Is this scenery worth exploring? Insight into the visual encoding of navigating ants

29 30 Solitary foraging insects like desert ants rely heavily on vision for navigation. While ants can 31 learn visual scenes, it is unclear what cues they use to decide if a scene is worth exploring at 32 the first place. To investigate this, we recorded the motor behavior of Cataglyphis velox ants 33 navigating in a virtual reality set -up and measured their lateral oscillations in response to 34 various unfamiliar visual scenes under both closed -loop and open -loop conditions. In 35 naturalistic-looking panorama, ants display regular oscillations as observed outdoors, allowing 36 them to efficiently scan the scenery. Manipulations of the virtual environment revealed distinct 37 functions served by dynamic and static cues. Dynamic cues, mainly rotational optic flow, 38 regulated the amplitude of oscillations but not their regularity. Conversely, static cues had little 39 impact on the amplitude but were essential for producing regular oscillations. Regularity of 40 oscillations decreased in scenes with only horizontal, only vertical or no edges but was restored 41 in scenes with both edge types together. The actual number of edges, the visual pattern 42 heterogeneity across azimuths, the light intensity or the relative elevation of brighter regions 43 did not affect oscillations. We conclude that ants use a simpl e but functional heuristic to 44 determine if the visual world is worth exploring, relying on the presence of at least two different 45 edge orientations in the scene. 46 Summary statement 47 48 Using a virtual reality setup, we reveal that ants rely on a heuristic to trigger visual exploration 49 in an unfamiliar scene. The simultaneous presence of vertical and horizontal edges is necessary 50 and sufficient for the ants to produce lateral oscillations and scan the scene. 51 52 53 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint

54 55 How can insect navigate complex nat ural environments using vision despite their small brain 56 and low-resolution eyes has been the focus of decades in research (Baddeley et al., 2011, 2012; 57 Collett et al., 2013, 2007; Collett and Cartwright, 1983; Cormons and Zeil, 2023; Franzke et 58 al., 2020; Graham and Cheng, 2009; Graham and Collett, 2002; Hoinville and Wehner, 2018; 59 Kohler and Wehner, 2005; Konnerth et al., 2023; Mangan and Webb, 2012; Menzel et al., 60 2005, 2018; Moura et al., 2023; Philippides et al., 2011, 2013; Thomson, 1996; Wehner and 61 Räber, 1979; Woodgate et al., 2016b; Wystrach et al., 2011a, 2013; Zeil et al., 2003; Zeil, 62 2012). We know that the visual systems of insects extract specific features such as boundaries 63 and edges (Harris et al., 2007; Horridge, 2009; O’Carroll, 1993; Seelig and Jayaraman, 2013) 64 as well as relative brightness and colour information (Ernst and Heisenberg, 1999; Horridge, 65 2005; V on Frish, 1914). Although insects can recognise specific, ecologically pertinent objects 66 with which they interact, such as flowers for foraging bees (Chittka and Raine, 2006) or 67 competitors for territorial hover flies (Nordström and O ’Carroll, 2006), evidence shows that 68 when it comes to visual navigation, the perceived scene is recognised as a whole through a 69 wide and low -resolution input spanning their whole panoramic views , which naturally 70 encompass the local landmarks and distal panorama without the need to extract them as 71 individual objects (Buehlmann et al., 2016; Graham and Cheng, 2009; Graham and Philippides, 72 2017; Stürzl et al., 2016, 2008; Towne and Moscrip, 2008; Wystrach et al., 2011a, 2011b; Zeil, 73 2012; Zeil et al., 2003). Notably, navigating insects rely on the overall shape of the panoramic 74 skyline – the border between terrestrial objects and the sky (Collett et al., 2007; Fukushi, 2001; 75 Graham and Cheng, 2009; Reid et al., 2011) or the position of large dark areas on their retina 76 rather than their specific, local shape s (Buehlmann et al., 2016; Ernst and Heisenberg, 1999; 77 Horridge, 2005; Lent et al., 2013a; Woodgate et al., 2016a) . In addition to these static cues, 78 bees, wasps and to a lesser extent ants can also use dynamic cues like translational optic flow 79 for guidance (Dittmar et al., 2010; Esch et al., 2001; Ronacher et al., 2000; Ronacher and 80 Wehner, 1995; Srinivasan et al., 2000, 1996; Stürzl et al., 2008). 81 To characterise the visual cues used by navigating insects , some studies exploited the ability 82 of experienced foragers to use learnt visual information (Chaib et al., 2021; Cormons and Zeil, 83 2023; Dittmar et al., 2010; Horridge, 2005; Jayatilaka et al., 2013; Kohler and Wehner, 2005; 84 Konnerth et al., 2023; Mangan and Webb, 2012; Moura et al., 2023; Murray et al., 2020; 85 Narendra et al., 2013; Philippides et al., 2013; Towne et al., 2017; Wystrach et al., 2013), while 86 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint other studies focused instead on the spontaneous orientation response of naïve individuals in 87 relation to particular visual features (Bausenwein et al., 1994; Buehlmann et al., 2020b; 88 Buehlmann and Graham, 2022; Byrne et al., 2003; el Jundi et al., 2016; Franzke et al., 2020, 89 2022; Goulard et al., 2021; Grabowska et al., 2018; Graham et al., 2003; Heusser and Wehner, 90 2002; Maimon et al., 200 8; Philippides et al., 2013; Wallace, 1958, 1959; Woodgate et al., 91 2016b). In both cases, these approaches revealed the nature of the cues used for heading control, 92 that is, when the insect uses visual cues to maintain its course towards a particular direction, 93 learnt or not (Buehlmann et al., 2020a; Grabowska et al., 2018; Graham et al., 2003; Harris et 94 al., 2007; Heusser and Wehner, 2002; Judd and Collett, 1998; Pratt et al., 2001; Stürzl et al., 95 2016; Wallace, 1959; Zeil, 1993). 96 Here, we did not investigate which features are important for heading control. Instead, we 97 focused on whether some features need to be present within a visual scene, unfamiliar to the 98 ants, to trigger an exploratory behavior in the first place. In other wo rds, does an exploratory 99 behavior occur 'by default', that is, as soon as the insect is outside of its nest, even if it is still 100 in the dark, or does it require the presence of specific visual features? Are such features simple 101 and general such as the mere presence of light, horizontal or vertical edges or does their 102 diversity and position also matter? Would an insect explore an artificial pattern repeated across 103 the whole 360° scene in the same way as a natural -looking scene containing an heterogenous 104 diversity of information across azimuths? Does the insect need to perceive the presence of some 105 general signatures of natural scenes, such as a darker ground and a brighter sky? Overall, do 106 insects use a visual filter to determine whether the surrounding scene ry is worth attending to 107 for exploration? We know that insects encode celestial cues through a ‘matched filter’ (Cheng 108 and Freas, 2015; Wehner, 1987), that is, a neural filtering that ‘expects’ and echoes aspects of 109 the natural spatial organization of the polarized light pattern across the sky (Aepli et al., 1985; 110 Homberg, 2004; Homberg et al., 2011; Zittrell et al., 2020) . Here, we wonder whether 111 navigating ants possess an equivalent ‘matched filter’(Cheng and Freas, 2015; Wehner, 1987), 112 which would filter natu ral aspects of terrestrial scenes and hence serves to only trigger the 113 motivation to explore when appropriate. 114 115 We used a virtual reality (VR) setup to record the detailed motor behaviour of the solitary 116 foraging ant, Cataglyphis velox, when presented with various visual sceneries (video S1). We 117 quantified their lateral oscillations, which many insects spontaneously display when navigating 118 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint (Cheng, 2022; Clement et al., 2023; Dauzere -Peres and Wystrach, 2023; Freas and Cheng, 119 2022; Iwano et al., 2010; Kanzaki et al., 1992; Kuenen and Baker, 1983; Namiki and Kanzaki, 120 2016a; Olberg, 1983; Wystrach et al., 2016) . In ants, these oscillations are crucial for visual 121 navigation (Clement et al., 2023; Lent et al., 2013b, 2010; Murray et al. , 2020). Oscillations 122 are tuned to enable the ants to scan multiple directions while walking and are upregulated when 123 in an unfamiliar environment (Clement et al., 2023; Murray et al., 2020) or when exploring a 124 novel scene during naive ants’ early learning walks (Clement et al., 2023; Jayatilaka et al., 125 2018; Zeil and Fleischmann, 2019). Therefore, we used these oscillations as a proxy to measure 126 the ants' motivation to explore new visual surroundings. This approach allowed us to 127 investigate whether the exploratory motivation depends on the visual structure of the 128 environment and to identify which visual cues prompt exploration. 129 Our results highlight the importance of specific static visual features, notably the simultaneous 130 presence of a diversity of edge orientations, to trigger the production of regular oscillations. 131 Dynamic features, such as rotational optic flow, turn out to be important for the oscillation’s 132 amplitude control but not for their production. These findings are discussed in the context of 133 the established neural circuitry and visual ecology in ants. 134

