{"paper_id":"56c5f382-c071-4115-aa62-64fbdf7c0adc","body_text":"1 \n \nMice discriminate odour source distance via sub-sniff temporal 1 \nfeatures of odour plumes 2 \n 3 \nAlina Cristina Marin1,2, Julia J. Harris1,&, Debanjan Dasgupta1,‡, Andrew Erskine1, Tom P.A. Warner1, 4 \nAndreas T. Schaefer1,2,* and Tobias Ackels1,2,3,* 5 \n1. Neurophysiology of Behaviour Laboratory, Francis Crick Institute, London, UK 6 \n2. Department of Neuroscience, Physiology and Pharmacology, University College London, UK 7 \n3. University of Bonn, Faculty of Medicine, Sensory Dynamics and Behaviour Laboratory, Institute of 8 \n    Experimental Epileptology and Cognition Research, Bonn, Germany 9 \n&. Current affiliation: Sainsbury Wellcome Centre, London, UK 10 \n‡. Current affiliation: Neural Circuit Dynamics Lab, MFCEM, Indian Institute of Technology, Kanpur 11 \n*  correspondence: andreas.schaefer@crick.ac.uk; tobias.ackels@ukbonn.de 12 \nAbstract 13 \nRodents rely on olfaction to navigate complex environments, particularly where visual cues 14 \nare limited. Yet how they estimate the dist ance to an odour source remains unclear. The 15 \nspatiotemporal dynamics of natural odour pl umes, shaped by airflow turbulence, offer 16 \nvaluable cues for locating odour sources. Here , we show that mice can discriminate odour 17 \nsources placed at different distances by extrac ting information from the sub-sniff temporal 18 \nstructure of naturalistic odour plumes. Using a wind tunnel and an olfactory virtual reality 19 \nsystem, we generated dynamic plumes and demonstrated, through high-throughput 20 \nautomated behaviour, that mice distingu ish near from far sources based on odour 21 \nfluctuations operating faster than their resp iratory cycle. Two-photon calcium imaging of 22 \nolfactory bulb projection neurons revealed that distance-dependent responses are present in 23 \na small subset of mitral and tufted cells, and that population activity encodes source 24 \ndistance. Critically, neural responses corr elated more strongly with high-frequency plume 25 \nfeatures than with mean odour concentration. Our results identify a neural basis for distance 26 \nestimation from odour dynamics and highlight the importance of rapid temporal processing in 27 \nmammalian olfaction. 28 \nIntroduction 29 \nOlfaction provides animals with the remarkable ability to remotely sample their environment 30 \nunder low-visibility conditions, a vital skill for crepuscular and nocturnal species, such as 31 \nrodents. Rather than relying solely on direct contact, as taste does, mice detect airborne 32 \nodours carried by complex, often turbulent airflows, enabling them to gather information 33 \nabout distant objects, such as potential food  sources, predators, or mating opportunities 34 \n(Marin et al., 2021; McKissick et al., 2024; Sunil et al., 2024). From an ecological standpoint, 35 \nthe ability to estimate how far away an odour s ource is can be crucial for a rodent’s survival. 36 \nFor instance, detecting the scent of a predator at close range prompts immediate evasive 37 \naction, whereas sensing it from afar might allow continued foraging. In the same way, 38 \nevaluating whether a food source is realisticall y within reach helps the animal to conserve 39 \nenergy and minimise exposure (Findley et al., 2021; Gire et al., 2016).  40 \nIn natural environments, odour signals rarely spread as uniform gradients. Instead, they form 41 \nspatiotemporally complex, dynamic plumes shaped by turbulent airflow (Crimaldi et al., 42 \n2022). Within these plumes, pockets and filaments of higher concentration are interspersed 43 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n2 \n \nwith intervals of relatively odour-free air (Celani et al., 2014; Crimaldi & Koseff, 2001; Moore 44 \n& Atema, 1991), producing odour events on timesca les of tens to hundreds of milliseconds, 45 \noften exceeding typical sniffing frequencies of 4–12 Hz in rodents (Wachowiak, 2011; 46 \nWelker, 1964). As a plume travels farther from its source, it expands spatially while changing 47 \nits temporal statistics, including odour event parameters such as peak height, onset slope, 48 \nand intermittency (Reddy et al., 2022; Schmuker et al., 2016; Vergassola et al., 2007). 49 \nThese features therefore carry crucial info rmation about the distance to an odour source, 50 \noffering cues to guide navigation and decisi on-making (Balkovsky & Shraiman, 2002; Mafra-51 \nNeto & Cardé, 1994; Rigolli et al., 2022). One key open question is whether and how rodents 52 \nexploit these high-frequency fluctuations (Ackels et al., 2021; Dasgupta et al., 2022; Marin et 53 \nal., 2021; Sunil et al., 2024; Tootoonian et al., 2025). If rodents can capitalise on this 54 \ntemporal information, it would not only enable them to pinpoint and distinguish different 55 \nsources from the atmospheric background (Rokni et al., 2014), but also to construct a spatial 56 \nmap of their environment using purely ol factory cues (Jacobs, 2012; Poo et al., 2022; 57 \nSterrett et al., 2024). Critically, such a strategy would let them rapidly locate and identify 58 \nodours without needing to physically move between sources to confirm their identity or 59 \nposition (Gire et al., 2016). 60 \nBehavioural evidence indicates that rodents mi ght indeed exploit these transient cues. In 61 \ncertain odour localisation tasks, mice commit to a directional choice shortly after detecting 62 \nthe odour, well before they have traversed the ent ire distance to the source (Findley et al., 63 \n2021). This early commitment suggests that relev ant spatial information is available in the 64 \nodour signal itself, even at a distance. Moreov er, when mice are trained in multi-choice 65 \narenas where multiple strategies such as  memory-based foraging are possible, they 66 \nsometimes switch between systematic searching and potentially more nuanced reliance on 67 \nthe spatiotemporal dynamics of the odour stimulus (Findley et al., 2021; Gire et al., 2016; 68 \nTariq et al., 2024). The fact that they do so under conditions of high plume complexity hints 69 \nthat rodents can integrate rapid temporal changes  of the stimulus that might be missed by 70 \nsimpler gradient-following strategies (Gire et al., 2016; Jones & Urban, 2018; Khan et al., 71 \n2012). 72 \nGrowing evidence suggests that each sniff captures not just a static snapshot of odour 73 \ncomposition but can also convey intricate temporal details. In rodents, behavioural 74 \nexperiments with optogenetic stimulation have shown that mice can discriminate between 75 \ninputs delivered as little as 10-20 ms apart (Li et al., 2014; Rebello et al., 2014; Smear et al., 76 \n2011, 2013), and asynchronous stimulation across the two olfactory bulbs can elicit different 77 \nresponses (Kuruppath et al., 2021). Systemat ic investigations of their temporal 78 \ndiscrimination abilities have revealed that mi ce can discriminate odour correlation structures 79 \nat frequencies up to 40 Hz, surpassing their typical sniffing rate (Ackels et al., 2021; 80 \nDasgupta et al., 2022; Warner et al., 2024). A head-restrained odour-counting task reveals 81 \nthat mice integrate sporadic, plume-like odour pulses across dozens of sniffs, giving greater 82 \nw\neight to inhalation-phase stimuli which might  ultimately constrain behavioural accuracy 83 \n(Boero et al., 2025). Psychophysical work in humans shows that observers can distinguish 84 \nthe temporal order of odour pulses spaced by as little as 120 ms within a single sniff, 85 \nindicating that fine-grained temporal coding extends across species (Wu et al., 2024). This 86 \ncapacity challenges earlier assumptions that the mammalian nasal cavity and slow 87 \ntransduction kinetics of olfactory receptor neurons (ORNs) would “wash out” high-frequency 88 \nfeatures of incoming odour plumes (Duchamp-Viret et al., 1999; Kepecs et al., 2006). 89 \nInstead, recent physiological and modelling work  indicates that the convergence of large 90 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n3 \n \npopulations of ORNs in the olfactory bulb (OB), allows access to fast-changing odour signals 91 \n(Ackels et al., 2021). Correspondingly, OB projection neurons, mitral and tufted cells 92 \n(MTCs), can encode odour correlation structure at frequencies up to 20 Hz (Ackels et al., 93 \n2021; Dasgupta et al., 2022). Further it has been shown that odour events such as onset, 94 \noffset, whiffs, and blanks are tightly coupled to  MTC population activity, with different MTC 95 \nclusters exhibiting varying degrees of correla tion (Lewis et al., 2021, 2024) and that mice 96 \ncan discriminate odour stimuli of low and high in termittency values (G umaste et al., 2024). 97 \nSuch high-frequency processing confers a potential advantage for resolving spatial 98 \ninformation from turbulent odour plumes. It could, for instance, enable rodents to perform 99 \nsource separation, correctly assigning odour mo lecules to their respective origins, and help 100 \ntackle the “olfactory cocktail party problem” (Hopfield, 1991; Rokni et al., 2014; Tootoonian 101 \net al., 2025). An olfactory system with access to rapid fluctuations allows mammals to extract 102 \ndistance and direction cues without physically sampling multiple locations, thereby improving 103 \nefficiency in odour-driven spatial tasks. 104 \nHere, we tested whether mice can indeed extract distance-related information from the 105 \nspatiotemporal structure of odour plumes. By combining wind tunnel recordings in an 106 \nautomated behavioural setup (Erskine et al., 2019) and an “olfactory virtual reality” system 107 \n(Ackels et al., 2021), we systematically ex plored how temporal features at various 108 \nfrequencies contribute to distance discrimination. Our results show that odour fluctuations in 109 \nthe sub-sniff frequency range are especially informative about space, providing more salient 110 \ndistance cues than slower timescale averages such as mean concentration. Mice learned to 111 \ndistinguish odour sources placed at differ ent distances purely based on these high-112 \nfrequency cues, underscoring the temporal bandwidth of rodent olfaction. Furthermore, two-113 \nphoton Ca 2+ imaging in the OB identified a subpopulation of MTCs that exhibit distance-114 \ndependent responses, suggesting a neural correlate for this spatial sensitivity. Overall, our 115 \nwork demonstrates that rodents can make use of rapid temporal signatures of odour plumes 116 \nfor spatial perception. 