Sexual dimorphism in sensorimotor transformation of optic flow

Sexual dimorphism in sensorimotor transformation of optic flow eLife Assessment Hoverflies are known for their sexually dimorphic visual systems and exquisite flight behaviors. This valuable study reports how two types of visual descending neurons differ between males and females in their motion- and speed-dependent responses, yet surprisingly, the behavior they control lacks any sexual dimorphism. The results convincingly support these findings, which will be of interest for studies of visuomotor transformations and network-level brain organization. https://doi.org/10.7554/eLife.109795.3.sa0Valuable: Findings that have theoretical or practical implications for a subfield - Landmark - Fundamental - Important - Valuable - Useful Convincing: Appropriate and validated methodology in line with current state-of-the-art - Exceptional - Compelling - Convincing - Solid - Incomplete - Inadequate During the peer-review process the editor and reviewers write an eLife Assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) and the strength of the evidence (on a scale ranging from exceptional to inadequate). Learn more about eLife Assessments Abstract Motion vision underpins a wide range of adaptive behaviours essential for individual and species survival. In hoverflies, some visual behaviours are sexually dimorphic, including for example male high-speed pursuit of conspecifics matched by improved optics and faster photoreceptors. Other visual behaviours are sexually monomorphic, with for example similar foraging flight speeds in male and female hoverflies. However, whether the descending neurons responsible for sensorimotor transformation of optic flow are sexually dimorphic is unknown. To address this, we combined morphological analysis with electrophysiology of optic flow sensitive descending neurons and compared neural responses to the behavioural output in tethered hoverflies. We found that while optomotor flight behaviour is largely sexually monomorphic, the underlying neural responses are sexually dimorphic, especially at higher optic flow velocities. Additionally, behavioural responses were noticeably slower than neural responses. Together, our findings uncover a nuanced, sex- and stimulus-dependent sensorimotor transformation, shaped by both neural architecture and behavioural demands. Introduction Motion vision is a fundamental sensory modality across the animal kingdom, enabling animals to navigate, to maintain a straight trajectory, to avoid collisions, and to identify prey, predators, or mates. Among the most potent cues for self-motion is widefield optic flow, the coherent motion of the entire visual field, generated by an animal’s own movement through the world. In insects, the neural mechanisms underlying optic flow processing have been studied extensively (for a review, see e.g. Mauss and Borst, 2020), offering a powerful model for understanding how compact nervous systems extract behaviourally relevant information from dynamic visual scenes. To generate appropriate responses to widefield optic flow, the visual input needs to be integrated across space. In flies, this spatial pooling occurs in 45–60 lobula plate tangential cells (LPTCs) (Pierantoni, 1976; Zhao et al., 2023), each matched to a particular type of self-generated optic flow (Franz and Krapp, 2000), where the horizontal system (HS) and vertical system (VS) cells are the most well described. HS cells respond optimally to rotations around the yaw axis, whereas VS cells are tuned to pitch and roll rotations (Krapp et al., 1998). LPTCs project to the inferior posterior slope (Wei et al., 2020), where they synapse with descending neurons (Strausfeld and Bassemir, 1985a; Strausfeld and Seyan, 1985b). In Drosophila at least 35 descending neuron types have their inputs in the posterior surface of the brain (named DNp1-35) (Namiki et al., 2018). Furthermore, in Drosophila and blowflies, three of these descending neurons have been shown to respond robustly to widefield optic flow. Their axons project to the dorsal part of the thoracic ganglion, where motor neurons controlling the neck, wings and halteres are located (Strausfeld and Bassemir, 1985a; Suver et al., 2016; Erginkaya et al., 2023; Haikala et al., 2013; Pokusaeva et al., 2024). DNp15, also called DNHS1, receives input from HS cells (Namiki et al., 2018; Suver et al., 2016; Erginkaya et al., 2023) and is physiologically similar to the optic flow sensitive descending neuron type 1 (OFS DN1) in the hoverfly Eristalis tenax (Nicholas et al., 2020a), although direct LPTC coupling has not yet been demonstrated in the hoverfly. DNHS1 projects to the neck and haltere motor neuropils (Namiki et al., 2018) and has been implicated in the control of head yaw movements, abdominal ruddering and flight stabilization via the haltere motor system (Suver et al., 2016). DNp20, or DNOVS1, receiving input from the ocelli and VS cells (Strausfeld and Bassemir, 1985a; Namiki et al., 2018; Suver et al., 2016; Haag et al., 2007), is likely involved in rapid head movements (Namiki et al., 2018), possibly facilitating gaze stabilization during flight (Suver et al., 2016). DNp22, or DNOVS2, also receives input from the ocelli but a different subset of VS cells (Strausfeld and Bassemir, 1985a; Namiki et al., 2018; Suver et al., 2016; Wertz et al., 2008). DNOVS2 is physiologically similar