135 Study Animal 136 137 We used the Iberian thermophilic desert ant species Cataglyphis velox. In this species, foragers 138 do not use pheromone tracks but forage solitarily (Cerdá and Retana, 2000) relying mainly on 139 learnt terrestrial visual cues and path integration (PI)(Mangan and Webb, 2012). 140 Three nests of the species C. velox, were collected in Seville (Spain). Since 2020 those nests 141 were maintained at the lab at the Paul Sabatier University, Toulouse. The ant colonies were 142 housed in vertical nests created by excavating galleries and chambers in aerated concrete. 143 These nests were maintained with controlled ventilation, temperature (24-30°C), and humidity 144 (15-40%). Each nest was connected by a 20 cm transparent tube to a 40 x 30 cm foraging arena 145 with sand on the floor and without any specific visual enrichment beyond the water tubes within 146 the arena and the view of the experimental room beyond the arena’s 10 cm high walls. The 147 foraging arena was exposed to a heating lamp, a natural 12 -hour day-night light cycle that 148 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint included ultraviolet light (UV). During data collection, colonies were underfed to ensure their 149 motivation to search for food. 150 151 Trackball set-up & Virtual Reality 152 153 To record the ants’ movement, we mounted them on a trackball device (Dahmen et al., 154 2017). This device consists of a polystyrene ba ll held in levitation in an aluminium cup by a 155 stable air flow. The trackball has two sensors placed at 90° to the azimuth of the sphere, which 156 record the movements of this sphere and translate them into X and Y coordinates retracing the 157 path of the ant (Fig. 1A). The X and Y acquisition of the trackball rotations happened at a 30 158 fps, enabling us to reconstruct the ant’s movements with high precision. Ants were tethered 159 and placed on top of the ball. The tether consisted of a 0.5 mm pin that could rotate within a 160 vertical glass capillary positioned above the ant (as in Dahmen et al., 2017) and attached to the 161 ant thorax, to which we applied a drop of magnetic paint, via a micro-magnet. This tether allows 162 the ants to rotate their bodies in the yaw axis while maintaining them on the top of the trackball 163 (Fig. 1A). The polystyrene ball was prevented from rotating in the horizontal plane by two 164 small vertical wheels touching the ball’s equator. As a result, ants could physically control their 165 heading direction on top of the ball , but translational movements resulted in rotations of the 166 ball. This mounting device provides a more natural experience for the ants. First, it has the 167 advantage of naturally coupling body rotations with the expected visual feedback, without the 168 computing lags of needing to rotate the visual scene around the animal. Second, the force the 169 ant must produce to rotate their body on top of the ball is equivalent to when walking on the 170 floor. By comparison, more traditional tethers where insects are fixed require around a hundred-171 fold increase in force to rotate the ball along the horizontal plane (Dahmen et al., 2017). Finally, 172 ants, mounted on the same trackball set-up in this ‘free to rotate’ tether, are known to use learnt 173 terrestrial cues, perform path integration and search when in unfamiliar environments in a 174 remarkably similar manner as when they are on natural grounds (Clément et al., 2016). Also, 175 the amplitudes and frequencies of oscillations recorded here corresponds to what is observe d 176 in the field with ants of the same species running on natural ground (Haalck et al., 2023). 177 Together, this provides conclusive evidence that our trackball setup does not disturb or prevent 178 the ant’s natural navigational behavior. 179 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint During experiments, ants on the trackball were immersed at the centre of a 360° visual 180 display (Fig. 1A): a cylinder of 50 cm diameter and 76 cm high, which inner surface is covered 181 by LED panels (73728 LEDs) providing a light intensity up to up to 860 mcd per pixel and 182 spatial resolution of 0.94 degrees per pixel, which exceeds the visual acuity of Cataglyphis 183 eyes (>2 degrees per pixel (Zollikofer et al., 1995) ). The LEDs are controlled through the 184 computer running a virtual environment using the freeware Unity 2020.1. 185 In this study, we used two different type of conditions hereafter referred to as ‘open -186 loop’ and ‘closed-loop’ experiments, respectively. 187 ‘Closed-loop’: The translational displacement of the ants modifies the VR display, by 188 updating its position in the 3D reconstructed world according to its displacement. This 189 generates translational optic flow through the floor texture and the nearby objects’ apparent 190 movement. Note that since the ants can rotate on top of the ball, it is not needed to rotate the 191 scene, and the point of expansion of translational optic flow is directly linked to the direction 192 of rotation of the ball, that is, to the ants' actual movement direction. 193 ‘Open-loop’: The ants’ movements do not modify the virtual reality display, which thus 194 displays only a fixed image. As a result, there is no translational optic flow in this condition, 195 however the rotation of the ants on top of the ball will naturally produce the expected rotational 196 optic flow on their retinas. 197 Virtual environment & experiments 198 199 To identify the key visual features that trigger exploration, we recorded ants in various 200 unfamiliar environments, spanning from natural sceneries to abstract patterns (Fig. 1B). The 201 natural looking panorama consisted of an infinitely distant horizon displaying a skyline 202 recorded in a typical natural habitat of these ants in Seville, as well as a hundred or so individual 203 objects (trees, bushes, rocks) spread over an area correspond ing to one hectare (100 x 100 m) 204 in relation to the ants' physical displacement. The ratios between the object sizes and the ant’s 205 movements were similar to natural conditions, with the ant's viewpoint placed at 5 mm above 206 the ground, and the biggest trees extending up to 30 m in height. The ants started the assay at 207 the centre of this world, which was thus large enough to ensure that, when in closed -loop 208 condition, they could not reach the world’s borders within the experiment’s duration (160 sec). 209 Importantly, these ants had no prior experience of the VR setup, nor of the world displayed. 210 Each ant was tested once in up to three different visual conditions in a random order. Between 211 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint each test, ants were released inside their nest for at least 3 min before being retrieved for another 212 trial in a different visual condition. Each individual was therefore placed in the VR up to three 213 times but was only used once for each visual condition. 214 215 Data extraction and analysis. 216 217 All statistical analyses were run using the free software R (v 3.6.2. R Core Development Team). 218 To determine the presence of regular lateral oscillations, our focus was on the angular velocity, 219 which is a direct measurement of the left/right motor control. We processed the time series data 220 through three successive steps to calculate its spectral density using the Wiener -Khinchin 221 theorem. First, we smoothed the X, Y path (Fig. 1A, red path) using a Savitzky -Golay filter 222 (from R “trajr” package) with a window length of 2 s. Then, we extracted the angular velocity 223 time series, which was smoothed twice: first with a moving median, then with a moving mean, 224 both with a window length of 0.5 s (Fig. 1C). Next, we conducted an autocorrelation function 225 (Fig. 1D) on the smoothed time series and performed a Fourier transformation on the 226 autocorrelation coefficient to obtain the power spectral density (Fig. 1E). For each individual, 227 we extracted the dominant frequency (i.e., the frequency with the highest peak magnitude) and 228 the corresponding magnitude (Fig. 1E). To determine if these magnitudes indicated a 229 significant regular oscillation, we compared them to an ‘average’ spectral density magnitude 230 obtained by resampling randomly the ant angular velocity time series a hundred times. These 231 resampled signals underwent the same processing as the original angular velocity time series 232 data, including smoothing, autocorrelation, Fourier transformation and extraction of the highest 233 peak magnitude. For each ant, the 100 highest magnitudes obtained were averaged and then 234 compared to the individual’s real signal magnitude using a Wilcoxon one -tail test for paired 235 data. 236 237 238 Statistical models 239 240 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint To determine which key features has an impact on the oscillation behaviour, we compared our 241 conditions using mixed models. Two types of models were used: one that considers the 242 interaction and the other one with simple additive effects. If the residuals of t hese models 243 deviated from normality and/or homoscedasticity, the response variables were transformed. 244 The model was selected and further analysed through an analysis of variance (Anova), followed 245 by a Tukey's rank comparison post -hoc analysis. Additionally , multiple independent 246 experiments were performed and each individual was tested across different conditions. As a 247 result, the models were mixed models that controlled for the effects of the sequence and 248 individual, as well as considering the set of experiments as a random variable. 249 250 251 252 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint 253 254 255 Figure 1: Trackball set-ups, recording and processing of the ants' trajectory . (A) Picture 256 of the trackball within the virtual reality set -up (side view). Two wheels prevent the sphere 257 from rotating along the horizontal plane. Ants are free to rotate their body along the yaw axis 258 to control in which direction they perceive the world. The path shows an example of ants 259 recorded in a naturalistic environment ( from B). Black path is the original recording, the 260 overlay red path is the smoothed one (Savitzky -Golay filter of 2 s) (B) Different background 261 displays (conditions) during the experiment. (C) Angular velocity signal over time of an 262 individual example path ( from A) with smoothed signal superimposed (red). (D) 263 Autocorrelation carried out on the entire smoothed angular velocity signal. (E) Fourier 264 transformation of the autocorrelation coefficients signal (shown in D) provides the ‘power 265 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint spectral density’. This ap proach has the advantage to provide magnitudes that are directly 266 comparable between individuals. For each individual, the frequency peak with the highest 267 magnitude was extracted, indicating a strong oscillation of the angular velocity signal at that 268 frequency (dashed blue lines). 269 270 271