117 \nResults 118 \nMice can discriminate between odours originating from near and far sources 119 \nNatural odour plumes are shaped by airflow turbulence, resulting in high-frequency odour 120 \nintensity fluctuations that may contain info rmation about odour source location (Gumaste et 121 \nal., 2020, p. 20; Hopfield, 1991; Liu et al., 2020). Recent work has shown that mice can 122 \naccess high frequency components of odour stimuli (Ackels et al., 2021; Dasgupta et al., 123 \n2022; Warner et al., 2024), and suggested this could be used by mice to gather spatial 124 \ninformation using olfaction (Ackels et al., 2021; Bhattacharyya & Singh Bhalla, 2015; Findley 125 \net al., 2021; Liu et al., 2020). In this work, we investigated whether mice can extract and use 126 \nthis information to discern odour source relative distance, and how this information is 127 \nrepresented in the OB.   128 \nTo this end, we first created a naturalistic but controlled environment that was robust enough 129 \nto allow for long-term, high-throughput animal training, but complex enough to mimic 130 \nnaturally-occurring plumes ( Fig. 1A-C ). Odour stimuli were generated with custom-built 131 \ndistance Odour Delivery Devices (dODDs, Methods, Fig. 1B) placed inside a wind tunnel, 132 \nusing a passive-release mechanism (liquid odour picked up by passing airflow when the 133 \ndODD is opened, Fig. S1.1 , S1.2 A,B ). This approach allowed for odour plumes to be 134 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n4 \n \ngenerated anew for every trial, thus creating highly complex temporal structures ( Fig. 1C) 135 \nunder reproducible conditions that neverthel ess mimic the high variability occurring under 136 \nnatural conditions.  137 \n 138 \n 139 \nFigure 1: Mice can discriminate between near and far odour sources. (A) Complex airflow is 140 \ngenerated inside a wind tunnel using a fan, a honeycomb structure and an obstacle. Odour sources 141 \nare placed downstream of the fan and upstream of the detector (PID), forming the distance Odour 142 \nDelivery Device (dODD). The dODD is formed of 8 odour boxes, 4 at each distance (near or far) from 143 \nthe PID, arranged symmetrically across the midline of the tunnel (Left and Right positions). Mice (n = 144 \n24) were housed in an automated operant conditioning system (AutonoMouse), where the only water 145 \nsource was the reward from the GNG task. (B) Odour presentation boxes consisting of a metal casing 146 \nsurrounding a glass dish. An Arduino controlled servo motor opens the lid to allow airflow to pick up 147 \nodour molecules. The lid opens horizontally to the table for minimum airflow disturbance. (C) Complex 148 \nairflow in the wind tunnel generated by a honeycomb structure and a cylindrical obstacle placed 25 149 \ncm downstream of the fan. Maximum intensity projection through a 10 s video of neutrally buoyant 150 \nsoap bubbles in the wind tunnel, filmed 30 cm downstream from the fan, immediately behind the 151 \nobstacle. Bubbles appear elongated due to the exposure time of the camera. See also Fig. S1.1 (D) 152 \nSchematic of task structure: Depicted is the reward structure for half of the animals (n = 13), where 153 \nplumes originating from the near source (30 cm) were associated with a reward (S+ trials) and the 154 \nplumes originating from the far source (80 cm) were not associated with a reward (S- trials). A lick 155 \nresponse (top) would allow for early termination of the trial if the total time licked (shaded in blue on 156 \nthe lick trace) was >200 ms in a 2 s rolling window. The absence of a lick response (bottom) was 157 \ndefined as total time licked <200 ms in a 2 s rolling window, and the trial would continue until its 158 \nmaximum length was reached at 3s. These two categories of responses to the two types of trials (S+ 159 \nor S-) produced the outcomes listed on the right-hand column (Hit, Miss, False Alarm, Correct 160 \nRejection). (E) Histograms of total time licked for an example mouse, as calculated by the 161 \nAutonoMouse software to decide upon delivering a water reward, using time above threshold (dotted 162 \nline) in the lick traces as shown in D (S+ in blue, S- in orange). (F) Discrimination index defined as the 163 \nratio of total time licked (S-/S+) for each mouse below the threshold (t = 200 ms), compared to the 164 \nmean of shuffled values for that mouse. The measured value is significantly higher than shuffle 165 \n(measured>shuffled in 99% of shuffles) in 19/24 mice (filled dots). (G) Hit rate (fraction of S+ trials 166 \nlicked) plotted against False alarm rate (fraction of S- trials licked) averaged across a period of 10 167 \nblocks of 100 trials each during a stable period of the experiment. Each dot is an individual mouse. A 168 \nperfect block would be placed in the upper left corner, while a block with continuous licking would be 169 \nplaced in the upper right corner. A block where the mouse was not engaged in the task would be 170 \nplaced in the lower left corner. Indiscriminate licking while engaged in the task would be placed along 171 \nthe diagonal. 172 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n5 \n \nA home cage-based automated behavioural system (AutonoMouse (Erskine et al., 2019); 173 \nMethods) was connected to the downwind end of the wind tunnel, allowing the animals to 174 \nsample odour plumes created inside the wind tunnel ( Fig. 1A, Fig. S1.3). This allowed us to 175 \nsimultaneously train a cohort of group-housed mice (n = 24) for a long period of time (up to 176 \n14 weeks), resulting in a large number of tr ials (4053.9 ± 660 trials/animal). Using this 177 \nsystem, mice were trained to discriminate ca tegories of odour plumes originating from 178 \ndifferent distances using an olfactory Go/No-Go (GNG) task paradigm ( Fig. 1A-D ). By 179 \nharnessing the high number of trials that can be generated and presented to the mice, we 180 \nwere not limited to predetermined stimulus structures and can thus study discrimination 181 \nability in mice in a naturalistic context with ethological relevance, where the stimulus is highly 182 \nvariable, but also highly informative of odour source distance.   183 \n 184 \nMice were trained to respond with licking to the S+ and suppress licking to S- stimuli, where 185 \nthe odour source distance associated with a reward (S+) was “Near” for half of the animals, 186 \nand “Far” for the other half ( Fig. S1.5 ). The total duration of licking (total time licked) was 187 \ncalculated for each trial and used in the analysis of discrimination ability ( Fig. 1E, Fig. S1.6). 188 \nUsing this measure, we found the distribution of total time licked to be different between S+ 189 \nand S- trials for 16/24 mice (p < 0.05, Bonferroni-corrected Mann-Whitney U test). More 190 \nspecifically, mice showed a narrower distribution of lick durations during S+ trials compared 191 \nto a wider distribution, often shifted to the left or with an additional peak below 200 ms for S- 192 \ntrials ( Fig. 1E  example mouse, more mice in Fig. S1.6 ). This suggests mice were more 193 \nengaged in licking to S+ trials, compared to S- (they spent more time licking and licked more 194 \nconsistently over a large number of trials).  195 \nTo quantify the difference between the distributions of lick durations in response to S+ or S- 196 \ntrials, we split the response window at a threshold of t = 200 ms and calculated a 197 \ndiscrimination index defined as the ratio between the number of S- trials and the number of 198 \nS+ trials with a lick duration smaller than the threshold t ( Fig. 1F ). A ratio >1 indicated a 199 \nhigher number of S- trials with lick durations shorter than t, indicating correct lick behaviour. 200 \nConversely, a ratio value <1 indicated a pref erence for S+ trials. For a perfectly trained 201 \nmouse with no bias, the ratio value would tend towards infinity, while a ratio value close to 1 202 \nwould indicate no preference (and theref ore no difference between S+ and S-). To 203 \ndistinguish a real difference from one obtained by  chance, while also taking the lick bias and 204 \nthe difficulty of the task into account, we compared the ratios obtained for individual mice to 205 \nratios calculated from their own resp onses, after shuffling the labels ( Fig. S1.6M). All mice 206 \nshowed a Discrimination index >1 and the differences were significant for 19/24 mice (p < 207 \n0.001) when compared to the corresponding shuffled controls ( Fig. 1F ). Additionally, lick 208 \nduration distributions remained robustly diffe rent for any threshold chosen between 50 and 209 \n600 m\ns (Fig. S1.6N,O). This analysis was necessary due to a clear bias for licking present in 210 \nall mice. Thus, most mice displayed almost 100% accuracy in responding to S+ stimuli by 211 \nlicking (Hits). However, when mice did refrain from licking, they predominantly did so only in 212 \nS- trials, as evidenced by the lower lick fraction recorded in S- trials compared to S+ trials 213 \n(Fig. S1.4, fraction licked split by S+/S-), resulting in fewer False Alarms while still displaying 214 \na high Hit rate ( Fig. 1G, Fig S1.6P). This is what allows mice to perform with above chance 215 \naccuracy (Fig. S1.4). Overall, we conclude mice can discriminate odour sources placed at 216 \ndifferent distances based on the different lick behaviours expressed in response to S+ 217 \ncompared to S-.   