton microscope to examine olfactory representation in the projection neurons (PNs) of the honeybee with improved temporal resolution. PNs were classified into 9 groups based on their response patterns. Authors found that excitatory repose in the PNs precedes the inhibitory responses for ~40ms, and ~50% of PN responses contain inhibitory components. They built the neural circuit model of the mushroom body (MB) with evolutionally conserved features such as sparse representation, global inhibition, and a plasticity rule. This MB model fed with the experimental data could reproduce a number of phenomena observed in experiments using bees and other insects, including dynamical representations of odor onset and offset by different populations of Kenyon cells, prolonged representations of after-smell, different levels of odor-specificity for early/delay conditioning, and shift of behavioral timing in delay conditioning. The trace conditioning was not modeled and tested experimentally. Also, the experimental result itself is largely confirmatory to preceding studies using other organisms. Nonetheless, the experimental data and the model provide a solid basis for future studies.

https://doi.org/10.7554/eLife.93789.1.sa1

**Reviewer #2 (Public Review):**

The study presented by Paoli et al. explores temporal aspects of neuronal encoding of odors and their perception, using bees as a general model for insects. The neuronal encoding of the presence of an odor is not a static representation; rather, its neuronal representation is partly
encoded by the temporal order in which parallel olfactory pathways participate and are
combined. This aspect is not novel, and its relevance in odor encoding and recognition has
been discussed for more than the past 20 years.

The temporal richness of the olfactory code and its significance have traditionally been
driven by results obtained based on electrophysiological methods with temporal resolution,
allowing the identification and timing of the action potentials in the different populations of
neurons whose combination encodes the identity of an odor. On the other hand,
optophysiological methods that enable spatial resolution and cell identification in odor
coding lack the temporal resolution to appreciate the intricacies of olfactory code dynamics.

1. In this context, the main merit of Paoli et al.'s work is achieving an optical recording that allows for spatial registration of olfactory codes with greater temporal detail than the classical method and, at the same time, with greater sensitivity to measure inhibitions as part of the olfactory code.

The work clearly demonstrates how the onset and offset of odor stimulation triggers a dynamic code at the level of the first interneurons of the olfactory system that changes at every moment as a natural consequence of the local inhibitory interactions within the first olfactory neuropil, the antennal lobe. This gives rise to the interesting theory that each combination of activated neurons along this temporal sequence corresponds to the perception of a different odor. The extent to which the corresponding postsynaptic layers integrate this temporal information to drive the perception of an odor, or whether this sequence is, in a sense, a journey through different perceptions, is challenging to address experimentally.

In their work, the authors propose a computational approach and olfactory learning experiments in bees to address these questions and evaluate whether the sequence of combinations drives a sequence of different perceptions. In my view, it is a highly inspiring piece of work that still leaves several questions unanswered.

1. In my opinion, the detailed temporal profile of the response of projection neurons and their respective probabilities of occurrence provide valuable information for understanding odor coding at the level of neurons transferring information from the antennal lobes to the mushroom bodies. An analysis of these probabilities in each animal, rather than in the population of animals that were measured, would aid in better comprehending the encoding function of such temporal profiles. Being able to identify the involved glomeruli and understanding the extent to which the sequence of patterns and inhibitions is conserved for each odor across different animals, as it is well known for the initial excitatory burst of activity observed in previous studies without the fine temporal detail, would also be highly significant.

In my view, the computational approach serves as a useful tool to inspire future experiments; however, it appears somewhat simplistic in tackling the complexity of the subject. One question that I believe the researchers do not address is to what extent the inhibitions recorded in the projection neurons are integrated by the Kenyon cells and are functional for generating odor-specific patterns at that level.