272 273 We recorded the motor behavior of navigating C. velox ants, captured in their foraging area 274 next to their nest. Crucially, these ants had no prior experience with the VR setup and were 275 tested in different unfamiliar visual conditions only once to prevent the influence of familiarity 276 visual recognition signals. Each ant was individually placed into the VR by tethering them on 277 top of the trackball in a way that enable them to physically rotate and control their actual body 278 orientation as when walking on the ground, as in (Clement et al., 2023; Dahmen et al., 2017; 279 Murray et al., 2020). The resulting paths obtained showed regular lateral oscillations – that is, 280 an alternation between left and right turns at a steady rhythm along their path – visible by naked 281 eyes (Fig. 1A). To quantify the regularity of these oscillations we extracted the angular velocity 282 signals form the paths and computed the Fourier’s power spectral density (PSD; Figs. 1, S1). 283 The magnitude of the PSD Fourier’s peak, which represented the most prominent rhythm in 284 the signal, reflects the regularity of the o scillations for a given frequency, with higher 285 magnitudes indicating a more consistent rhythm. 286 287 Ants do oscillate in the virtual reality with a naturalistic reconstructed 288 environment 289 290 We first investigated whether ants would display oscillations in the VR when in closed-loop 291 with a realistic -looking unfamiliar virtual environment . The 3D world consisted of a 292 reconstruction of a typical panorama of the ant’s habitat (Fig. 1B, first picture) providing a rich 293 distant panorama (horizon line) as well as distant and proximal cues (bushes, trees, rocks, etc.). 294 In this closed -loop condition the ant experienced translational and rotational optic -flow in 295 response to its own movement velocities (up to 544 deg/sec for rotations and 26 cm/sec for 296 translation) similarly to what they would experience in a natural scenario. Ants recorded in this 297 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint reconstructed unfamiliar environment displayed path oscillations visible to the naked eye (Fig. 298 2A). The highest PSD magnitudes obtained from the ant's signal were greater than those 299 obtained from randomly resampled signals (Wilcoxon one-tail test: V=15, P<0.001; mean±se: 300 naturalistic panorama=179.938±13.53; resampled signal=108.102±0.494), indicating that ants 301 displayed lateral oscillations with a significantly higher regularity than would be expected by 302 chance. 303 The frequency peak fell within the expected range of 0.1 to 0.54 Hz (Fig. 2D, J), consistent 304 with previous observations in other insect species (Clement et al., 2023; Lonnendonker, 1991; 305 Wystrach et al., 2016). Importantly, this rhythm is 10 to 50 times slower than the ants' typical 306 stepping frequency (Zollikofer, 1994), showing that it is not a by-product of their walking gait 307 but the result of an independent internal oscillatory mechanism (Clement et al., 2023). Overall, 308 these results confirm that ants produce regular lateral oscillations while walking within this 309 naturalistic-looking virtual environment. 310 311 312 Dynamic visual cues are involved in the control but not the production of 313 oscillations 314 315 In the previous experiment, ants were in closed -loop with the environment, that is, the ant’s 316 movements on the trackball generated translational and rotational optic flow responses in the 317 virtual scene, as well as parallax movements of the proximal objects. 318 To expand upon this finding, w e investigated whether these optic flow cues are necessary to 319 trigger regular oscillations by recordings ants with a static image of the same realistic looking 320 virtual environment (Fig. 2B for example path). Note that by doing so, the ants still perceived 321 rotational optic flow caused by their own rotation on the ball but no longer experienced 322 translational optic flow or parallax movements in response to their movement. In this situation, 323 ants still displayed clear oscillations ( Fig. 2 B, E; Wilcoxon one -tail test: V=305, P<0.001, 324 mean+se: no TOF=158±4; resampled signal=107±0.2). Angular and forward velocities as well 325 as the magnitude (i.e., regularity) of the oscillations were similar to the closed-loop conditions 326 (Fig. 2C, E, F; Magnitude (Fig. S1A, B): F1,132=2.2464, P=0.134; Angular velocities: F1,132= 327 0.0176, P=0.895; F orward velocities: F1,132= 0.3852, P=0.535), however, the oscillations’ 328 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint frequency was significantly lower in open -loop condition (Fig. 2D; F 1,132= 10.801, P<0.01; 329 Fig. S1A, B ). This shows that translational optic flow does not influence the regularity or 330 production of oscillations but impacts their frequency. 331 We next tested the impact of rotational optic -flow (ROF) on oscillatory signatures by 332 comparing the ants’ behaviour within abstract visual sceneries consisting of either vertical or 333 horizontal stripes (Fig. 1B, last row) , with the latter condition producing no ROF as the ant 334 rotates on the ball. In these abstract environments, ants still display regular oscillations visible 335 to the naked eye and significantly above those that would be expected by chance (Fig. 2G, H; 336 Wilcoxon one-tail test=Ps<0.03; means±se: horizontal: 119.535±5.37; vertical: 130.504±5.54; 337 resampled signal both group=107.505±3.89). The absence of rotational optic flow (i.e., with 338 horizontal stripes) dramatically increased the angular velocity (Fig. 2K; F1,56= 10.54, P<0.001, 339 mean±se: horizontal: 52.776±1.323 °/s; vertical: 47.876± 1.433 °/s) and reduced the forward 340 speed (Fig. 2L; F1,56= 26.74, P <0.001, means±se: horizontal: 3.86± 2.312 cm/s; vertical 5.285 341 ±2.504 cm/s), leading the ants to often execute full loops (Fig. 2H). This confirms that ROF is 342 involved in limiting the amplitude of the oscillations, as previously suggested (Clement et al., 343 2023; Dauzere -Peres and Wystrach, 2023) . However, the oscillations ’ magnitude (i.e., 344 regularity) and frequency were not different between the vertical or horizontal stripes 345 conditions (Fig. 2I, J; Anova, magnitude: F 1,56=2.01, P=0.155; frequency: F 1,56= 0.139, 346 P=0.708; Fig. S1, C, D ), showing that the presence or absence of ROF does not impact the 347 production of oscillations. 348 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint 349 Figure 2. Dynamic visual cues are involved in the control of oscillations . (A-B, G -H) 350 Examples paths of four ants across 160 s recorded with translational optic flow (TOF; A, light 351 green) and without TOF (B, green) or recorded in visual surrounding made of vertical (G, grey) 352 and horizontal (B, light grey) stripes. (C-D; I-J) Distribution of the individual Fourier dominant 353 peak obtained from angular velocities times series across 160 s. High magnitudes indicate a 354 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint strong presence of this oscillation. High frequencies indicate a fast -oscillatory rhythm. The 355 dashed black line represents the mean of the spectral density peak magnitudes resulting from 356 100 random resamples of the angular velocity time series (see methods). (C; I) Dominant 357 frequency. (D; J) Highest peak magnitude. (E -F, K-L) Distribution of the angular (E; K) and 358 forward velocities (F; L). 359 360 361 362 Diversity of edge orientation impacts the production of oscillations. 363 364 Despite being tested within an abstract environment (presented only vertical or only horizontal 365 edges), ants still displayed clear oscillations. However, these were less regular ( lower PSD 366 magnitude) than those produced in the naturalistic -looking environment (naturalistic with 367 TOF=180±12; naturalistic no TOF=158±4; horizontal: 119.535±5.37; vertical: 130.504±5.54). 368 We first hypothesized that this was due to the lack of heterogeneity across heading directions 369 of these abstract visual scenes presenting a regular pattern of stripes, which would decrease the 370 motivation to explore and inhibit the production of oscillations. Indeed, functionally, a visual 371 environment consisting of a homogeneous pattern across azimuths is less worth exploring than 372 a heterogeneous one. To test for this, we recorded ants in visual sceneries presenting a same 373 number and diversity of edges (both vertical and horizontal), but with either a homogeneous or 374 heterogeneous distribution of these stripes across azimuths (Fig. 3A). We found no effect on 375 either the regularity or frequency of the oscillations across these different experimental 376 conditions (F1,59= 0.1253, P=0.722), showing that the homogeneity/heterogeneity of the visual 377 scene across azimuth had no impact on the production or control of oscillations. 378 However, the magnitude (i.e., regularity) of oscillations in these still artificial conditions was 379 similar to the one recorded in a n aturalistic visual scenery (mean±se: naturalistic: 380 TOF=180±12; no TOF =158±4; heterogeneous=164±12; homogeneous: 165±9), suggesting 381 that the two different edge orientations (vertical and horizontal) are sufficient for the ants to 382 fully produce oscillatory behaviour s. Based on this, we put forth the hypothesis that a 383 simultaneous presence of diversity of edge orientations plays a role in the production of 384 oscillations. 385 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint To test this idea, we compiled and analysed the results of our conducted series of experiments 386 with a variety of visual backgrounds, spanning from visual scenes, which contained a complete 387 absence of edges (uniform black or uniform white) , scenes containing a single edge or two 388 edge orientations (both horizontal and vertical) or complex visual scenes with many edges (Fig 389 3B). Across these four conditions, the number of edge orientations had a strong impact on the 390 regularity of oscillations ( F3,547= 40.559, P<0.001, mean±se : zero=133.22±3.483; 391 one=134.519±3.223; two=167.4067±6.4; naturalistic: 161.4075±4.326; Fig. 3,). Contrastingly, 392 the overall brightness (Anova: brightness: 𝜒2 = 0.775, P=0.379; number of edges orientation: 393 𝜒2=19.050, Ps<0.001; Fig. 3C), the actual total edge length across the scenery (Anova: edges 394 total length: 𝜒2=1.62, P=0.203; number of e dge orientations 𝜒2=25.925, Ps<0.001; Fig. 3E) 395 and the position of contrast (Fig. 3E, F; see inverted contrast condition) of the pattern had all 396 no significant effect on the observed oscillatory characteristics. 397 Thus, for ants, the primary visual features that trigger the production of regular oscillations 398 appears to be based on the simultaneous presence of a diversity of edge orientations rather 399 than on the other parameters we tested (Fig. 3 C-F). 400 401 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint 402 Figure 3. Ants display regular oscillations in visually structured environments. (A, B) 403 Distribution of the individual Fourier dominant magnitudes obtained on angular velocities 404 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint times series across 160 s. High magnitudes indicate a strong presence of this oscillation. (A) 405 Ants recorded in heterogeneous (left) or homogeneous (right) background constitute of 406 horizontal and vertical stripes. (B) Pooling of several experimental conditions according to 407 the number of edge orientations from absence of edges to several. (C-F) Distribution of 408 magnitudes according to (C) the brightness, (D) length of edges, (E) position of contrast and 409 (F) number of stripes. For (C-F) the diversity of edges orientation significantly impacts the 410 oscillations magnitude whereas the brightness (C) or the total edge length (D) did not have an 411 effect (Anova: edge orientation Ps0.05). 412 413 414