218 \nThe primary aim of the behaviour experiment  described above was to determine whether 219 \nmice are capable of discriminating between odour stimuli with the same odour identity, but 220 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n6 \n \ndelivered from two different locations in space, and thus arriving at the nose of the animal 221 \nwith different temporal structures. We found that mice were capable of this discrimination, 222 \nbut it remained unclear what information in the stimulus they were using to inform their 223 \nresponses. In order to answer this question, we further aimed to investigate what information 224 \nis present in the odour plumes presented to the mouse, and what information from these 225 \nplumes is accessible to the mouse olfactory system.  226 \nWhat features of the odour plume allow for distance discrimination? 227 \nTo analyse what information is available in the plume structure that would allow distance 228 \ndiscrimination, we recorded temporal struct ures of odour plumes using a photoionization 229 \ndetector (PID). We placed the detector in the same location where a mouse’s nose would be 230 \nduring a trial, and delivered odours as described above ( Fig. 2). This was done during the 231 \nsame period as animal training (though not simultaneously), with the additional purpose of 232 \nconfirming stimulus stability throughout the training period. This large plume bank allowed us 233 \nto get a handle on the large variability seen in nat uralistic plumes, which is a key feature of 234 \nour approach, as well as an experimental chal lenge. The temporal structure of recorded 235 \nodour plumes was different between distances. Figure 2B shows example recorded plumes 236 \nfrom a far source and near source, respectively. Additional examples ( Fig. S2.1 ) further 237 \nillustrate the differences between distances, as well as the variability within categories and 238 \nacross recorded plumes overall.  239 \n 240 \n 241 \nFigure 2: Odour plume structure varies reliably with distance and sub-sniff features are the 242 \nmost informative. (A) Schematic of the distance Odour Delivery Device (dODD) as in Fig. 1 . 243 \nComplex airflow is generated inside a wind tunnel using a fan, a honeycomb structure and an 244 \nobstacle. Odour sources are placed downstream of the fan and upstream of the detector (PID) and 245 \narranged symmetrically across the midline of the tunnel (Left and Right positions). (B) Example 246 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n7 \n \nplumes recorded from the Near (30 cm) and Far (80 cm) positions. (C) Quantification of Mean odour 247 \nconcentration (left), Maximum odour concentration (middle) and Number of peaks (right) for near and 248 \nfar plumes. Box indicates 25th-75th percentiles, thick line is median, whiskers are most extreme data 249 \npoints not considered outliers (see methods). (D) tSNE analysis of odour features (mean odour 250 \nconcentration, maximum odour concentration (largest peak height), number of peaks, maximum peak 251 \nprominence, standard deviation, variance, kurtosis, skewness) calculated for a 5 s time window for 252 \neach plume, showing clear separation by distance. Each dot is a trial (n = 360 plumes, see methods). 253 \n(E) Same as (D) but for plume structure features. Dots are colour-coded the value of individual 254 \ntemporal structure features to visualise any gradients preserved in the two-dimensional space. (F) 255 \nSchematic of temporal Olfactory Delivery Device (tODD). (G) Example plumes from near and far 256 \ndistances. Recorded plumes in grey, reproduced plumes in blue (near) or orange (far). (H) Same as in 257 \n(C) but for plumes reproduced using tODD. Box indicates 25th-75th percentiles, thick line is median, 258 \nwhiskers are most extreme data points not considered outliers. 259 \nTo quantify the differences in the temporal st ructure of odour plumes, we compared the two 260 \ndistance categories for eight features calculated for each plume instance ( Fig. 2B,C, Fig. 261 \nS2.2, S2.3, S2.5 , Methods): mean odour concentration, maximum odour concentration 262 \n(largest peak height), number of peaks, maximum peak prominence, standard deviation, 263 \nvariance, kurtosis, skewness ( Fig. 2C and Fig. S2.3  with all features). The most substantial 264 \ndifferences were observed in temporal features of the plumes, such as number or height of 265 \npeaks. These were also the features that allowed for the highest accuracy of a linear 266 \ndiscriminant trained on individual features one at a time ( Fig. S2.4). Moreover, combining 267 \ndifferent features allows for almost perfect classification of plumes into near and far 268 \ncategories (tSNE, eight features used, Fig. 2D ) where the number of peaks is the most 269 \ninformative axis for the discrimination (Fig. 2E ; see Fig. S2.7  for complementary 270 \ndimensionality reduction analysis using PCA). Thus, the temporal structure of odour plumes 271 \ncontains enough information to distinguish stimu li from sources at different distances, with 272 \nsome features allowing for higher discrimination accuracy than others. 273 \nIn order to further investigate which features are most informative, we moved to a more 274 \ncontrolled setting, in which we could readily simulate odour plumes from different distances 275 \nusing a high-speed odour delivery device: the temporal Odour Delivery Device (tODD 276 \n(Ackels et al., 2021); Fig. 2F). Using this device, we were able to reproduce the recorded 277 \nstructures described above with high fidelity ( Fig. S2.8, S2.9, S2.10 ). The reproduction 278 \nstrategy retained the discriminability between plume categories based on individual features 279 \nby capturing the differences and variability in features observed in recorded plumes ( Fig. 280 \n2G,H, S2.11, S2.12). 281 \n 282 \nBy capturing and replicating the spatiotemporal attributes that differentiate near from far 283 \nodour sources, we have shown that temporal f eatures can reliably classify plume distance 284 \ncategories. Precise playback of these reco rded plumes preserves variability across 285 \nindividual stimuli. This sets the stage for tightly controlled physiology experiments under 286 \nhead-fixation, to link the observed discriminability in behavioural tasks to neural responses in 287 \nthe OB.  288 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n8 \n \nOdour source distance is encoded in a subset of OB projection neurons 289 \nWe have so far shown that mice can discrimi nate between odour sources placed at different 290 \ndistances, and that temporal information pres ent in odour plumes would allow for this 291 \ndiscrimination. We next examined whethe r these high-frequency c ues are actually 292 \naccessible to OB neurons. Historically, the brief, rapidly fluctuating concentration peaks 293 \nfound in turbulent plumes were assumed to exceed the temporal resolution of mammalian 294 \nolfaction . However, recent work suggests otherwise (Ackels et al., 2021; Dasgupta et al., 295 \n2022; Warner et al., 2024; Wu et al., 2024). To investigate whether the OB can indeed 296 \nextract distance-related information from nat uralistic plume structures, we combined a 297 \nmethod for reliably recreating plume dynamics with two-photon calcium imaging of dorsal OB 298 \nprojection neurons (MTCs) in head-fixed, anaes thetised mice (Tbet-cre:GCaMP6f, n=3), 299 \nwhile simultaneously monitoring respiration (Fig. 3A-C). Rather than repeatedly presenting a 300 \nsingle plume signature, we delivered 40 distinct “near” plumes and 40 distinct “far” plumes 301 \n(Fig. 3D , outer columns), capturing a br oad range of temporal features ( Fig. 2G-H ). This 302 \nallowed us to assess whether information about  distance can be extracted by the OB from 303 \nhighly variable, naturalistic stimuli. Additionally, the large number of distinct plumes, and the 304 \nresulting wide range of different features and f eature combinations covered by our stimulus 305 \nspace ( Fig. 2G-H ), allowed us to further probe into how different features of naturalistic 306 \nodour plumes are represented in the OB. 307 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n9 \n \n 308 \n 309 \nFigure 3: Responses of MTCs to temporally structured odour plumes recorded from different 310 \ndistances. (A) Schematic of the two-photon imaging approach. (B) GCaMP6f fluorescence from 311 \nmitral and tufted cells (maximum projection of 8,000 frames). Responses from ROI marked in red are 312 \nshown in (C). Scale bar, 100 μ m (C) Example single trials in response to an odour stimulus from a 313 \nnear (30 cm) or far (80 cm) virtual odour source (top row). Respiration traces (inhalation pointing 314 \nupwards) from the same trials (middle row). Example fluorescence traces (df/f) from the red circled 315 \nROI in (B). Shaded area represents 3 s odour stimulation window. (D) Individual near and far plumes 316 \n(outer columns) and corresponding calcium responses (inner columns) of one ROI. (E) Averaged 317 \nresponses to all near and far plumes (n = 40 for each condition, mean ± s.e.m.). (F) Average 318 \nfluorescence traces (mean ± s.e.m., n = 160 trials) for 6 example distance sensitive ROIs (near: blue, 319 \nfar: orange; ROIs i-iv: EB, v-vi: 2H). 320 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n10 \n \nTo assess whether the OB is able to discriminat e between plume structures originating from 321 \ndifferent distances, we recorded neuronal activity (as expressed by a change in GCaMP6f 322 \nfluorescence, Fig. 3D, inner columns) in response to 40 different plume structures for each 323 \ndistance category ( Fig. 3D, outer columns). Across all trials (~90 per distance, two odours; 324 \nethyl butyrate and 2-heptanone), we identified a small subset (13 out of 1062 cell-odour 325 \npairs; 1.2%) that responded differently to near versus far plumes ( Fig. 3E). These “distance-326 \nsensitive” cells showed stronger responses to far stimuli in the majority of cases ( Fig. 3F), 327 \nand each cell’s sensitivity was typically specific to one odour ( Fig. S3.1, S3.2). In addition, a 328 \nsubset of plumes was presented multiple times to capture response variability ( Fig. S3.2 ). 