Lastly, the behavioral result indicating a difference in conditioned response latency after early or delayed learning protocol is interesting. However, it does not align with the expected time for the neuronal representation that was theoretically rewarded in the delayed protocol. This final result does not support the authors' interpretation regarding the existence of a smell and an after-smell as separate percepts that can serve as conditioned stimuli.

https://doi.org/10.7554/eLife.93789.1.sa0
## Author Response

We are grateful for the reviewers' appreciation of our work and for their constructive feedback. We will address their comments through a revised version of the manuscript.

**Reviewer #1 (Public Review):**

This study by Paoli et al. used a resonant scanning multiphoton microscope to examine olfactory representation in the projection neurons (PNs) of the honeybee with improved temporal resolution. PNs were classified into 9 groups based on their response patterns. Authors found that excitatory repose in the PNs precedes the inhibitory responses for ~40ms, and ~50% of PN responses contain inhibitory components. They built the neural circuit model of the mushroom body (MB) with evolutionally conserved features such as sparse representation, global inhibition, and a plasticity rule. This MB model fed with the experimental data could reproduce a number of phenomena observed in experiments using bees and other insects, including dynamical representations of odor onset and offset by different populations of Kenyon cells, prolonged representations of after-smell, different levels of odor-specificity for early/delay conditioning, and shift of behavioral timing in delay conditioning. The trace conditioning was not modeled and tested experimentally. Also, the experimental result itself is largely confirmatory to preceding studies using other organisms. Nonetheless, the experimental data and the model provide a solid basis for future studies.

We thank the reviewer for summarizing the value of our study and recognizing its generality and significance. As suggested, in a revised version of the manuscript, we will discuss the implication of our approach for the context of trace conditioning. The model we presented hinges on the learning-induced plasticity of KC-to-MBON synapses recruited during the learning window (i.e., the simulated US arrival). In the case of trace conditioning, the model predicts that the time of the behavioral response time should match the expected US arrival. Contrary to this prediction, preliminary analyses on empirical measurements of PER latency upon trace conditioning indicate this is not the case. In a revised version of the manuscript, we will discuss the differences between the predictions of the model and the experimental observations in a trace conditioning paradigm.

**Reviewer #2 (Public Review):**

The study presented by Paoli et al. explores temporal aspects of neuronal encoding of odors and their perception, using bees as a general model for insects. The neuronal encoding of the presence of an odor is not a static representation; rather, its neuronal representation is partly encoded by the temporal order in which parallel olfactory pathways participate and are combined. This aspect is not novel, and its relevance in odor encoding and recognition has been discussed for more than the past 20 years.

The temporal richness of the olfactory code and its significance have traditionally been driven by results obtained based on electrophysiological methods with temporal resolution, allowing the identification and timing of the action potentials in the different populations of neurons whose combination encodes the identity of an odor. On the other hand, optophysiological methods that enable spatial resolution and cell identification in odor coding lack the temporal resolution to appreciate the intricacies of olfactory code dynamics.

(1) In this context, the main merit of Paoli et al.'s work is achieving an optical recording that allows for spatial registration of olfactory codes with greater temporal detail than the classical method and, at the same time, with greater sensitivity to measure inhibitions as part of the olfactory code.
The work clearly demonstrates how the onset and offset of odor stimulation triggers a dynamic code at the level of the first interneurons of the olfactory system that changes at every moment as a natural consequence of the local inhibitory interactions within the first olfactory neuropil, the antennal lobe. This gives rise to the interesting theory that each combination of activated neurons along this temporal sequence corresponds to the perception of a different odor. The extent to which the corresponding postsynaptic layers integrate this temporal information to drive the perception of an odor, or whether this sequence is, in a sense, a journey through different perceptions, is challenging to address experimentally.

In their work, the authors propose a computational approach and olfactory learning experiments in bees to address these questions and evaluate whether the sequence of combinations drives a sequence of different perceptions. In my view, it is a highly inspiring piece of work that still leaves several questions unanswered.

We thank the reviewer for considering that our work has an inspiring nature. Below we have tried to answer the questions raised by the following comments, and we will include part of these answers in the revised version of our manuscript.