415 416 We demonstrated that ants navigating in a VR environment exhibit their natural 417 oscillatory behaviour, with magnitudes and frequencies that closely resemble those observed 418 in ants navigating naturally in their environment (Clement et al., 2023; Jayatilaka et al., 2018; 419 Murray et al., 2020) . Such oscillations reflect the activity of an intrinsic neural oscillator, 420 widespread across species and serving various navigational context s (Clement et al., 2023; 421 Dauzere-Peres and W ystrach, 2023; Iwano et al., 2010; Lonnendonker, 1991; Namiki and 422 Kanzaki, 2016a; Steinbeck et al., 2020; Wystrach et al., 2016) . These oscillations are key to 423 visual navigation as they optimize visual exploration and are amplified when the ant is in an 424 unfamiliar visual environment (Clement et al., 2023) . Therefore, we reasoned that the 425 production of such oscillations can serve as a proxy to quantify the ant’s motivation at 426 exploring the visual world. This provided us with a tool to investigate whether the exploratory 427 motivation in ants depends on the visual structure of the surroundings. Correspondingly, we 428 identified which visual cues are key for the foraging ant to trigger such an exploratory 429 behaviour. We used VR sceneries, unfamiliar to the ants, to prevent the influence of familiarity 430 visual recognition signals on left and right turns (Wystrach et al., 2020) and thus observe a 431 more direct expression of the oscillatory behaviour (Clement et al., 2023) . However, our 432

regarding the up- or down regulation of the oscillator, based on the structure of the 433 scene and its control by dynamic cues, may well apply to both unfamiliar and familiar 434 environments. 435 The role of dynamic cues for the control of oscillations 436 437 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint The lateral oscillations and forward displacements of ants in the world generate a 438 dynamic, re-afferent sensory feedback in the form of rotational and translational optic flow, 439 respectively. Such dynamic cues are known to play a major role in insects for v arious tasks 440 such as speed control (Baird et al., 2010, 2005; Barron and Srinivasan, 2006; Portelli et al., 441 2011), optomotor r esponses (Krapp, 2000; Nityananda et al., 2017) , distance estimation for 442 landing (Lehrer et al., 1988; Ruffier et al., 2019; Srinivasan et al., 2000, 1996, 1989) the 443 recognition of a familiar scenery itself (Dittmar et al., 2010; Zeil, 1993) or to from a functional 444 compass representation (Beetz et al., 2022). As expected, manipulating these dynamic cues in 445 our virtual reality set -up influence d the dynamics of the oscillations. As previously shown 446 (Busch et al., 2018; Dauzere-Peres and Wystrach, 2023; Egelhaaf, 2023; Franzke et al., 2022, 447 2020; Pansopha et al., 2014), the absence of rotational optic flow led to a dramatic increase of 448 the turn’s amplitude, up to non-adaptive full loops (Fig. 2H, K) confirming the role of rotational 449 optic flow to control the extent of the current turn. Removing translational optic flow and the 450 parallax motion of the objects – generated by forward motion – down-regulated the oscillation 451 frequency ( Fig. 2 D). However, neither of these dynamical cues impacted the oscillation 452 regularity (Fig. 2G-I). Overall, this suggests that dynamic cues are dedicated to the control of 453 the dynamics (amplitude and timing) of oscillations but not involved in their actual production. 454 455 The role of static cues for the triggering of oscillations? 456 457 Interestingly, presenting artificially impoverished visual sceneries disrupted the regular ity of 458 the oscillatory rhythm of the ants (Fig. 3B), showing that the structure of the world perceived 459 by th e ants influences the production of oscillations. The crucial parameter to explain the 460 presence of prominent, regular oscillations turned out to be the diversity of edge orientations 461 (Fig. 3B) rather than the overall brightness, the distribution of the contrasts , the quantity of 462 edges in the scene (Fig. S2) or the heterogeneity of the visual pattern across the azimuths (Fig. 463 3A). For instance, oscillations are equally downregulated in a uniform background (black or 464 white Fig. 3 B), slightly more prominent in a scenery consisting of only one type of edge 465 orientation (horizontal or vertical and independently of the number of bars displayed) and fully 466 reinstated in sceneries with a combination of at least two edge orientations ( Fig. 3B). The 467 insect’s visual system is known to extract edges to detect the boundaries of objects and surfaces 468 (Buehlmann and Graham, 2022; Grabowska et al., 2018; Horridge, 2009; Maimon et al., 2008). 469 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Here we show that the presence of this feature, at least in C. velox ants, is important to trigger 470 oscillations and thus a proper exploration of the scene. 471 Ants are well-known to use the skyline to maintain their direction of travel (Collett et 472 al., 2007; Fukushi, 2001; Graham and Cheng, 2009; Philippides et al., 2011) . The skyline 473 represents the boundary between the sky, on top and the terrestrial objects, below. Given that 474 in natural conditions the sky is generally brighter than the ground, we could have expected that 475 insect possess a ‘matched filter’ (Cheng and Freas, 2015; Wehner, 1987) to process 476 preferentially sceneries that are brighter in the up per part (Möller, 2002; Philippides et al., 477 2011; Schultheiss et al., 2016). Perhaps surprisingly, inversions of the scenery contrast did not 478 influence the behavioural outcome as ants displayed equally prominent oscillations whether 479 the upper part of the scenery was bright or dark (Fig. S2C). However, the sceneries presented 480 here were monochromatic (black and white) using LED with RGB wavelength (peaks at 624, 481 520 and 470 nm respectively), and thus lacking UV- as well as polarized light, which is present 482 in natural skylines . Arthropod eyes (Aepli et al., 1985; Labhart and Meyer, 2002; Mote and 483 Wehner, 1980) are sensitive to UV - and polarized light (notably UV polarization) with the 484 latter being detected by ommatidia pointing towards the sky (Aepli et al., 1985; Labhart and 485 Meyer, 2002, 1999; Möller, 2002) and both celestial cues are important for navigational 486 behaviours. Hence, w hether ants possess a match ed filter for sceneries with UV and/or 487 polarized light in the upper part of their visual field to regulate their oscillations remains to be 488 explored. However, we show here that the direction of edges in the RGB range, which is picked 489 up mainly by the ant’s long-range visual receptor (Aksoy and Camlitepe, 2018) play a role in 490 the context of controlling exploratory behaviours. 491 492 A simple but functional heuristic 493 494 Oscillations in ants are optimized for visual exploration across azimuths (Clement et al., 2023) 495 and consequently it would be useless to perform such exploratory behaviours in visually 496 structure-less sceneries. Therefore, it makes functional sense that ants downregulate the 497 production of oscillations in the presence of such structure-less environments (Fig. 2 G-H; Fig. 498 3B). The fact that ants rely on the presence of a diversity of edge orientations – rather than 499 brightness or the actual quantity of edges – to control the production of oscillations (Fig. S 2), 500 can also be explained functionally. Indeed, relying on brightness or the overall number of edges 501 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint within a visual scene would lead ants to oscillate and explore differently in visually cluttered 502 environments (dark but large quantity of edges) and open e nvironments (bright with little 503 quantity of edges); even though both environments must be equally scanned across all azimuths 504 to recall proper heading directions (Murray et al., 2020; Stürzl et al., 2016; Zeil e t al., 2003). 505 Relying on the presence of at least two different edge orientations appears as a good proxy to 506 recognize an environment worth exploring. The only natural landscapes that would not fit in 507 this criterion would be the ones with a perfectly flat horizon; in which case there would be only 508 one type of edge orientation: the horizon. Such f lat landscapes provide no directional 509 information, so to repeatedly scan different directions is not beneficial. What’s more, ants 510 tested in flat landscapes without terrestrial information spontaneously rely more on their path 511 integration mechanisms to navigate to their goal in a straight line (Schultheiss et al., 2013; 512 Schwarz and Cheng, 2010; Sommer and Wehner, 2004), that is, a navigation strategy that would 513 be hindered, rather than helped, by the production of high amplitude lateral oscillations. 514 Perhaps more surprisingly, ants produced equally regular and prominent oscillations in 515 a natural-like scenery than in an artificial scenery consisting of a same repeated pattern across 516 azimuths, as long as the latter presented both horizontal and vertical edges ( Fig. 3B). Such 517 artificial repeated-pattern sceneries were directionally non -informative and thus are 518 theoretically not worth exploring; yet the ants explored them thoroughly. This shows that ants 519 pay no attention to whether the perceived scenery changes significantly across heading 520 directions; or at least this information , which theoretically would be the best one to measure, 521 is not used to control the production of oscillation s. However, such environments, consisting 522 of a perfectly repeated pattern of at least two types of edge orientation are extremely unlikely 523 to exist in natural conditions. As it is often the case in ant navigation literature (Cheung et al., 524 2014; Cruse and Wehner, 2011; Menzel et al., 2005; Menzel and Muller, 1996; Wehner, 2003; 525 Wehner and Menzel, 1990), we conclude that ants use a simple but efficient heuristic to decide 526 whether a visual world is worth exploring. The latter seems to be a matched filter, enabling the 527 ants to detect the presence of at least two different edge orientations. 528 We speak here of match ed filter in the sense that ants are not ‘measuring’ the 529 information that is directly related to their aim (i.e., oscillations are trigger ed accordingly to 530 whether they currently provide pertinent visual information ) but use a visual heuristic that 531 matches natural conditions with the way they gather information through oscillations along the 532 yaw axis (i.e., the presence of horizontal and vertical edges means oscillations should be useful 533 given any natural environment ). Surely, the match between the filter and the world is less 534 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint impressive than in the context of sky compass orientation, where the receptive fields of neurons 535 can literally ma tch a natural distribution of the e -vector across azimuth and elevation 536 (Homberg, 2004; Homberg et al. , 2011). However, the visual terrestrial sceneries can var y in 537 the natural habitat of C. velox, even over short distances: from arid landscape to meter high 538 field of plants in spring and presence or absence of trees providing large skyline changes and 539 even partial canopies above the ant’s nest (personal observation s). Hence, assuming the 540 presence of some horizontal and vertical edges may well fall close to the most optimized filter 541 one could do to trigger exploratory behaviours. 