329 \nAn overview of responses of all ROIs to all stimuli ( Fig. S3.3) and to intermediate distances 330 \n(Fig. S3.4 ) and to a subset of plumes that were presented multiple times ( Fig S3.5) 331 \nillustrates the variety in their response strengths  and kinetics. This indicates that, despite the 332 \nbroader heterogeneity in temporal plume featur es, a subset of MTCs can encode distance-333 \nrelated information.  334 \nThis finding, while involving only a small number  of MTCs, suggests that distance-specific 335 \nsignals may be accessible at the population level. It therefore led us to the question of 336 \nwhether these neural responses can be used to predict an odour source’s distance? In the 337 \nfollowing section, we address this by examini ng whether the patterns of OB activity can 338 \nserve as a readout of plume distance. 339 \nOdour plume distance can be predicted from MTC responses 340 \nWe next assessed whether distance-dependent differences in OB activity extend beyond the 341 \nsmall number of clearly distance-sensitive MTCs ( Fig. 3 ). To visualise the responses 342 \nunderlying this classification, we plotted the average calcium signals in response to near and 343 \nfar plumes over time (Fig. 4A, left and middle). The difference map ( Fig. 4A, right) highlights 344 \nthat a subset of cell-odour pairs exhibited higher activity in response to far plumes (blue) or 345 \nnear plumes (red). A linear classifier was trained on the population responses of all recorded 346 \ncell-odour pairs to determine whether the stimulus originated from a near or far source, 347 \nbased on the ensemble activity ( Fig. 4B). Classifier performance steadily increased with the 348 \nnumber of cell-odour pairs included (purple line, maximum performance: 73.0 ± 9.0%), 349 \nsubstantially exceeding the shuffled control accura cy (black line). This result indicates that 350 \nwhile only a fraction of MTCs responded signific antly differentially to distance, activity 351 \npatterns across the population of cells carry significant information about plume origin. 352 \nFurther classification analyses for both odours individually ( Fig. S4.1, S4.2 ) and a pairwise 353 \ncomparison between further intermediate odour source distances (Fig. S4.3) can be found in 354 \nthe supplement.  355 \nWhen comparing mean responses of individual cells to far versus near conditions ( Fig. 4C), 356 \nthe majority of responses showed similar amplitude for both plume types, but a subset 357 \n(purple) fell above the unity line, showing str onger responses towards far stimuli. When we 358 \nfurther segregated these cells’ activity by odour identity, it became clear that distance 359 \nsensitivity was associated with the o dour that elicited stronger responses ( Fig. S4.4, S4.5 ). 360 \nOverall, these results demonstrate that ev en modest differences in MTC response strength 361 \nallow above-chance prediction of odour source distance from population activity. 362 \nIn the final part of our results, we will examine which temporal features of naturalistic plumes 363 \nunderlie this distance-dependent coding, providing a direct link between the neural 364 \nrepresentation in the OB and the animal’s ability to discriminate odour sources at varying 365 \ndistances. 366 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n11 \n \n 367 \n 368 \nFigure 4: Population activity and linear discriminant analysis of MTC responses. (A) Calcium 369 \ntransients as colour maps for averaged responses to near (left) and far (middle) plumes and for the 370 \ndifference between the two (right) for all cell-odour pairs (n = 1062).  (B) Accuracy of linear classifier 371 \ntrained on a 5 s response window (purple; black, shuffle control) to near versus far plumes (n = up to 372 \n1062 cell-odour pairs from 3 mice; mean ± s.d. of 50000 repetitions).  (C) Responses to near vs 373 \nresponses to far plumes plotted as mean df/f response for all trials from those distances. Distance-374 \nsensitive cells shown in purple. Cells above diagonal respond more strongly to far plumes while cells 375 \nbelow diagonal respond more strongly to near plumes. 376 \nStimulus features informing distance-sensitivity  377 \nWe first demonstrated that mice can discrimi nate between odour sources placed at different 378 \ndistances. Additionally, we showed distance-related information is also discernible from the 379 \nactivity of OB projection neurons. Next, we aimed to understand which features of the 380 \nstimulus inform the OB (and consequently the animal) about distance. To this end, we took 381 \nadvantage of the wide range of plume structures  we presented in the head-fixed setting. As 382 \ndescribed above (and Methods), we presented 40 different plume structures for each 383 \ndistance category, with each plume not only informing about distance, but also expressing 384 \nmany temporal features, in different combinations. Thus, this experimental design allowed us 385 \nto analyse cell responses not only in terms of preference for a particular distance, but also 386 \npreference for specific features of the odour plume. To further leverage diversity in the 387 \ntemporal features space, we introduced two intermediate source distances (40 cm and 60 388 \ncm) for a total of 160 different plume structures presented to the mouse ( Fig. 5A). For each 389 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n12 \n \nplume we defined extracted values for an array of different features. This allowed us to move 390 \nbeyond simple near vs far comparison (broad categories) and explore how the OB encodes 391 \nspecific temporal features across and within  categories. To support consistent plume 392 \npresentation and alignment, respiration was recorded during the experiments and remained 393 \nstable across mice and conditions at ~2 Hz ( Fig. S5.1 ). Stimuli were aligned to inhalation 394 \nonset, ensuring consistent timing across trials. We also verified that total and inhaled odour 395 \nconcentrations were broadly matched across distances ( Fig. S5.2), ensuring that observed 396 \nneural differences were not simply driven by concentration differences but solely by temporal 397 \nplume structure. When examining odour exposure on a per-sniff basis, we found that the 398 \namount of odour inhaled during the first sniff was comparable across distances, while 399 \ndifferences in the inhaled odour amount between sniffs emerged from the second sniff 400 \nonwards, particularly for near versus far plumes (Fig. S5.3). 401 \n 402 \n 403 \n 404 \nFigure 5: Features of the temporal structure of odour plumes involved in distance 405 \ndiscrimination. (A) Example plumes recorded from four different distances (30 cm, 40 cm, 60 cm 406 \nand 80 cm). (B) Quantification of mean odour concentration (left), number of peaks (middle) and 407 \nmaximum odour concentration (right) for all plumes from four distances. Box indicates 25th-75th 408 \npercentiles, thick line is median, whiskers are most extreme data points not considered outliers (see 409 \nmethods). (C) Response of an example distance-sensitive ROI (df/f averaged over the 5 s response 410 \nwindow) plotted against individual features of the odour stimulus (x-axis). Each dot is a trial (n = 160 411 \ntrials), colour-coded by distance (80 cm orange, 60 cm pale orange, 40 cm pale blue, 30 cm blue). (D) 412 \nCorrelation coefficients to distance and other stimulus features for three ROIs (i-iii). (E) Heatmap of 413 \ncorrelation coefficients as calculated in C for all cells (columns, n = 531 ROIs from 3 animals), sorted 414 \nby their correlation to distance (top row). Labels i, ii, iii correspond to ROIs shown in (D). 415 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n13 \n \nTo investigate which stimulus properties drive the responses of distance-sensitive cells, we 416 \nleveraged the substantial variability in tempor al features within each distance category (Fig. 417 \n5B). Specifically, we examined the correlation between trial-by-trial response amplitudes and 418 \nindividual plume features. Figure 5C  illustrates the method for a representative distance-419 \nsensitive ROI: the calcium response is plo tted against three plume descriptors (mean odour 420 \nconcentration, number of peaks and maximum peak concentration). This approach revealed 421 \ncells whose activity is strongly correlated to certain plume features. For example, the 422 \nresponse of Cell ii in Fig. 5D appears to be closely linked to the total odour inhaled. Notably, 423 \nfor distance-sensitive cells the strongest negative correlations appeared with the plume 424 \nfeatures that best indicate source distance. These features operate at sub-sniff timescales, 425 \nsuch as the number of peaks and the maximum peak concentration ( Fig. 5C,D). When we 426 \nsorted all recorded cells by their correlation with distance, we observed that correlation 427 \nbetween response and distance could be inversely mapped to the correlation between 428 \nresponse and sub-sniff temporal features ( Fig. 5E, bottom part of the heatmap). In contrast, 429 \nno pattern emerged between correlation with distance and average correlation measures 430 \n(Fig. 5E).  431 \n 432 \nCollectively, these findings underscore that sub-sniff temporal dynamics, rather than simple 433 \nconcentration averages, serve as  key indicators of source distance for OB neurons. This 434 \nprovides critical insight into how rodents mi ght use the complex temporal dynamics of odour 435 \nplumes under natural conditions. 