(2) In my opinion, the detailed temporal profile of the response of projection neurons and their respective probabilities of occurrence provide valuable information for understanding odor coding at the level of neurons transferring information from the antennal lobes to the mushroom bodies. An analysis of these probabilities in each animal, rather than in the population of animals that were measured, would aid in better comprehending the encoding function of such temporal profiles. Being able to identify the involved glomeruli and understanding the extent to which the sequence of patterns and inhibitions is conserved for each odor across different animals, as it is well known for the initial excitatory burst of activity observed in previous studies without the fine temporal detail, would also be highly significant.

We thank the reviewer for recognizing the relevance of the findings in understanding the logic of olfactory coding. We agree about the importance of establishing if the different glomerular response profiles are evenly distributed across individuals or have individual biases. In the revised version of the manuscript, we will provide data on the distribution of response profiles for each animal and for different olfactory stimuli. Also, we fully agree on the importance of assessing to what extent such response profiles - largely determined by the local network of AL interneurons - are glomerulus-specific and conserved across individuals.

*In my view, the computational approach serves as a useful tool to inspire future experiments; however, it appears somewhat simplistic in tackling the complexity of the subject. One question that I believe the researchers do not address is to what extent the inhibitions recorded in the projection neurons are integrated by the Kenyon cells and are functional for generating odor-specific patterns at that level.*

The model we proposed represents, indeed, a simplification of olfactory signal processing throughout the honey bee olfactory circuit. Still, it shows that simple but realistic rules can be sufficient to grasp some fundamental aspects of olfactory coding. However, we agree with the reviewer and believe that such a minimalistic model can provide a basis for designing future experiments in which complexity can be increased by adding relevant features, such as the learning-induced plasticity of PN-to-KC synapses or the divergence of multiple PNs from the same glomerulus to different KCs

Concerning the reviewer's question on the involvement of inhibitory inputs in generating odor-specific patterns at the level of the KCs, the short answer is yes, they contribute to the
summed input of a target KC, thus to the odor representation. In designing the model, we considered that a given glomerulus provides maximal input at maximal excitation and minimal input (=0 input) at maximal inhibition. For this reason, an inhibited glomerulus contributes less (to KC action potential probability) than a glomerulus showing baseline activity. This, in turn, contributes less than an excited glomerulus. From the modeling point of view, normalizing the signal between 0 and 1 (i.e., setting minimal inhibition to 0 and maximal excitation to 1) would yield a similar result as with the current approach, where values range from -25% to +30% F/F. We implement the model's description to clarify this point.

> Lastly, the behavioral result indicating a difference in conditioned response latency after early or delayed learning protocol is interesting. However, it does not align with the expected time for the neuronal representation that was theoretically rewarded in the delayed protocol. This final result does not support the authors' interpretation regarding the existence of a smell and an after-smell as separate percepts that can serve as conditioned stimuli.

Considering that our odor stimulus lasted 5 seconds, glomerular activity is highly variable at odor onset (i.e., within the first 1s) because of short excitatory response profiles and the delayed and slower onset of inhibitory responses. After the initial phase, the neural representation of the stimulus becomes more stable. Consequently, a neural signature learned in the case of delay conditioning, i.e., with the US appearing towards the end of the olfactory stimulation (t = 4 - 5s), may present itself much earlier (t = 1.5s), triggering a behavioral response that largely anticipates the expected US arrival time.

In the model, we observe an early decrease in action potential probability even in the case of delay conditioning. This occurs because the synapses recruited during the last second of olfactory stimulation (within the learning window during which CS and US overlap) become inactive. Because odorant-induced activity recruits highly overlapping synaptic populations between 1.5 and 5 s from the onset, a learning-induced inactivation of part of these synapses will result in a reduced action-potential probability in the modeled MBON. Importantly, this event will not be governed by time but by the appearance of the learned synaptic configuration.

We will add a new section to the revised version of the manuscript to clarify this concept and perform further analyses to characterize the contribution of different response types to the modeled response latency.