542 543 How similar or different such a matched filter is in different ant species and whether it actually 544 reflects some properties of their natural habitat, remains to be seen. North African Cataglyphis 545 fortis ants living in saltpans usually forage in a featureless environment, relied more on their 546 PI and were slower at learning visual cues as compared to Melophorus bagoti, which thrives 547 in a more cluttered environment (Cheng et al., 2014; Schwarz and Cheng, 2010) . Also, given 548 that our experiments exclusively involved individuals raised in a lab environment, we cannot 549 conclude whether the visual features identified here are developmentally constrained (Aepli et 550 al., 1985; Bolzon et al., 2009; Ehmer and Gronenberg, 2002; Labhart and Meyer, 1999) and 551 structural re -organization through pruning and synaptic change along visual pathway s 552 (Blakemore and Cooper, 1970; Cabirol et al., 2018; Grob et al., 2024; Gu and Kanai, 2014; 553 Rössler, 2019; Rössler and Groh, 2012; Voss et al., 2017). Manipulating the visual environment 554 during early experiences, as well as comparative studies across different ant species in such 555 VR systems promises to shed light on the plasticity of the brain in interpreting a visual scenery. 556 557 558 Neural considerations 559 560 Visual feature extractions can occur early in visual processing. Notably, lateral 561 inhibition between neighbouring ommatidia extract local edges, which then can be integrated 562 and summed up in the optic lobes (Bolzon et al., 2009; Srinivasan et al., 1982) . From there, 563 several pathways may convey various visual information to infl uence oscillations. Lateral 564 oscillations in insects are likely produced in a pre -motor area called the Lateral Accessory 565 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Lobes (LAL), which receive multiple input s from many brain regions (Chiang et al., 2011; 566 Namiki et al., 2014; Namiki and Kanzaki, 2018, 2016a, 2016b; Steinbeck et al., 2020). 567 Regarding the effect of dynamic visual cues, wide-field rotational optic flow computed 568 in the optic lobes (Busch et al., 2018; Egelhaaf, 2023; Pansopha et al., 2014) is locally 569 compared to efference copies of the motor signal (Dauzere-Peres and Wystrach, 2023; Fenk et 570 al., 2021; Kim et al., 2015), as well as proprioceptive feedback to form a prediction error signal 571 that is sent to the LAL to modulate the oscillations (Dauzere-Peres and Wystrach, 2023). This 572 control operates via an asymmetrical activation of the LAL enhancing a left or a right turn, in 573 order to reduce the computed error. This is in congruence with the high amplitude turns 574 (looping behaviour) observed in the absence of horizontal optic flow (Fig. 2G-L), which must 575

in a prediction error that activates one side of the LAL to prolong the current turn. 576 Our work reveals the existence of another pathway, which conveys this time 577 information about static features in the visual scene, and notably, the simultaneous presence of 578 at least two types of edge orientation in the scene . This pathway does not modulate the 579 amplitude and frequency of oscillations but their actual presence and regularity in the outputted 580 behaviour, which could be simply achieved through bilater al and thus overall excitation or 581 inhibition of the LAL production of rhythmical oscillations. However, through which relay this 582 pathway operates remains unclear. Evidence suggests the existence of a direct pathway from 583 the optic lobe to the LAL (Namiki et al., 2014; Namiki and Kanzaki, 2018, 2016b; Steinbeck 584 et al., 2020) providing a likely candidate to explain how the presence of a diversity of edges in 585 the scene can modulate the activity of the LAL and thus the production of regular oscillations. 586 Alternatively, a parallel pathway exists from the optic lobe to the Mushroom Bodies 587 (MBs) (Ehmer and Gronenberg, 2002; Gronenberg, 2001; Heisenberg, 2003; Paulk and 588 Gronenberg, 2008), which outputs information to the LAL (Aso et al., 2014; Manjila et al., 589 2019; Scaplen et al., 2021; Steinbeck et al., 2020) . The MBs are implicated in formation and 590 retrieval of route memories of visual scenes in ants and can output a familiarity signal of the 591 currently perceived scene (Buehlmann et al., 2020b; Kamhi et al., 2020; Webb and Wystrach, 592 2016; Wystrach, 2023), which we know can modulate the dynamics of oscillation (Clement et 593 al., 2023; Murray et al., 2020) . Whether information about the static visual feature identified 594 here are conveyed through the MBs remains however a priori less likely, given that it operates 595 already during early exploration of novel environments, without the need for learning. 596 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Finally, the up - and down regulation of oscillations based on visual cues could also 597 involve the Central Complex. This brain area keeps track of the insect current heading using 598 mutli-modal information including visual and motion cues (Green et al., 2017; Kim et al., 2019; 599 Stone et al., 2017; Turner -Evans and Jayaraman, 2016) and output s left and right motor 600 commands to the LAL to align the current heading with respect to a desired ‘goal heading’ 601 (Beetz et al., 2023; Honkanen and Adden, 2019; Mussells Pires et al., 2024; Westeinde et al., 602 2024). We understand well how the Central Complex can adjust the course of ants in regards 603 to both learnt (Matheson et al., 2022; Wystrach et al., 2020) and innate visual cues (Goulard et 604 al., 2021) but how it is involved during the ant’s systematic search in an unfamiliar environment 605 remains unclear. We could envision how the presence of visual cues such as the one 606 characterised here could up- or down regulate the Central Complex influence on the ant’s motor 607 control and hence modulate the intrinsic production of oscillations in the LAL. 608

609 610 Here we show that ants adjust their visual exploration to the ty pe of structure of the 611 surrounding visual scenery. This control is based on a simple heuristic or ‘matched filter’ 612 (Cheng and Freas, 2015; Wehner, 1987) detecting the simultaneous presence of both vertical 613 and horizontal edges in the scenery (Grabowska et al., 2018), which upregulates the production 614 of regular oscillations and thus expose s the ant’s gaze to multiple directions. Dynamic cues, 615 such as optic flow, are also picked up but used for the different purpose of controlling the 616 amplitude of the oscillations. How plastic and different this heuristic is across species remains 617 unknown but the possibility to conduct experiments with navigating insects within VR systems 618 promises to shed light onto how various species encode their visual natural environment, adapt 619 to various early life experiences and thus helps to understand each species’ respective Umwelt 620 (von Uexküll, 1934). 621 622

623 624 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint We thank Hansjürgen Dahmen for providing us with the trackball system. We also thank 625 Cody Freas, and Andrew Philippides for their helpful feedback on the manuscript. Funding: 626 European Research Council, grant reference number: EMERG-ANT 759817, author: A.W. 627 Declaration of interests 628 The authors declare no competing interests. 629 630 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint 631

632 633 Aepli, F., Labhart, T., Meyer, E.P ., 1985. Structural specializations of the cornea and retina at the 634 dorsal rim of the compound eye in hymenopteran insects. Cell Tissue Res. 239, 19–24. 635 https://doi.org/10.1007/BF00214897 636 Aso, Y ., Hattori, D., Yu, Y ., Johnston, R.M., Iyer, N.A., Ngo, T.-T., Dionne, H., Abbott, L., Axel, R., 637 Tanimoto, H., Rubin, G.M., 2014. The neuronal architecture of the mushroom body provides a 638 logic for associative learning. eLife 3, e04577. https://doi.org/10.7554/eLife.04577 639 Baddeley, B., Graham, P ., Husbands, P ., Philippides, A., 2012. A Model of Ant Route Navigation Driven 640 by Scene Familiarity. PLoS Comput Biol 8, e1002336. 641 https://doi.org/10.1371/journal.pcbi.1002336 642 Baddeley, B., Graham, P ., Philippides, A., Husbands, P ., 2011. Holistic visual encoding of ant-like 643 routes: Navigation without waypoints. Adaptive Behavior 19, 3–15. 644 https://doi.org/10.1177/1059712310395410 645 Baird, E., Kornfeldt, T., Dacke, M., 2010. Minimum viewing angle for visually guided ground speed 646 control in bumblebees. Journal of Experimental Biology 213, 1625–1632. 647 https://doi.org/10.1242/jeb.038802 648 Baird, E., Srinivasan, M.V., Zhang, S., Cowling, A., 2005. Visual control of flight speed in honeybees. 649 Journal of Experimental Biology 208, 3895–3905. https://doi.org/10.1242/jeb.01818 650 Barron, A., Srinivasan, M.V., 2006. Visual regulation of ground speed and headwind compensation in 651 freely flying honey bees ( Apis mellifera L.). Journal of Experimental Biology 209, 978–984. 652 https://doi.org/10.1242/jeb.02085 653 Bausenwein, B., Müller, N.R., Heisenberg, M., 1994. Behavior-dependent activity labeling in the 654 central complex of Drosophila during controlled visual stimulation. Journal of Comparative 655 Neurology 340, 255–268. https://doi.org/10.1002/cne.903400210 656 Beetz, M.J., Kraus, C., el Jundi, B., 2023. Neural representation of goal direction in the monarch 657 butterfly brain. Nat Commun 14, 5859. https://doi.org/10.1038/s41467-023-41526-w 658 Beetz, M.J., Kraus, C., Franzke, M., Dreyer, D., Strube-Bloss, M.F., Rössler, W., Warrant, E.J., Merlin, C., 659 Jundi, B. el, 2022. Flight-induced compass representation in the monarch butterfly heading 660 network. Current Biology 32, 338-349.e5. https://doi.org/10.1016/j.cub.2021.11.009 661 Blakemore, C., Cooper, G.F., 1970. Development of the Brain depends on the Visual Environment. 662 Nature 228, 477–478. https://doi.org/10.1038/228477a0 663 Bolzon, D.M., Nordström, K., O’Carroll, D.C., 2009. Local and Large-Range Inhibition in Feature 664 Detection. J. Neurosci. 29, 14143–14150. https://doi.org/10.1523/JNEUROSCI.2857-09.2009 665 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Buehlmann, C., Aussel, A., Graham, P ., 2020a. Dynamic multimodal interactions in navigating wood 666 ants: What do path details tell us about cue integration? Journal of Experimental Biology 667 jeb.221036. https://doi.org/10.1242/jeb.221036 668 Buehlmann, C., Graham, P ., 2022. Innate visual attraction in wood ants is a hardwired behavior seen 669 across different motivational and ecological contexts. Insect. Soc. 69, 271–277. 