436 \nDiscussion 437 \nRodents depend on olfactory cues to navigate their surroundings, making the ability to 438 \ndiscern whether an odour source is  nearby or distant critical for survival decisions such as 439 \nforaging or predator avoidance. To invest igate how rodents might perform such 440 \ndiscrimination, we developed an experimental setup that reliably produces temporally 441 \ncomplex odour plumes from different distan ces, striking a balance between naturalistic 442 \ncomplexity and experimental control. With high-throughput behavioural assays and in vivo  443 \nphysiological recordings, we examined whether mice can discriminate the distance of 444 \ndifferent odour sources and how distance-related temporal plume features are encoded 445 \nwithin the OB. 446 \n 447 \nOur results demonstrate that mice can discrim inate odour sources placed at different spatial 448 \ndistances, showing for the first ti me that information within odour plumes provides sufficient 449 \ncues for distance discrimination. We observed t hat plume features at sub-sniff timescales, 450 \nrather than broader measures such as mean odour concentration, carried more information 451 \nto discriminate odour source distances. Particularly the number of peaks and maximum peak 452 \nconcentration provided higher accuracy in distinguishing between near and far sources. This 453 \naligns with odour bout count and stimulus inte rmittency measures proposed in previous 454 \nresearch (Gumaste et al., 2024; Schmuker et al., 2016). We conducted plume feature 455 \nanalysis at high sampling frequencies (1 kHz) which likely exceeds the temporal resolution of 456 \nmouse olfactory perception (approximately 40 Hz; (Ackels et al., 2021)) and thereby 457 \npotentially inflates peak counts and magnitude values. Future studies should systematically 458 \ntest plume feature reliability and informativeness at various sampling frequencies to align 459 \nwith rodent sensory capabilities (Boero et al., 2025; Gumaste et al., 2024; Lewis et al., 460 \n2024). The distances we chose for our ex periments provided clear and discriminable 461 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n14 \n \nstatistical differences in plume features, effect ively capturing the natural variability rodents 462 \nmight encounter in their environments (Reddy et al., 2022; Rigolli et al., 2022). Interestingly, 463 \nwe observed that the four distances studied grouped into two distinct categories, suggesting 464 \na near versus far categorisation as opposed to  a gradient mapping of source distance. This 465 \ncategorical distinction may reflect an intrinsi c boundary in rodent olfactory perception (Liu et 466 \nal., 2020; Rigolli et al., 2022). Yet, whether this categorization generalises beyond our 467 \nexperimental settings remains to be determined. Expanding the range of distances and 468 \nsystematically varying environmental parameters, such as airflow dynamics and obstacle 469 \nconfiguration, would clarify how robustly specific plume features inform distance 470 \ndiscrimination across diverse contexts. 471 \n 472 \nOur findings show mice consistently displayed differential licking responses to odour sources 473 \nat varying distances, although achieving cons istently high discrimination accuracy proved 474 \nchallenging. Several factors likely contributed to this moderate performance. Notably, the 475 \ninherent asymmetry of the Go/No-Go task, where an impulsive action (licking) is contrasted 476 \nagainst inhibitory control (withholding licki ng), might have influenced overall accuracy 477 \n(Carandini & Churchland, 2013). Detailed analys is of licking patterns revealed that mice 478 \nwere more consistently responsive during re warded (S+) trials, suggesting clear reward 479 \nanticipation, whereas variability in responses during non-rewarded (S−) trials might reflect 480 \nfluctuations in motivation rather than genuine perceptual errors (Berditchevskaia et al., 481 \n2016). Additionally, efforts to mitigate predictabl e differences in odour arrival times between 482 \ndistances, by incorporating variable onset delays and flexible trial termination, inadvertently 483 \nintensified task asymmetry. Although designed to maintain engagement, these adjustments 484 \nallowed mice to conclude rewarded trials prematurely while requiring them to endure the full 485 \nduration of non-rewarded trials for a correct response. Consequently, mice may have 486 \nstrategically licked simply to reduce trial durations, potentially decoupling their licking 487 \ndecisions from actual stimulus discriminat ion. Taking these behavioural and task-design 488 \nconsiderations into account, it seems like ly that mice possess greater discrimination 489 \ncapability than was observed under our current experimental conditions. Refining the training 490 \nparadigm, for instance by implementing a two-alternative forced-choice (2AFC) task 491 \n(Bitzenhofer et al., 2022; Boero et al., 2025; Nakayama et al., 2022) to mitigate motivational 492 \nbiases, may help shed further light onto the limits of sensory discrimination capabilities. 493 \n 494 \nA key question we asked was: How is odour source distance represented in the OB? To 495 \nensure naturalistic variability of temporal pa tterns, we exposed animals to a wide range of 496 \nplume structures recorded from near and far sources. When decoding odour source distance 497 \nfrom the population of MTC calcium responses, we found that accuracy exceeded 498 \nbehavioural performance of mice proficient in  the discrimination task. This suggests that 499 \ndistance information carried by odour plumes c ould, in principle, support even more precise 500 \ndiscri\nmination. Strikingly, a small subset of  individual MTCs exhibited clear differential 501 \nresponses to near versus far plume sources, with most distance-selective MTCs responding 502 \nmore strongly to far than to near stimuli in mo st cases. Notably, this tuning was already 503 \npresent in naïve animals that had never been trained to judge source distance. To 504 \nunderstand what might be driving this selectivit y, we presented a large and diverse set of 505 \nplume structures, including intermediate distances (40 cm and 60 cm). When correlating 506 \nneural responses in a trial-by-trial fashion with  specific stimulus features, we observed that 507 \ntheir activity tended to correlate inversely with  sub-sniff plume features most predictive of 508 \nsource distance. Furthermore, when sorting cells by their correlation with distance, we found 509 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n15 \n \nthat their response tuning could be almost perfe ctly mapped to their tuning to these fast 510 \ntemporal features. In contrast, correlations  with broader, average plume statistics did not 511 \nshow such a pattern. These findings suggest that fa st fluctuations in odour intensity, rather 512 \nthan slow integrative cues, may play a c entral role in how OB neurons encode spatial 513 \ninformation. We acknowledge, however, that this observation is correlative, and future work 514 \nis needed to determine whether these features ar e used directly by the OB circuitry to 515 \nsupport behavioural discrimination. 516 \nWhat cellular or circuit mechanisms could account for these distance-dependent responses? 517 \nOne possibility is that MTCs are not “count ing peaks” per se, but instead integrate the 518 \nconstancy of odour concentration (Boero et al., 2025; Lewis et al., 2024). Less-peaky, more 519 \ncontinuous plumes (typical of far sources) c ould drive a larger, temporally summated input 520 \nfrom ORNs than highly intermittent near plumes (Lewis et al., 2024). Since ORNs of a given 521 \nreceptor type are scattered across the epithelium before converging onto the same 522 \nglomerulus (Malnic et al., 1999; Mombaerts et al., 1996), pockets of odorised air will likely 523 \nrecruit partially non-overlapping ORN ensembl es over time. A smoother plume might 524 \ntherefore activate a larger part of that ensemble, whereas a smoother plume with low 525 \nintermittency may engage fewer ORNs within brief time windows. Additional temporal 526 \nsummation could occur at the level of individual ORNs or of their postsynaptic MTCs, as 527 \nORN spike fidelity drops at high stimulat ion frequencies or high concentrations (Ghatpande 528 \n& Reisert, 2011). Taken together, this could tran sform fine-scale plume fluctuations into 529 \npopulation codes that emphasise the more stabl e temporal signature of distant sources. 530 \nConsistent with this view, the distance-sens itive MTCs we identified were generally more 531 \nnarrowly tuned to odour identity. This raises the testable idea that the most reliable 532 \nresponders within a glomerular column jointly encode both chemical identity and temporally 533 \nderived spatial information (Arneodo et al., 2018). 534 \nMethodologically, our calcium imaging approach is well suited for identifying and targeting 535 \nspecific neurons, but its temporal resolu tion limits capturing millisecond-scale plume 536 \ndynamics. Future work using high-density electrophysiology, faster GCaMP variants, or 537 \ngenetically encoded voltage indicators will be essential to resolve the precise timing of ORN 538 \nand MTC firing relative to fine temporal plume f eatures (Lewis et al., 2024; Storace et al., 539 \n2015). Combining such recordings with selective activation of a single, genetically labelled 540 \nglomerulus (Arneodo et al., 2018; Schwarz et al., 2018), and multiplexed imaging of both 541 \nORN input and MTC output (Martelli & Storac e, 2021; Storace & Cohen, 2017) could reveal 542 \nhow lateral inhibition, sister-cell diversity (Zhang et al., 2025), and molecular specialisations 543 \ntogether give rise to distance-sensitive signalling in the OB. 544 \n 545 \nOur results show that mice can discriminate the distance of an odour source by tracking 546 \nrapid, sub-sniff changes in a plume, and that a subset of MTCs in the OB reflects this 547 \ninformation. By tying together plume measur ements, behaviour, and single-cell activity, we 548 \nadd a strong link between the physics of natural odours, animal behaviour and neural 549 \ncoding. 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Nature Human Behaviour. 742 \nhttps://doi.org/10.1038/s41562-024-01984-8  743 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n20 \n \nMethods 744 \nBehaviour experiments 745 \nAnimals 746 \nAll animal procedures performed in this study were approved by the UK government (Home 747 \nOffice PPL number PA2F6DA12) and by Institutional Animal Welfare Ethical Review Panel. 748 \nAll mice used were C57/Bl6 males (n = 24 mice), evenly distributed across the different 749 \ngroups. Long-term group housing precluded us from using mixed-sex cohorts. 750 \nAll mice in a cohort (n = 24) were group-housed from weaning (21 days of age) to avoid 751 \ndisruption of social hierarchy and aggression la ter in the experiment (Van Loo et al., 2001; 752 \nVan Loo et al., 2003). They were housed into a suitably large cage, with chew blocks, a 753 \nvariety of houses and hiding places and multiple sources of water and food, until ready to be 754 \nimplanted with the RFID. All mice were kept on a 12h light cycle with the light phase 755 \nbetween 7:00 and 19:00. The change in light occurred gradually over a period of 15 minutes. 756 \nRoom temperature and humidity were checked daily and kept around 21°C and 50% 757 \nrespectively. The group cage (and later the Au tonoMouse cage) was the only cage in the 758 \nroom, to avoid conspecific odours disturbing the social hierarchy and causing aggression 759 \nwithin the cage. 760 \nRFID implantation 761 \nThe RFID implant surgery occurred around 6 weeks of age, as previously described (Erskine 762 \net al., 2019). Briefly, Mice were anaesthetised under isoflurane (induction: 5% in O 2 2 l/min, 763 \nmaintenance: 2%) and placed on a heat pad for maintenance of body temperature during the 764 \nsurgery. The fur around the base of the neck and scruff was shaved away and the skin 765 \ncleaned with chlorhexidine (1%) and then dried with a sterile swab. A pre-sterilised needle 766 \n(IM-200, RFID Systems Ltd., Yorkshire, UK) containing an RFID chip (ID-100B, RFID 767 \nSystems Ltd., Yorkshire, UK, in Chapter 3, ID-162B/1.4, RFID Systems Ltd., Yorkshire, UK 768 \nin Chapter 5) was then loaded onto a plunger and in serted into the loose skin at the base of 769 \nthe neck, facing towards the tail of the mouse. The plunger was used to push the chip out of 770 \nthe needle before removing the needle, leaving the RFID chip implanted under the skin. 771 \nForceps were then used to pinch shut the incision made by the needle and medical 772 \nsuperglue (Vetbond, 3M) was applied to seal the wound. Animals were returned to an 773 \nindividual cage for 10 minutes following the surgery to recover from anaesthesia and for the 774 \nsuperglue to dry. Once the righting reflex  was regained and the wound was confirmed as 775 \nproperly sealed the mouse was returned to the group cage with its. Following at least 5 days 776 \nrecovery from the implant surgery, the mice were transferred to the AutonoMouse system 777 \nand kept for up to 18 months. 778 \nAutomated Behaviour setup 779 \nIn AutonoMouse, groups of mice (up to 24) implanted with an RFID chip are housed in a 780 \ncommon home cage (for detailed description see Erskine et al., 2019; Ackels et al., 2021). 781 \nWithin the common home cage of AutonoMouse, mice have free access to food, social 782 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n21 \n \ninteraction and environmental enrichment (including nesting material, chew blocks, climbing 783 \nframes, cardboard tubes, cardboard and red plasti c houses). Water is not freely available in 784 \nthe system, but can be gained at any time by completion of an operant conditioning Go/No-785 \nGo task. To access these behavioural tasks, mice must leave the home cage and enter a 786 \nbehavioural area. This behavioural area cont ains the odour port and a lick port through 787 \nwhich water rewards can be released. The lick port is also connected to a lick sensor, which 788 \nregisters the animal’s response (time spent in contact with the sensor) in response to the 789 \ntask stimuli. As animals can only gain their daily water intake by completing behavioural 790 \ntasks, mice are motivated to complete long sequences of trials without manual water 791 \nrestriction. However, there is no limit on how many times mice can trigger a trial, or how 792 \nmuch time they can spend in the odour port, which could add additional constraints on using 793 \ntimeouts for negative reinforcements. 794 \nAcclimatisation to the AutonoMouse system 795 \nAnimals were weighed and their weight recorded on the day they were transferred to the 796 \nAutonoMouse and then every day for 2 weeks. 797 \nThe first phase of the behavioural task assigned to all animals was a ’pre-training’ phase, 798 \ndesigned to train animals to reliably gain their water intake from the behavioural port. This 799 \nphase was itself split into multiple tasks. At first water was delivered as soon as an animal 800 \nwas detected in the behaviour port (20 trials). Next, water was delivered only if the mouse 801 \nlicked at least once after activating the behavi our port (50 trials). Following from that, the 802 \npercentage of total trial time (2 s) that the animal must lick to gain a water reward was 803 \nincreased in 50 ms increments up to 10% of trial length, or 200 ms (50 trials per step). Each 804 \nwater reward was initially 15 µl. This was adjusted to 10-30 µl depending on animal 805 \nperformance (to ensure all mice performed roughly the same number of trials per day). 806 \nFor the first two weeks of any AutonoMouse experiment, animal weights were checked daily 807 \nto ensure health status of the cohort. After two weeks, weight was checked more 808 \ninfrequently (once a week) but total trials performed were monitored daily to ensure animals 809 \nhad acquired sufficient water rewards (e.g. by performing >100 trials, 50% rewarded) in the 810 \nlast 24 hours. Any animal not meeting this criterion or consistently dropping in weight (more 811 \nthan 2 days in a row) was isolated in the behaviour port and manually given water rewards 812 \nfrom the lick port. All animals successfully learnt to acquire water by engaging the task. 813 \nOnce reliable licking was achieved, the S+ stimulus was introduced. This often caused a 814 \ndrop in lick reliability, but mice quickly got accustomed to the new stimuli. Once lick reliability 815 \nwas re-established, the mice were progressed to the first stage of the Go/No-Go odour 816 \ndiscrimination task (EB vs IAA). 817 \nDistance discrimination stimuli 818 \nTo probe whether mice could discriminate bet ween odours originating from two different 819 \nsources, odours were placed at different distances to the mouse in the wind tunnel. All tasks 820 \nfollowed a standard Go/No-Go training paradigm, where one distance was associated with a 821 \nwater reward (S+ trials), while the other distance was not (S- trials), and licking during these 822 \ntrials would trigger a time-out of 7 s. Reward was reversed for roughly half the experimental 823 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n22 \n \ngroup (13 mice trained to lick for Near stimuli, 11 mice trained to lick for Far stimuli). Before 824 \nbeing introduced to the distance discrimination tasks, mice underwent a series of pre-training 825 \nstages that gradually increased in difficulty: 826 \nStage A - Introduction to the AutonoMouse system – 1-7 days 827 \nBriefly, animals were first trained to lick the wa terspout for a certain amount of time (the lick 828 \nthreshold, usually 10% of total trial length, or 200 ms for trials of variable length) in the 829 \npresence of an odour. Further reinforcement was offered for animals performing fewer than 830 \n200 trials/day, to ensure they could successfully use the system to meet their daily water 831 \nrequirement. 832 \nStage B - Odour discrimination – 3 weeks 833 \nNext followed a simple odour discrimination task (EB vs IAA) where animals learnt to lick to 834 \nthe Go cue and refrain from licking in response to the No Go cue. Half of the animals trained 835 \nto lick in response to EB, and half in response to IAA. Once stable performance above 80% 836 \nwas achieved, the onset of the odour delivery wa s varied to train the mice to wait for odour 837 \narrival, in preparation for the variable odour arrival of the plumes from the wind tunnel in the 838 \ndistance discrimination stage. The delay was chosen to be between 100-950 ms, 839 \nrandomised for each trial and starting with shorter delays before progressing to the full range 840 \nof delays. A switch valve control was performed at this stage, by introducing two new valves 841 \nthat had not been used before in the task. This assessed whether animals were using odour 842 \ninformation and no other environmental cues. 843 \nStage C - Valve distance discrimination – introduction to the wind tunnel – 4 weeks 844 \nNext, the animals performed the same odour discrimination (EB vs IAA), but this time 845 \ndelivered from the wind tunnel. EB was delivered from the distance odour delivery devices, 846 \nforming odour plumes, while IAA was delivered from a pressurised airflow valve. The 847 \nconcentration of IAA was decreased in small st eps (5% per day starting from 100%) over 4 848 \nweeks, such that by the end of this stage the animals were effectively performing a distance 849 \ndiscrimination task between the near EB source(s) and the far EB source(s). For roughly half 850 \nof all animals, the near source was S+ (rew arded) and the far source was S- (unrewarded); 851 \nin the other half of the group, this reward valence was reversed. 852 \nStage D - Distance discrimination task – 4 weeks 853 \nFinally, mice were then progressed to the nex t stage of the experiment, the full distance 854 \ndiscrimination task. This was split across 4 experimental stages (27 days excluding breaks, 855 \nor 3981 ± 550 trials) with short pauses between stages for equipment maintenance (10 days 856 \nbetween 2 and 3, and 3 days between 3 and 4). Additionally, the experiment was paused for 857 \n~30 min every 12h to replenish the odours, shuffle the spatial arrangement of the dODD, 858 \nand replace any servo motors that were at risk of failure due to overheating.  859 \nThe algorithm for generating distance discrimination trials was as follows: 860 \n1. Choose whether the stimulus will be Near or Far. 861 \n2. Randomly select an onset delay of 100-950 ms. 862 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n23 \n \n3. Randomly select an odour dODD position (Left or Right) from the corresponding distance. 863 \n4. Randomly select a blank dODD position (Left or Right) from opposite distance. 