670 https://doi.org/10.1007/s00040-022-00867-3 671 Buehlmann, C., Woodgate, J.L., Collett, T.S., 2016. On the Encoding of Panoramic Visual Scenes in 672 Navigating Wood Ants. Current Biology 26, 2022–2027. 673 https://doi.org/10.1016/j.cub.2016.06.005 674 Buehlmann, C., Wozniak, B., Goulard, R., Webb, B., Graham, P ., Niven, J.E., 2020b. Mushroom Bodies 675 Are Required for Learned Visual Navigation, but Not for Innate Visual Behavior, in Ants. Current 676 Biology 30, 3438-3443.e2. https://doi.org/10.1016/j.cub.2020.07.013 677 Busch, C., Borst, A., Mauss, A.S., 2018. Bi-directional Control of Walking Behavior by Horizontal Optic 678 Flow Sensors. Current Biology 28, 4037-4045.e5. https://doi.org/10.1016/j.cub.2018.11.010 679 Byrne, M., Dacke, M., Nordström, P ., Scholtz, C., Warrant, E., 2003. Visual cues used by ball-rolling 680 dung beetles for orientation. J Comp Physiol A 189, 411–418. https://doi.org/10.1007/s00359-681 003-0415-1 682 Cabirol, A., Cope, A.J., Barron, A.B., Devaud, J.-M., 2018. Relationship between brain plasticity, 683 learning and foraging performance in honey bees. PLoS ONE 13, e0196749. 684 https://doi.org/10.1371/journal.pone.0196749 685 Cerdá, X., Retana, J., 2000. Alternative strategies by thermophilic ants to cope with extreme heat: 686 individual versus colony level traits. Oikos 89, 155–163. https://doi.org/10.1034/j.1600-687 0706.2000.890117.x 688 Chaib, S., Dacke, M., Wcislo, W., Warrant, E., 2021. Dorsal landmark navigation in a Neotropical 689 nocturnal bee. Current Biology 31, 3601-3605.e3. https://doi.org/10.1016/j.cub.2021.05.029 690 Cheng, K., 2022. Oscillators and servomechanisms in orientation and navigation, and sometimes in 691 cognition. Proceedings of the Royal Society B: Biological Sciences 289, 20220237. 692 https://doi.org/10.1098/rspb.2022.0237 693 Cheng, K., Freas, C.A., 2015. Path integration, views, search, and matched filters: the contributions of 694 Rüdiger Wehner to the study of orientation and navigation. J Comp Physiol A 201, 517–532. 695 https://doi.org/10.1007/s00359-015-0984-9 696 Cheng, K., Schultheiss, P ., Schwarz, S., Wystrach, A., Wehner, R., 2014. Beginnings of a synthetic 697 approach to desert ant navigation. Behavioural Processes 102, 51–61. 698 https://doi.org/10.1016/j.beproc.2013.10.001 699 Cheung, A., Collett, M., Collett, T.S., Dewar, A., Dyer, F., Graham, P ., Mangan, M., Narendra, A., 700 Philippides, A., Stürzl, W., Webb, B., Wystrach, A., Zeil, J., 2014. Still no convincing evidence for 701 cognitive map use by honeybees. Proc. Natl. Acad. Sci. U.S.A. 111. 702 https://doi.org/10.1073/pnas.1413581111 703 Chiang, A.-S., Lin, Chih-Yung, Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., Shih, C.-T., Wu, J.-J., 704 Wang, G.-T., Chen, Y .-C., Wu, Cheng-Chi, Chen, G.-Y ., Ching, Y .-T., Lee, P .-C., Lin, Chih-Yang, Lin, H.-705 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint H., Wu, Chia-Chou, Hsu, H.-W., Huang, Y .-A., Chen, J.-Y ., Chiang, H.-J., Lu, C.-F., Ni, R.-F., Yeh, C.-Y ., 706 Hwang, J.-K., 2011. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in 707 Drosophila at Single-Cell Resolution. Current Biology 21, 1–11. 708 https://doi.org/10.1016/j.cub.2010.11.056 709 Chittka, L., Raine, N.E., 2006. Recognition of flowers by pollinators. Current Opinion in Plant Biology, 710 Biotic interactions / edited by Anne Osbourn and Sheng Yang He 9, 428–435. 711 https://doi.org/10.1016/j.pbi.2006.05.002 712 Clement, L., Schwarz, S., Wystrach, A., 2023. An intrinsic oscillator underlies visual navigation in ants. 713 Current Biology 33, 411-422.e5. https://doi.org/10.1016/j.cub.2022.11.059 714 Collett, M., Chittka, L., Collett, T.S., 2013. Spatial Memory in Insect Navigation. Current Biology 23, 715 R789–R800. https://doi.org/10.1016/j.cub.2013.07.020 716 Collett, T.S., Cartwright, B.A., 1983. Eidetic images in insects: their role in navigation. Trends in 717 Neurosciences 6, 101–105. https://doi.org/10.1016/0166-2236(83)90048-6 718 Collett, T.S., Graham, P ., Harris, R.A., 2007. Novel landmark-guided routes in ants. Journal of 719 Experimental Biology 210, 2025–2032. https://doi.org/10.1242/jeb.000315 720 Cormons, M.J., Zeil, J., 2023. Digger wasps Microbembex monodonta SAY (Hymenoptera, 721 Crabronidae) rely exclusively on visual cues when pinpointing their nest entrances. PLOS ONE 722 18, e0282144. https://doi.org/10.1371/journal.pone.0282144 723 Cruse, H., Wehner, R., 2011. No Need for a Cognitive Map: Decentralized Memory for Insect 724 Navigation. PLoS Comput Biol 7, e1002009. https://doi.org/10.1371/journal.pcbi.1002009 725 Dahmen, H., Wahl, V.L., Pfeffer, S.E., Mallot, H.A., Wittlinger, M., 2017. Naturalistic path integration of 726 Cataglyphis desert ants on an air-cushioned lightweight spherical treadmill. J Exp Biol 220, 634–727 644. https://doi.org/10.1242/jeb.148213 728 Dauzere-Peres, O., Wystrach, A., 2023. Ants integrate proprioception, visual context and efference 729 copies to make robust predictions (preprint). Animal Behavior and Cognition. 730 https://doi.org/10.1101/2023.03.29.534571 731 Dittmar, L., Stürzl, W., Baird, E., Boeddeker, N., Egelhaaf, M., 2010. Goal seeking in honeybees: 732 matching of optic flow snapshots? Journal of Experimental Biology 213, 2913–2923. 733 https://doi.org/10.1242/jeb.043737 734 Egelhaaf, M., 2023. Optic flow based spatial vision in insects. J Comp Physiol A. 735 https://doi.org/10.1007/s00359-022-01610-w 736 Ehmer, B., Gronenberg, W., 2002. Segregation of visual input to the mushroom bodies in the 737 honeybee (Apis mellifera). J. Comp. Neurol. 451, 362–373. https://doi.org/10.1002/cne.10355 738 el Jundi, B., Foster, J.J., Khaldy, L., Byrne, M.J., Dacke, M., Baird, E., 2016. A Snapshot-Based 739 Mechanism for Celestial Orientation. Current Biology 26, 1456–1462. 740 https://doi.org/10.1016/j.cub.2016.03.030 741 Ernst, R., Heisenberg, M., 1999. The memory template in Drosophila pattern vision at the flight 742 simulator. Vision Research 39, 3920–3933. https://doi.org/10.1016/S0042-6989(99)00114-5 743 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Esch, H.E., Zhang, S., Srinivasan, M.V., Tautz, J., 2001. Honeybee dances communicate distances 744 measured by optic flow. Nature 411, 581–583. https://doi.org/10.1038/35079072 745 Fenk, L.M., Kim, A.J., Maimon, G., 2021. Suppression of motion vision during course-changing, but 746 not course-stabilizing, navigational turns. Current Biology 31, 4608-4619.e3. 747 https://doi.org/10.1016/j.cub.2021.09.068 748 Franzke, M., Kraus, C., Dreyer, D., Pfeiffer, K., Beetz, M.J., Stöckl, A.L., Foster, J.J., Warrant, E.J., el 749 Jundi, B., 2020. Spatial orientation based on multiple visual cues in non-migratory monarch 750 butterflies. Journal of Experimental Biology 223, jeb223800. 751 https://doi.org/10.1242/jeb.223800 752 Franzke, M., Kraus, C., Gayler, M., Dreyer, D., Pfeiffer, K., el Jundi, B., 2022. Stimulus-dependent 753 orientation strategies in monarch butterflies. Journal of Experimental Biology 225, jeb243687. 754 https://doi.org/10.1242/jeb.243687 755 Freas, C.A., Cheng, K., 2022. The Basis of Navigation Across Species. Annu. Rev. Psychol. 73, 217–241. 756 https://doi.org/10.1146/annurev-psych-020821-111311 757 Fukushi, T., 2001. Homing in wood ants. J. Exp. Biol. 204. 758 Goulard, R., Buehlmann, C., Niven, J.E., Graham, P ., Webb, B., 2021. A unified mechanism to support 759 innate and learned use of visual landmark guidance in insects (preprint). Animal Behavior and 760 Cognition. https://doi.org/10.1101/2021.01.28.428620 761 Grabowska, M.J., Steeves, J., Alpay, J., van de Poll, M., Ertekin, D., van Swinderen, B., 2018. Innate 762 visual preferences and behavioral flexibility in Drosophila. Journal of Experimental Biology 763 jeb.185918. https://doi.org/10.1242/jeb.185918 764 Graham, P ., Cheng, K., 2009. Ants use the panoramic skyline as a visual cue during navigation. 765 Current Biology 19, R935–R937. https://doi.org/10.1016/j.cub.2009.08.015 766 Graham, P ., Collett, T.S., 2002. View-based navigation in insects: how wood ants ( Formica rufa L.) 767 look at and are guided by extended landmarks. Journal of Experimental Biology 205, 2499–768 2509. https://doi.org/10.1242/jeb.205.16.2499 769 Graham, P ., Fauria, K., Collett, T.S., 2003. The influence of beacon-aiming on the routes of wood ants. 770 Journal of Experimental Biology 206, 535–541. https://doi.org/10.1242/jeb.00115 771 Graham, P ., Philippides, A., 2017. Vision for navigation: What can we learn from ants? Arthropod 772 Structure & Development 46, 718–722. https://doi.org/10.1016/j.asd.2017.07.001 773 Green, J., Adachi, A., Shah, K.K., Hirokawa, J.D., Magani, P .S., Maimon, G., 2017. A neural circuit 774 architecture for angular integration in Drosophila. Nature 546, 101–106. 775 https://doi.org/10.1038/nature22343 776 Grob, R., Müller, V.L., Grübel, K., Rössler, W., Fleischmann, P .N., 2024. Importance of magnetic 777 information for neuronal plasticity in desert ants. Proceedings of the National Academy of 778 Sciences 121, e2320764121. https://doi.org/10.1073/pnas.2320764121 779 Gronenberg, W., 2001. Subdivisions of hymenopteran mushroom body calyces by their afferent 780 supply. J. Comp. Neurol. 435, 474–489. https://doi.org/10.1002/cne.1045 781 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Gu, J., Kanai, R., 2014. What contributes to individual differences in brain structure? Front. Hum. 782 Neurosci. 8. https://doi.org/10.3389/fnhum.2014.00262 783 Harris, R.A., Graham, P ., Collett, T.S., 2007. Visual Cues for the Retrieval of Landmark Memories by 784 Navigating Wood Ants. Current Biology 17, 93–102. https://doi.org/10.1016/j.cub.2006.10.068 785 Heisenberg, M., 2003. Mushroom body memoir: from maps to models. Nat Rev Neurosci 4, 266–275. 786 https://doi.org/10.1038/nrn1074 787 Heusser, D., Wehner, R., 2002. The visual centring response in desert ants, Cataglyphis fortis. Journal 788 of Experimental Biology 205, 585–590. https://doi.org/10.1242/jeb.205.5.585 789 Hoinville, T., Wehner, R., 2018. Optimal multiguidance integration in insect navigation. Proc. Natl. 790 Acad. Sci. U.S.A. 115, 2824–2829. https://doi.org/10.1073/pnas.1721668115 791 Homberg, U., 2004. In search of the sky compass in the insect brain. Naturwissenschaften 91, 199–792 208. https://doi.org/10.1007/s00114-004-0525-9 793 Homberg, U., Heinze, S., Pfeiffer, K., Kinoshita, M., el Jundi, B., 2011. Central neural coding of sky 794 polarization in insects. Phil. Trans. R. Soc. B 366, 680–687. 795 https://doi.org/10.1098/rstb.2010.0199 796 Honkanen, A., Adden, A., 2019. The insect central complex and the neural basis of navigational 797 strategies. Journal of Experimental Biology 15. 