864 \n5. Randomly select two mineral oil valves from the tODD to create a blank plume and anti-865 \nplume structure, to create a noise decoy, using a 3s long plume template from the distance 866 \nplume bank as described elsewhere. 867 \n6. Trigger the trial to have valves in (5) open from time 0 until the trial is terminated, and the 868 \n2 dODD positions open from time 0 + onset delay decided in (2) + 2 s. 869 \nData analysis 870 \nAutonoMouse data was first extracted into MATLAB using the corresponding AutonoMouse 871 \nsoftware feature (Erskine et al., 2019, https://github.com/RoboDoig/autonomouse-control), 872 \nextracting all task parameters and lick data as to tal time licked (sum of all “on” times) and 873 \nindividual lick times (times of threshold crossing). Full lick sensor traces were also saved as 874 \nseparate files sampled at 20 kHz for future reference. First analysis steps were performed in 875 \nMATLAB using custom scripts and further analysis was performed using Python 3 in Jupyter 876 \nLab. 877 \nImaging experiments 878 \nAnimals 879 \nThree female mice aged 20 weeks expressing the calcium indicator GCaMP6f in MTCs were 880 \nused for this experiment. The mice were Tbet-Cre (Haddad et al., 2013) crossed with 881 \nGCaMP6f reporter line Ai95(RCL-GCaMP6f)-D (Madisen et al., 2015). 882 \nSurgical procedure 883 \nPrior to surgery all utilised surfaces and apparat us were sterilised with 1% trigene. Mice 884 \nwere anaesthetised using a mixture of fentanyl/midazolam/medetomidine (0.05, 5, 0.5 mg/kg 885 \nrespectively). Depth of anaesthesia was monitored throughout the procedure by testing the 886 \ntoe-pinch reflex. The fur over the skull and at the base of the neck was shaved away and the 887 \nskin cleaned with 1% chlorhexidine scrub. Mice were then placed on a thermoregulator (DC 888 \nTemperature Controller, FHC, ME USA) heat pad controlled by a temperature probe inserted 889 \nrectally. While on the heat pad, the head of the animal was held in place with a set of ear 890 \nbars. The scalp was incised and pulled away from t he skull with four arterial clamps at each 891 \ncorner of the incision. A custom head-fixation implant was attached to the base of the skull 892 \nwith medical super glue (Vetbond, 3M, Maplewood MN, USA) such that its most anterior 893 \npoint rested approximately 0.5 mm posterior to the bregma line. Dental cement (Paladur, 894 \nHeraeus Kulzer GmbH, Hanau, Germany; Simplex Rapid Liquid, Associated Dental Products 895 \nLtd., Swindon, UK) was then applied around the edges of the implant to ensure firm 896 \nadhesion to the skull. A craniotomy over the left olfactory bulb (approximately 2 × 2 mm) was 897 \nmade with a dental drill (Success 40, Osada, Tokyo, Japan) and then immersed in ACSF 898 \n(NaCl (125 mM), KCl (5 mM), HEPES (10 mM), pH adjusted to 7.4 with NaOH, 899 \nMgSO4.7H2O (2 mM), CaCl2.2H2O (2 mM), glucose (10 mM)) before removing the skull 900 \nwith forceps. The dura was then peeled back using fine forceps. A layer of 2% low-melt 901 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n24 \n \nagarose diluted in ACSF was applied over the exposed brain surface before placing a glass 902 \nwindow cut from a cover slip (borosilicate glass #1 thickness [150 μ m]) using a diamond 903 \nscalpel (Sigma-Aldrich) over the craniotomy. The edges of the window were then glued with 904 \nmedical super glue (Vetbond, 3M, Maplewood MN, USA) to the skull. Following surgery, 905 \nmice were placed in a custom head-fixation apparatus and transferred to a two-photon 906 \nmicroscope rig along with the heat pad. 907 \nImaging parameters 908 \nThe microscope (Scientifica Multiphoton VivoScope) was coupled with a MaiTai DeepSee 909 \nlaser (Spectra Physics, Santa Clara, CA) tuned to 940 nm (~30 mW average power on the 910 \nsample) for imaging. Images (512 × 512 pixels, field of view 550 × 550 μ m) were acquired 911 \nwith a resonant scanner at a frame rate of 30 Hz using a 16 × 0.8 NA water-immersion 912 \nobjective (Nikon). Using a piezo motor (PI In struments, UK) connected to the objective, a 913 \nvolume of ~300 μ m was divided into 6 planes resulting in an effective volume repetition rate 914 \nof ~5 Hz. The odour port was adjusted to approximately 0.5 cm away from the ipsilateral 915 \nnostril to the imaging window, and a flow sensor (A3100, Honeywell, NC, USA) was placed 916 \nto the contralateral nostril for continuous resp iration recording and digitised with a Power 917 \n1401 ADC board (CED, Cambridge, UK). 918 \nOdour stimuli 919 \nFor this experiment a total of 752 trials were planned to be presented for each mouse. The 920 \nplumes used were recorded in the wind tunnel during the same session and were picked 921 \nfrom the centre of the distributions of plume features as shown in Fig. S2.3. We presented 922 \n40 plumes for each of the 4 distances using 2 main odours (EB and 2H), resulting in 320 923 \n“core” trials that were randomised before bei ng added to the trial bank. The next 384 trials 924 \nconsisted of the same core 320 trials, together with an additional 8 more repeats of one 925 \nplume/distance/odour, which were again independently randomised and added to the trial 926 \nbank. Finally, the last 48 trials were 3 repeats of one plume/distance/odour for an additional 927 \n4 odours, also randomised. This trial order was then presented to each mouse, in blocks of 928 \n40 trials, in order to facilitate data acquisiti on. Odour delivery was triggered by inhalation 929 \nusing a threshold-crossing algorithm. The delay between trigger and odour delivery was the 930 \nsame for all plumes at 65ms. 931 \nImaging data analysis 932 \nFor MTC imaging, motion correction, segment ation and trace extraction were performed 933 \nusing the Suite2p package (https://github.com/MouseLand/suite2p and (Pachitariu et al., 934 \n2016). Putative neuronal somata were automatically identified by segmentation and curated 935 \nmanually. Fluorescence signal from all pixels  within each ROI was averaged and extracted 936 \nas a time series. Δ F/F = (F − F0)/F0, in which F is raw fluorescence and F0 is the median of 937 \nthe fluorescence signal distribution. Soma and neuropil fluorescence traces were extracted 938 \nand neuropil fluorescence was subtracted from t he corresponding soma trace. All further 939 \nanalysis with custom written scripts in Python 3 and JupyterLab. 940 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n25 \n \nResponsive cells / cell-odour pairs  were defined as cells/cell-odour pairs for which the mean 941 \nfluorescence during the response window for all trials of an individual odour was above 942 \nmean + 3*standard deviation of the period 3 s before stimulus onset. 943 \nDistance sensitive cells / cell-odour pairs  were defined as cells showing a significantly 944 \ndifferent (one way ANOVA) response to one distance category of the four presented. 945 \nMultiple comparisons arising from these test s were controlled using the Benjamini-Hochberg 946 \nfalse-discovery-rate (FDR) procedure with α  = 0.05.  947 \nUnless otherwise specified, linear classifiers used to understand the ability of the olfactory 948 \nbulb to represent distance-dependent plumes were all LinearSVC, as implemented in the 949 \nscikit-learn package (https://scikit-learn.org/). Data was split into a training set and a test set 950 \nusing a 75:25 split stratified by category (dis tance). Values were then scaled using Standard 951 \nScaler. C parameter was set to 0.001. 952 \nRespiration data analysis 953 \nRespiration data was extracted using Spike 2 (CED, Cambridge, UK) and further analysed in 954 \nPython 3. Inhalation was performed using a threshold crossing approach. Traces were 955 \nnormalised within one experiment to the maximum value over the length of the experiment, 956 \nthen segmented into inhalation (above threshold) and non-inhalation (below threshold) 957 \nphases. This segmentation was used to creat e a binary signal that was overlaid on the 958 \nplume traces to calculate the “inhaled plume” and features that took account of inhalation. 959 \nOdour delivery 960 \nOdours used 961 \nAll odours were obtained in their pure form from Sigma-Aldrich, St. Louis MO, USA. Odours 962 \nused: isoamyl acetate (IAA), 1-heptanal (1H), nonanoic acid (NA), α -terpinene (AT), ethyl 963 \nbutyrate (EB), 2-heptanone (2H), benzyl acetate (BA). Odours used in the wind tunnel were 964 \npresented in their pure form (EB, IAA). For imaging experiments, odours were diluted 1:50 in 965 \nmineral oil and a further 1:2 in air, resulting in ~1:100 or 1% concentration reaching the 966 \nanimal. 967 \nWind tunnel 968 \nTo produce olfactory stimuli with similar tem poral structures as those measured outdoors 969 \n(Ackels et al., 2021), a wind tunnel with near-laminar flow was developed. An obstacle 970 \nplaced in the airflow introduced controlled complexity to the airflow. The wind tunnel was 971 \nsquare in cross-section and measured 165 cm (L) x 65 cm (W) x 62 cm (H). The frame was 972 \nconstructed from aluminium profiles (MayTe c Aluminium Systemtechnik GmbH, Dachau, 973 \nGermany) and walled with clear acrylic panels. The floor of the tunnel was a laboratory 974 \nbench placed 5cm lower than the odour port that mice used for sampling. The obstacle was 975 \na plastic cylinder (height: 12.7 cm, diameter: 7.6 cm), positioned on the midline 15 cm 976 \ndownstream of the fan, inspired by the routine use of cylindrical obstacles to generate airflow 977 \nturbulence in fluid dynamics research (Béra et al., 2000). 978 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n26 \n \nWind generation was achieved by a centrifugal blower (D2E133-AM47-23, ebm-papst) 979 \nplaced in the inlet section of the tunnel, with the rear end being open for air to exit the tunnel. 980 \nThe room ventilation system had its outlet placed at the back of the room, above and slightly 981 \nbehind the mouse/PID end of the wind tunnel. To better control the airflow, we used a 982 \nhoneycomb structure (Hexagonal Aluminium W/CRIII Coating 3.0 3/8 .002N 5052, 10 cm in 983 \nthickness, Texas Almet) placed between the fan and the tunnel, to create a largely laminar 984 \nairflow. This ensured that the airflow complexity we observed was primarily created by the 985 \nobstacle. Neutrally buoyant helium-filled soap bubbles (Sage Action, Inc.) were used to 986 \nvisualise airflow, as filmed with a high-speed camera (Basler). 