798 Horridge, A., 2009. What does an insect see? Journal of Experimental Biology 212, 2721–2729. 799 https://doi.org/10.1242/jeb.030916 800 Horridge, G.A., 2005. Recognition of a familiar place by the honeybee (Apis mellifera). J Comp Physiol 801 A 191, 301–316. https://doi.org/10.1007/s00359-004-0592-6 802 Iwano, M., Hill, E.S., Mori, A., Mishima, Tatsuya, Mishima, Tsuneko, Ito, K., Kanzaki, R., 2010. Neurons 803 associated with the flip-flop activity in the lateral accessory lobe and ventral protocerebrum of 804 the silkworm moth brain. J. Comp. Neurol. 518, 366–388. https://doi.org/10.1002/cne.22224 805 Jayatilaka, P ., Murray, T., Narendra, A., Zeil, J., 2018. The choreography of learning walks in the 806 Australian jack jumper ant Myrmecia croslandi. J Exp Biol 221, jeb185306. 807 https://doi.org/10.1242/jeb.185306 808 Jayatilaka, P ., Raderschall, C.A., Narendra, A., Zeil, J., 2013. Individual foraging patterns of the jack 809 jumper ant Myrmecia croslandi (Hymenoptera: Formicidae). Myrmecological News 19, 75–83. 810 Judd, S.P .D., Collett, T.S., 1998. Multiple stored views and landmark guidance in ants. Nature 392, 811 710–714. https://doi.org/10.1038/33681 812 Kamhi, J.F., Barron, A.B., Narendra, A., 2020. Vertical Lobes of the Mushroom Bodies Are Essential for 813 View-Based Navigation in Australian Myrmecia Ants. Current Biology 30, 3432-3437.e3. 814 https://doi.org/10.1016/j.cub.2020.06.030 815 Kanzaki, R., Sugi, N., Shibuya, T., 1992. Self-generated Zigzag Turning of Bombyx mori Males during 816 Pheromone-mediated Upwind Walking(Physology). Zoological science. 9, 515–527. 817 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Kim, A.J., Fitzgerald, J.K., Maimon, G., 2015. Cellular evidence for efference copy in Drosophila 818 visuomotor processing. Nat Neurosci 18, 1247–1255. https://doi.org/10.1038/nn.4083 819 Kim, S.S., Hermundstad, A.M., Romani, S., Abbott, L.F., Jayaraman, V., 2019. Generation of stable 820 heading representations in diverse visual scenes. Nature 576, 126–131. 821 https://doi.org/10.1038/s41586-019-1767-1 822 Kohler, M., Wehner, R., 2005. Idiosyncratic route-based memories in desert ants, Melophorus bagoti: 823 How do they interact with path-integration vectors? Neurobiology of Learning and Memory 83, 824 1–12. https://doi.org/10.1016/j.nlm.2004.05.011 825 Konnerth, M.M., Foster, J.J., el Jundi, B., Spaethe, J., Beetz, M.J., 2023. Monarch butterflies memorize 826 the spatial location of a food source. Proceedings of the Royal Society B: Biological Sciences 827 290, 20231574. https://doi.org/10.1098/rspb.2023.1574 828 Krapp, H.G., 2000. Neuronal Matched Filters for Optic Flow Processing in Flying Insects, in: 829 International Review of Neurobiology. Elsevier, pp. 93–120. https://doi.org/10.1016/S0074-830 7742(08)60739-4 831 Kuenen, L.P .S., Baker, T.C., 1983. A non-anemotactic mechanism used in pheromone source location 832 by flying moths. Physiol Entomol 8, 277–289. https://doi.org/10.1111/j.1365-833 3032.1983.tb00360.x 834 Labhart, T., Meyer, E.P ., 2002. Neural mechanisms in insect navigation: polarization compass and 835 odometer. Current Opinion in Neurobiology 12, 707–714. https://doi.org/10.1016/S0959-836 4388(02)00384-7 837 Labhart, T., Meyer, E.P ., 1999. Detectors for polarized skylight in insects: a survey of ommatidial 838 specializations in the dorsal rim area of the compound eye. Microsc. Res. Tech. 47, 368–379. 839 https://doi.org/10.1002/(SICI)1097-0029(19991215)47:63.0.CO;2-Q 840 Lehrer, M., Srinivasan, M.V., Zhang, S.W., Horridge, G.A., 1988. Motion cues provide the bee’s visual 841 world with a third dimension. Nature 332, 356–357. https://doi.org/10.1038/332356a0 842 Lent, D.D., Graham, P ., Collett, T.S., 2013a. Visual Scene Perception in Navigating Wood Ants. Current 843 Biology 23, 684–690. https://doi.org/10.1016/j.cub.2013.03.016 844 Lent, D.D., Graham, P ., Collett, T.S., 2013b. Phase-Dependent Visual Control of the Zigzag Paths of 845 Navigating Wood Ants. Current Biology 23, 2393–2399. 846 https://doi.org/10.1016/j.cub.2013.10.014 847 Lent, D.D., Graham, P ., Collett, T.S., 2010. Image-matching during ant navigation occurs through 848 saccade-like body turns controlled by learned visual features. Proceedings of the National 849 Academy of Sciences 107, 16348–16353. https://doi.org/10.1073/pnas.1006021107 850 Lonnendonker, U., 1991. DYNAMIC PROPERTIES OF ORIENTATION TO A VISUALLY FIXATED TARGET BY 851 WALKING COLORADO BEETLES. Journal of Experimental Biology 158, 16. 852 Maimon, G., Straw, A.D., Dickinson, M.H., 2008. A Simple Vision-Based Algorithm for Decision Making 853 in Flying Drosophila. Current Biology 18, 464–470. https://doi.org/10.1016/j.cub.2008.02.054 854 Mangan, M., Webb, B., 2012. Spontaneous formation of multiple routes in individual desert ants 855 (Cataglyphis velox). Behavioral Ecology 23, 944–954. https://doi.org/10.1093/beheco/ars051 856 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Manjila, S.B., Kuruvilla, M., Ferveur, J.-F., Sane, S.P ., Hasan, G., 2019. Extended Flight Bouts Require 857 Disinhibition from GABAergic Mushroom Body Neurons. Current Biology 29, 283-293.e5. 858 https://doi.org/10.1016/j.cub.2018.11.070 859 Matheson, A.M.M., Lanz, A.J., Medina, A.M., Licata, A.M., Currier, T.A., Syed, M.H., Nagel, K.I., 2022. 860 A neural circuit for wind-guided olfactory navigation. Nat Commun 13, 4613. 861 https://doi.org/10.1038/s41467-022-32247-7 862 Menzel, R., Greggers, U., Smith, A., Berger, S., Brandt, R., Brunke, S., Bundrock, G., Hülse, S., Plümpe, 863 T., Schaupp, F., Schüttler, E., Stach, S., Stindt, J., Stollhoff, N., Watzl, S., 2005. Honey bees 864 navigate according to a map-like spatial memory. Proc. Natl. Acad. Sci. U.S.A. 102, 3040–3045. 865 https://doi.org/10.1073/pnas.0408550102 866 Menzel, R., Muller, U., 1996. Learning and Memory in Honeybees: From Behavior to Neural 867 Substrates. Annu Rev Neurosci 379–404. 868 https://doi.org/10.1146/annurev.ne.19.030196.002115. 869 Menzel, R., Tison, L., Fischer-Nakai, J., Cheeseman, J., Balbuena, M.S., Chen, X., Landgraf, T., Petrasch, 870 J., Polster, J., Greggers, U., 2018. Guidance of Navigating Honeybees by Learned Elongated 871 Ground Structures. Front Behav Neurosci 12, 322. https://doi.org/10.3389/fnbeh.2018.00322 872 Möller, R., 2002. Insects Could Exploit UV–Green Contrast for Landmark Navigation. Journal of 873 Theoretical Biology 214, 619–631. https://doi.org/10.1006/jtbi.2001.2484 874 Mote, M.I., Wehner, R., 1980. Functional characteristics of photoreceptors in the compound eye and 875 ocellus of the desert ant,Cataglyphis bicolor. J. Comp. Physiol. 137, 63–71. 876 https://doi.org/10.1007/BF00656918 877 Moura, P .A., Young, F.J., Monllor, M., Cardoso, M.Z., Montgomery, S.H., 2023. Long-term spatial 878 memory across large spatial scales in Heliconius butterflies. Current Biology 33, R797–R798. 879 https://doi.org/10.1016/j.cub.2023.06.009 880 Murray, T., Kócsi, Z., Dahmen, H., Narendra, A., Le Möel, F., Wystrach, A., Zeil, J., 2020. The role of 881 attractive and repellent scene memories in ant homing ( Myrmecia croslandi ). J Exp Biol 223, 882 jeb210021. https://doi.org/10.1242/jeb.210021 883 Mussells Pires, P ., Zhang, L., Parache, V., Abbott, L.F., Maimon, G., 2024. Converting an allocentric 884 goal into an egocentric steering signal. Nature 626, 808–818. https://doi.org/10.1038/s41586-885 023-07006-3 886 Namiki, S., Iwabuchi, S., Pansopha Kono, P ., Kanzaki, R., 2014. Information flow through neural 887 circuits for pheromone orientation. Nat Commun 5, 5919. 888 https://doi.org/10.1038/ncomms6919 889 Namiki, S., Kanzaki, R., 2018. Morphology of visual projection neurons supplying premotor area in 890 the brain of the silkmoth Bombyx mori. Cell Tissue Res 374, 497–515. 891 https://doi.org/10.1007/s00441-018-2892-0 892 Namiki, S., Kanzaki, R., 2016a. The neurobiological basis of orientation in insects: insights from the 893 silkmoth mating dance. Current Opinion in Insect Science 15, 16–26. 894 https://doi.org/10.1016/j.cois.2016.02.009 895 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Namiki, S., Kanzaki, R., 2016b. Comparative Neuroanatomy of the Lateral Accessory Lobe in the 896 Insect Brain. Front. Physiol. 7. https://doi.org/10.3389/fphys.2016.00244 897 Narendra, A., Gourmaud, S., Zeil, J., 2013. Mapping the navigational knowledge of individually 898 foraging ants, Myrmecia croslandi. Proc. R. Soc. B 280, 20130683. 899 https://doi.org/10.1098/rspb.2013.0683 900 Nityananda, V., Tarawneh, G., Errington, S., Serrano-Pedraza, I., Read, J., 2017. The optomotor 901 response of the praying mantis is driven predominantly by the central visual field. J Comp 902 Physiol A 203, 77–87. https://doi.org/10.1007/s00359-016-1139-3 903 Nordström, K., O’Carroll, D.C., 2006. Small object detection neurons in female hoverflies. Proceedings 904 of the Royal Society B: Biological Sciences 273, 1211–1216. 905 https://doi.org/10.1098/rspb.2005.3424 906 O’Carroll, D., 1993. Feature-detecting neurons in dragonflies. Nature 362, 541–543. 907 https://doi.org/10.1038/362541a0 908 Olberg, R.M., 1983. Pheromone-triggered flip-flopping interneurons in the ventral nerve cord of the 909 silkworm moth,Bombyx mori. J. Comp. Physiol. 152, 297–307. 910 https://doi.org/10.1007/BF00606236 911 Pansopha, P ., Ando, N., Kanzaki, R., 2014. Dynamic use of optic flow during pheromone tracking by 912 the male silkmoth, Bombyx mori. Journal of Experimental Biology 217, 1811–1820. 913 https://doi.org/10.1242/jeb.090266 914 Paulk, A.C., Gronenberg, W., 2008. Higher order visual input to the mushroom bodies in the bee, 915 Bombus impatiens. Arthropod Structure & Development 37, 443–458. 916 https://doi.org/10.1016/j.asd.2008.03.002 917 Philippides, A., Baddeley, B., Cheng, K., Graham, P ., 2011. How might ants use panoramic views for 918 route navigation? Journal of Experimental Biology 214, 445–451. 919 https://doi.org/10.1242/jeb.046755 920 Philippides, A., de Ibarra, N.H., Riabinina, O., Collett, T.S., 2013. Bumblebee calligraphy: the design 921 and control of flight motifs in the learning and return flights of Bombus terrestris. Journal of 922 Experimental Biology 216, 1093–1104. https://doi.org/10.1242/jeb.081455 923 Portelli, G., Ruffier, F., Roubieu, F.L., Franceschini, N., 2011. Honeybees’ Speed Depends on Dorsal as 924 Well as Lateral, Ventral and Frontal Optic Flows. PLoS ONE 6, e19486. 925 https://doi.org/10.1371/journal.pone.0019486 926 Pratt, S.C., Brooks, S.E., Franks, N.R., 2001. The Use of Edges in Visual Navigation by the Ant 927 Leptothorax albipennis. Ethology 107, 1125–1136. https://doi.org/10.1046/j.1439-928 0310.2001.00749.x 929 Reid, S.F., Narendra, A., Hemmi, J.M., Zeil, J., 2011. Polarised skylight and the landmark panorama 930 provide night-active bull ants with compass information during route following. Journal of 931 Experimental Biology 214, 363–370. https://doi.org/10.1242/jeb.049338 932 Ronacher, B., Gallizzi, K., Wohlgemuth, S., Wehner, R., 2000. Lateral Optic Flow Does Not Influence 933 Distance Estimation in the Desert Ant Cataglyphis Fortis. Journal of Experimental Biology 203, 934 1113–1121. https://doi.org/10.1242/jeb.203.7.1113 935 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Ronacher, B., Wehner, R., 1995. Desert ants Cataglyphislot fortis use self-induced optic flow to 936 measure distances travelled. J Comp Physiol A 177. 937 Rössler, W., 2019. Neuroplasticity in desert ants (Hymenoptera: Formicidae) – importance for the 938 ontogeny of navigation. Myrmecological News. 939 https://doi.org/10.25849/MYRMECOL.NEWS_029:001 940 Rössler, W., Groh, C., 2012. Plasticity of Synaptic Microcircuits in the Mushroom-Body Calyx of the 941 Honey Bee, in: Galizia, C.G., Eisenhardt, D., Giurfa, M. (Eds.), Honeybee Neurobiology and 942 Behavior. Springer Netherlands, Dordrecht, pp. 141–153. https://doi.org/10.1007/978-94-007-943 2099-2_12 944 Ruffier, F., Portelli, G., Serres, J., Raharijaona, T., Franceschini, N., 2019. From bees’ surface following 945 to lunar landing. hal. 946 Scaplen, K.M., Talay, M., Fisher, J.D., Cohn, R., Sorkaç, A., Aso, Y ., Barnea, G., Kaun, K.R., 2021. 947 Transsynaptic mapping of Drosophila mushroom body output neurons. eLife 10, e63379. 948 https://doi.org/10.7554/eLife.63379 949 Schultheiss, P ., Schwarz, S., Cheng, K., Wehner, R., 2013. Foraging ecology of an Australian salt-pan 950 desert ant (genus Melophorus). Aust. J. Zool. 60, 311–319. https://doi.org/10.1071/ZO12096 951 Schultheiss, P ., Wystrach, A., Schwarz, S., Tack, A., Delor, J., Nooten, S.S., Bibost, A.-L., Freas, C.A., 952 Cheng, K., 2016. Crucial role of ultraviolet light for desert ants in determining direction from 953 the terrestrial panorama. Animal Behaviour 115, 19–28. 954 https://doi.org/10.1016/j.anbehav.2016.02.027 955 Schwarz, S., Cheng, K., 2010. Visual associative learning in two desert ant species. Behav Ecol 956 Sociobiol 64, 2033–2041. https://doi.org/10.1007/s00265-010-1016-y 957 Seelig, J.D., Jayaraman, V., 2013. Feature detection and orientation tuning in the Drosophila central 958 complex. Nature 503, 262–266. https://doi.org/10.1038/nature12601 959 Sommer, S., Wehner, R., 2004. The ant’s estimation of distance travelled: experiments with desert 960 ants, Cataglyphis fortis. Journal of Comparative Physiology A: Sensory, Neural, and Behavioral 961 Physiology 190, 1–6. https://doi.org/10.1007/s00359-003-0465-4 962 Srinivasan, M., Laughlin, S.B., Dubs, A., 1982. Predictive coding: a fresh view of inhibition in the 963 retina. Proc. R. Soc. Lond. B. 216, 427–459. https://doi.org/10.1098/rspb.1982.0085 964 Srinivasan, M.V., Lehrer, M., Zhang, S.W., Horridge, G.A., 1989. How honeybees measure their 965 distance from objects of unknown size. J. Comp. Physiol. 165, 605–613. 966 https://doi.org/10.1007/BF00610992 967 Srinivasan, M.V., Zhang, S., Altwein, M., Tautz, J., 2000. Honeybee Navigation: Nature and Calibration 968 of the “Odometer.” Science 287, 851–853. https://doi.org/10.1126/science.287.5454.851 969 Srinivasan, M.V., Zhang, S.W., Lehrer, M., Collett, T.S., 1996. Honeybee Navigation En Route to the 970 Goal: Visual Flight Control and Odometry. Journal of Experimental Biology 199, 237–244. 971 https://doi.org/10.1242/jeb.199.1.237 972 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Steinbeck, F., Adden, A., Graham, P ., 2020. Connecting brain to behaviour: a role for general purpose 973 steering circuits in insect orientation? J Exp Biol 223, jeb212332. 974 https://doi.org/10.1242/jeb.212332 975 Stone, T., Webb, B., Adden, A., Weddig, N.B., Honkanen, A., Templin, R., Wcislo, W., Scimeca, L., 976 Warrant, E., Heinze, S., 2017. An Anatomically Constrained Model for Path Integration in the 977 Bee Brain. Current Biology 27, 3069-3085.e11. https://doi.org/10.1016/j.cub.2017.08.052 978 Stürzl, W., Cheung, A., Cheng, K., Zeil, J., 2008. The information content of panoramic images I: The 979 rotational errors and the similarity of views in rectangular experimental arenas. Journal of 980 Experimental Psychology: Animal Behavior Processes 34, 1–14. https://doi.org/10.1037/0097-981 7403.34.1.1 982 Stürzl, W., Zeil, J., Boeddeker, N., Hemmi, J.M., 2016. How Wasps Acquire and Use Views for Homing. 983 Current Biology 26, 470–482. https://doi.org/10.1016/j.cub.2015.12.052 984 Thomson, J.D., 1996. Trapline foraging by bumblebees: I. Persistence of flight-path geometry. 985 Behavioral Ecology 7, 158–164. https://doi.org/10.1093/beheco/7.2.158 986 Towne, W.F., Moscrip, H., 2008. The connection between landscapes and the solar ephemeris in 987 honeybees. Journal of Experimental Biology 211, 3729–3736. 988 https://doi.org/10.1242/jeb.022970 989 Towne, W.F., Ritrovato, A.E., Esposto, A., Brown, D.F., 2017. Honeybees use the skyline in orientation. 990 Journal of Experimental Biology 220, 2476–2485. https://doi.org/10.1242/jeb.160002 991 Turner-Evans, D.B., Jayaraman, V., 2016. The insect central complex. Current Biology 26, R453–R457. 992 https://doi.org/10.1016/j.cub.2016.04.006 993 Von Frish, K., 1914. Der farbensinn und Formensinn der Biene. 994 von Uexküll, J., 1934. Mondes Animaux Et Monde Humain, Bibliothèque Médiations-Éditions Denoël. 995 ed. 996 Voss, P ., Thomas, M.E., Cisneros-Franco, J.M., de Villers-Sidani, É., 2017. Dynamic Brains and the 997 Changing Rules of Neuroplasticity: Implications for Learning and Recovery. Front. Psychol. 8, 998 1657. https://doi.org/10.3389/fpsyg.2017.01657 999 Wallace, G.K., 1959. Visual Scanning in the Desert Locust Schistocerca Gregaria Forskål. Journal of 1000 Experimental Biology 36, 512–525. https://doi.org/10.1242/jeb.36.3.512 1001 Wallace, G.K., 1958. Some Experiments on Form Perception in the Nymphs of the Desert Locust, 1002 Schistocerca Gregaria Forskål. Journal of Experimental Biology 35, 765–775. 1003 https://doi.org/10.1242/jeb.35.4.765 1004 Webb, B., Wystrach, A., 2016. Neural mechanisms of insect navigation. Current Opinion in Insect 1005 Science 15, 27–39. https://doi.org/10.1016/j.cois.2016.02.011 1006 Wehner, R., 2003. Desert ant navigation: how miniature brains solve complex tasks. Journal of 1007 Comparative Physiology A: Sensory, Neural, and Behavioral Physiology 189, 579–588. 1008 https://doi.org/10.1007/s00359-003-0431-1 1009 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Wehner, R., 1987. ’Matched filters’-neural models of the external world. J. Comp. Physiol. 161, 511–1010 531. https://doi.org/10.1007/BF00603659 1011 Wehner, R., Menzel, R., 1990. Do Insects Have Cognitive Maps? Annu. Rev. Neurosci. 13, 43–414. 1012 Wehner, R., Räber, F., 1979. visual spatial memory in desert ants,Cataglyphis bicolor (Hymenoptera: 1013 Formicidae). experientia 35, 1569–1571. https://doi.org/doi.org/10.1007/BF01953197 1014 Westeinde, E.A., Kellogg, E., Dawson, P .M., Lu, J., Hamburg, L., Midler, B., Druckmann, S., Wilson, R.I., 1015 2024. Transforming a head direction signal into a goal-oriented steering command. Nature 626, 1016 819–826. https://doi.org/10.1038/s41586-024-07039-2 1017 Woodgate, J.L., Buehlmann, C., Collett, T.S., 2016a. When navigating wood ants use a shape’s centre 1018 of mass to extract directional information from a panoramic skyline. Journal of Experimental 1019 Biology jeb.136697. https://doi.org/10.1242/jeb.136697 1020 Woodgate, J.L., Makinson, J.C., Lim, K.S., Reynolds, A.M., Chittka, L., 2016b. Life-Long Radar Tracking 1021 of Bumblebees. PLoS ONE 11, e0160333. https://doi.org/10.1371/journal.pone.0160333 1022 Wystrach, A., 2023. Neurons from pre-motor areas to the Mushroom bodies can orchestrate latent 1023 visual learning in navigating insects (preprint). Neuroscience. 1024 https://doi.org/10.1101/2023.03.09.531867 1025 Wystrach, A., Beugnon, G., Cheng, K., 2011a. Landmarks or panoramas: what do navigating ants 1026 attend to for guidance? Front Zool 8, 21. https://doi.org/10.1186/1742-9994-8-21 1027 Wystrach, A., Lagogiannis, K., Webb, B., 2016. Continuous lateral oscillations as a core mechanism for 1028 taxis in Drosophila larvae. eLife 5. https://doi.org/10.7554/eLife.15504 1029 Wystrach, A., Le Moël, F., Clement, L., Schwarz, S., 2020. A lateralised design for the interaction of 1030 visual memories and heading representations in navigating ants (preprint). Animal Behavior 1031 and Cognition. https://doi.org/10.1101/2020.08.13.249193 1032 Wystrach, A., Mangan, M., Philippides, A., Graham, P ., 2013. Snapshots in ants? New interpretations 1033 of paradigmatic experiments. Journal of Experimental Biology 216, 1766–1770. 1034 https://doi.org/10.1242/jeb.082941 1035 Wystrach, A., Schwarz, S., Schultheiss, P ., Beugnon, G., Cheng, K., 2011b. Views, landmarks, and 1036 routes: how do desert ants negotiate an obstacle course? J Comp Physiol A 197, 167–179. 1037 https://doi.org/10.1007/s00359-010-0597-2 1038 Zeil, J., 2012. Visual homing: an insect perspective. Current Opinion in Neurobiology 22, 285–293. 1039 https://doi.org/10.1016/j.conb.2011.12.008 1040 Zeil, J., 1993. Orientation flights of solitary wasps (Cerceris; Spheeidae; Hymenoptera). J Comp 1041 Physiol A. 1042 Zeil, J., Fleischmann, P .N., 2019. The learning walks of ants (Hymenoptera: Formicidae). 1043 https://doi.org/10.25849/MYRMECOL.NEWS_029:093 1044 Zeil, J., Hofmann, M.I., Chahl, J.S., 2003. Catchment areas of panoramic snapshots in outdoor scenes. 1045 J. Opt. Soc. Am. A, JOSAA 20, 450–469. https://doi.org/10.1364/JOSAA.20.000450 1046 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint Zittrell, F., Pfeiffer, K., Homberg, U., 2020. Matched-filter coding of sky polarization results in an 1047 internal sun compass in the brain of the desert locust. Proceedings of the National Academy of 1048 Sciences 117, 25810–25817. https://doi.org/10.1073/pnas.2005192117 1049 Zollikofer, C.P .E., 1994. STEPPING PATTERNS IN ANTS. Journal of Experimental Biology 192, 107–118. 1050 Zollikofer, C.P .E., Wehner, R., Fukushi, T., 1995. Optical Scaling in Conspecific Cataglyphis Ants. J. Exp. 1051 Biol. 198, 1637–1646. https://doi.org/10.1242/jeb.198.8.1637 1052 1053 1054 1055 Supplemental information 1056 1057 Figure S1: Power spectral density sample for different experimental manipulation. (A-1058 D) Examples of ‘power spectral density’ four ants recorded with translational optic flow 1059 (TOF; A, light green) and without TOF (B, green) or recorded in visual surrounding made of 1060 vertical (C, grey) and horizontal (D, light grey) stripes. (A-D) Distribution of the individual 1061 Fourier dominant peak obtained from angular velocities times series across 160s. For each 1062 individual, the frequency peak with the highest magnitude was extracted, indicating a strong 1063 oscillation of the angular velocity signal at that frequency (dashed red lines). 1064 1065 .CC-BY-NC-ND 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprintthis version posted August 29, 2024. ; https://doi.org/10.1101/2024.08.28.610048doi: bioRxiv preprint