987 \nDistance Odour Delivery Device (dODD) 988 \nThe distance Odour Delivery Device (dODD) was used to deliver odours into the airflow in a 989 \npassive fashion. The device of choice was a custom-built ensemble of an Arduino controlled 990 \nservo motor (TowerPro SG-5010, Adafruit) that rotates an aluminium lid covering a glass 991 \npetri dish (5cm diameter). Eight such devices were placed inside the wind tunnel, and 992 \ncontrolled via a compact data acquisition (cDAQ) device (National Instruments) and either 993 \nPulseBoy or the AutonoMouse software (Ackels et al., 2021; Erskine et al., 2019, 994 \nhttps://github.com/RoboDoig/PulseBoy and https://github.com/autonomouse-control). This 995 \nalso allowed for integration with the temporal ODD (Ackels et al., 2021), which was adapted 996 \nand integrated into the system for the imaging experiments. Before each recording session 997 \nthe petri dishes were filled with 10 ml ethyl butyrate (EB), which was the odour used for all 998 \nplume experiments. 999 \nTemporal Odour Delivery Device (tODD) 1000 \nReproduction of recorded odour plumes was achieved with a temporal Odour Delivery 1001 \nDevice (tODD) and custom control software, as previously described in Ackels et al., 2021. 1002 \nIn brief, the odour delivery device was based on a modular design of four separate odour 1003 \nchannels, and consisted of an odour manifold for odour storage, a valve manifold for control 1004 \nof odour release and hardware for controlling and directing airflow through the system. We 1005 \nused high-speed micro-dispense valves with cu stom electronics for pulse-width modulation 1006 \nto maximise bandwidth. Two manifolds of four valves were joined together using a three-way 1007 \nconnector (TMMA3203950Z, The Lee Company) to create an eight-channel device. Pulse 1008 \nprofiles for calibration and stimulus production were generated with custom Python software, 1009 \nallowing us to define pulse parameters across multiple valves using a graphical user 1010 \ninterface. The drivers themselves were passed valve opening times via a 5 V TTL pulse from 1011 \ndigital I/O controls via a compact data acquisition (cDAQ) device (National Instruments). 1012 \nOdour plume recordings 1013 \nThe temporal structure of odour plumes was recorded using a mini-PID photoionization 1014 \ndetector with a bandwidth of ~330 Hz (200B miniPID, Aurora Scientific, Aurora ON, Canada) 1015 \nand Spike2 software (Cambridge Electronic Design), sampled at 1 kHz. The tip of the 1016 \ndetector was placed in the behaviour chamber at  the centre of the odour port, to mimic the 1017 \nposition of the nose of a mouse performing the task. For each set of recordings, the odour 1018 \nsource was alternated between 4 different positions (near or far, left or right) inside the wind 1019 \ntunnel. The PID was calibrated periodically by adjusting the gain and baseline such that it 1020 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n27 \n \nwould produce the same response to a series of square pulses of known amplitude (1000 1021 \nppm, 100 ppm, 10 ppm isobutylene). Thus, we could compare broad ranges of concentration 1022 \nover different recording sessions 1023 \nReproduction of recorded plumes 1024 \nReproduction of recorded odour plumes was achieved using pulse-width modulation (PWM) 1025 \ngenerated with custom Python software (PyPulse, PulseBoy, daqface; 1026 \nhttps://github.com/RoboDoig and https://github.com/warnerwarner) as previously described 1027 \nin Ackels et al., 2021. Briefly, this was ac hieved by mapping odour signal amplitudes from 1028 \nrecorded plume data to PWM duties, converting a time series of amplitudes to a time series 1029 \nof PWM duties. These duties could then be used as the input to an odour valve to reproduce 1030 \nthe original signal. First, each PID trace was corrected to baseline drift by performing 1031 \nbaseline subtraction, as compared to a 1 s period before each trial. The trace was then 1032 \nnormalised to between 0 and 1, as set by the target_max variable in PulseBoy, and then 1033 \nconverted into a series of binary opening and closing times, at a frequency of 500 Hz. To 1034 \nreproduce the plume using the whole dynamic range of the olfactometer, target_max would 1035 \nbe set to 1. The length of the openings and closings relate directly to the value of the 1036 \nnormalised signal, a value of 1 translates to a continuous opening, and a value of 0 1037 \ntranslates to continuously closed. In an offline assignment, target_max values were adjusted 1038 \nto group ratios and within group ratios to match mean concentration over the whole plume. 1039 \nThus, there was variability in the overall conc entration presented, but that variability was 1040 \nlargely within groups of plumes from the sa me distance, not between different distances. 1041 \nGenerally, wherever an odour position is inactivated a blank position was activated to 1042 \ncompensate for flow change. Thus, an inverse sequence of commands as used for the 1043 \nplume generation, termed “anti-plume”, was si multaneously fed to a valve connected to a 1044 \nmineral oil (blank) channel to produce the co mpensatory flow. This reproduction strategy 1045 \nallowed to precisely control stimulus onset  and to fully equalise mean concentration, though, 1046 \ncrucially, preserving variability within groups (Fig. 2H, Fig S2.11, S2.12).  1047 \nPlume analysis 1048 \nPlumes were recorded using Spike2 (Cambridge Electronic Design), sampled at 1 kHz. Trial 1049 \nonset was calculated using a TTL pulse passed through an additional channel on the 1050 \nacquisition board (Cambridge Electronic Desi gn). Further analysis was then performed 1051 \ninitially in MATLAB and Python 3 using Jupyter Lab. Briefly, traces were segmented using 1052 \nthe trial onsets and baseline subtraction performed using the 1 s prior to trial onset. Plume 1053 \nfeatures were defined and calculated as follows: Correlation was calculated using the corr 1054 \nfunction in MATLAB or DataFrame.corr(method=’pearson’) in Python 3 using the pandas 1055 \npackage. Plume arrival time  was calculated as the time of the first point above 3*SD of the 1056 \nbaseline (1s interval before trial onset). Mean odour concentration  was calculated as the 1057 \naverage P\nID signal over a certain time window as specified. Maximum peak was calculated 1058 \nas the maximum value of the PID signal over a certain time window as specified. Number of 1059 \npeaks was calculated using the find_peaks function (scipy.signal.find_peaks) in Python 3, 1060 \nusing the parameters height = 0.05 and prominence = 0.1 for the reproduced plumes, and 1061 \nheight = 0.01 and prominence = 0.01 for the recorded plumes. Maximum prominence was 1062 \nextracted from the outputs of the find_peaks function above. Standard deviation, Variance, 1063 \nKurtosis and Skewness were calculated using the corresponding functions in the scipy.stats 1064 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint \n\n28 \n \nlibrary for different time windows as specified. Reproduced plume integral and Odour inhaled 1065 \nwere calculated using the integrate.trapz function in scipy. Cumulative odour presented  and 1066 \nCumulative odour inhaled were calculated using the cumsum function in numpy. 1067 \nStatistical analysis and data display 1068 \nTo test for statistical significance between individual groups where appropriate we used 1069 \neither paired or unpaired student t-tests or, for non-parametric data, the Mann–Whitney U 1070 \ntest, or the Kolmogorov-Smirnov test for the equality of probability distributions. Statistical 1071 \ntest details and P values are provided in figures and/or legends. 1072 \nData figures were plotted using Jupyter Lab using seaborn, pandas, sci-kit learn or matplotlib 1073 \nfunctions in Python 3. Unless specified otherwise, boxplots depict the median as a thick line 1074 \nand default maximal whisker length of 1.5 × (q3 − q1) where q3 and q1 indicate the 75th and 1075 \n25th percentile, respectively. If points were located outside this whisker range, they were 1076 \ndisplayed individually as outliers. Unless ot herwise specified, line plots depicting animal 1077 \nperformance show mean (thick line) ± SD (shaded area) for a rolling window of blocks of 100 1078 \ntrials. 1079 \nData availability 1080 \nSource data supporting the graphs presented in  the main figures are provided as a 1081 \nrepository from https://zenodo.org/records/15390336. 1082 \nCode availability  1083 \nAnalysis code to recreate the main figures is available from https://github.com/ackels-1084 \nlab/Odour_distance_paper. All custom analysis code supporting the findings of this study will 1085 \nbe made publicly available through the same code repository upon publication. 1086 \nAcknowledgements 1087 \nThis work was supported by the Francis Crick Institute which receives its core funding from 1088 \nCancer Research UK (FC001153), the UK Medical Research Council (FC001153), and the 1089 \nWellcome Trust (FC001153); a Wellcome Trust Investigator (110174/Z/15/Z) grant and the 1090 \nNeuroNex program “From Odor to Action” to A.T.S., a BIF doctoral fellowship to A.C.M., and 1091 \na DFG postdoctoral fellowship to T.A. It was further supported by the German Research 1092 \nFoundation (FOR5424 “Modolfor”, A.T.S and T.A) and the European Union (ERC, 1093 \n“TempCOdE”, 101077017, T.A.). Views and opinions expressed are, however, those of the 1094 \nauthor(s) only and do not necessarily reflect those of the European Union or the European 1095 \nResearch Council. Neither the European Union nor the granting authority can be held 1096 \nresponsible for them.  1097 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted May 17, 2025. ; https://doi.org/10.1101/2025.05.14.653752doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}