to the E. tenax optic flow sensitive descending neuron type 2 (OFS DN2) (Nicholas et al., 2020a), but synaptic coupling with VS cells has not been shown in hoverflies. DNOVS2 projects to the neck, wing and haltere motor neuropils (Namiki et al., 2018) and has been implied in the initiation of the fast body saccades that support rapid re-orientation (Suver et al., 2016) during flight in response to dynamic visual cues. Hoverflies are interesting in the context of motion vision, which they use both to maintain a hovering stance and to fly at high speed (Collett and Land, 1975). Indeed, hoverflies show striking sexually dimorphic flight behaviour, where males establish territories which they guard rigorously from a hovering stance (Wellington and Fitzpatrick, 1981), followed by high-speed flight to chase away any intruding insects and/or pursue conspecific females for courtship and mating (Collett and Land, 1975; Collett and Land, 1978). Male hoverflies are also smaller than females (Daňková et al., 2023; Nicholas et al., 2018b), which introduces an aerodynamic component by reducing inertia and enabling finer control over rapid flight adjustments (Dudley, 2002). Accompanying this sexually dimorphic pursuit behaviour, male E. tenax have larger lenses than females in a dorso-frontal bright zone (Straw et al., 2006), with faster motion detection and increased signal-to-noise ratio (van Hateren et al., 1989). In many fly species, the photoreceptors in this part of the eye are also faster in males (Hornstein et al., 2000). In hoverflies, even if the LPTCs are typically implied in optomotor responses (Mauss and Borst, 2020), males have a smaller HSN receptive field (Nordström et al., 2008) and velocity tuning shifted to higher velocities (Straw et al., 2006; Barnett et al., 2010). These adaptations are likely useful in regulating optomotor responses during high-speed target pursuit (Nicholas and Nordström, 2021; Ghosh et al., 2025). Indeed, as target selective descending neurons (TSDNs) are suppressed when target and background move in the same direction (Nicholas and Nordström, 2021; Nicholas et al., 2018a), the optic flow sensitive descending neurons may serve a complementary role in stabilizing flight under these conditions. The differences in optics, photoreceptor dynamics, and LPTC receptive field size and velocity tuning have been interpreted as required by males in the fast flight used during sexually dimorphic territorial behaviours. Interestingly, there is no sexual dimorphism in flight speed during other behaviours likely to be governed primarily by the LPTCs, such as foraging between flowers (Thyselius et al., 2018) and when flying within the confines of an indoor arena (Thyselius et al., 2023). To investigate the discrepancy between sexually dimorphic visual processing and sexually monomorphic flight speed, we compared the electrophysiological response characteristics and morphology of optic flow sensitive descending neurons in male and female hoverflies and compared these findings to the behaviour of tethered hoverflies viewing similar stimuli. We used the wing beat amplitude (WBA) as a measure of the optomotor response and found no sexual dimorphism at speeds up to 2 m/s for translation and 200°/s for rotation. While the head movements were largely sexually monomorphic, the extension of the fore- and hind legs exhibited clearer sexual dimorphism. Furthermore, while neural morphology, receptive fields and direction sensitivity of the descending neurons showed minimal sex differences, there was a significant and noticeable difference in the velocity response functions between males and females, especially at higher speeds. Critically, neural differences were not only velocity dependent but also varied between stimuli (occurring for sideslip, lift, and thrust but not roll) and neuron type. These neuron-, stimulus-, and sex-specific differences uncover a previously unrecognized complexity in the neural encoding of visual motion, revealing a sex-dependent transformation from sensory input to motor output. Results Two distinct types of optic flow sensitive descending neurons can be identified by their receptive field location and preferred direction Optic flow sensitive descending neurons can be readily identified by mapping their receptive field using small sinusoidal gratings (Nicholas et al., 2020a; Straw et al., 2006). Based on the receptive fields of 100 reference neurons recorded from 90 male hoverflies, we found that two key parameters, the azimuthal position of the receptive field centre and its preferred direction of motion, are sufficient to reliably ascertain neuron type (Figure 1, Figure 1—figure supplement 1). OFS DN1 has a preferred direction up and away from the midline, either leftward (range from 137° to 171°) or rightward (range from 16° to 40°) for neurons on the left- and right-hand side of the visual field, respectively (Figure 1A–C; green, Figure 1G–I). OFS DN2 responds preferentially to downward motion (range from 228° to 293°; Figure 1D–F; yellow and orange, Figure 1G–I) with the azimuthal position of the receptive field centre separating left-hand side (LHS) from right-hand side (RHS) neurons (Figure 1G–I). We used the maximum local motion sensitivity (LMS, Figure 1C, F), the extent of the receptive fields (number of positions with LMS over 50%, arrows in Figure 1C, F) and the local preferred direction (LPD, Figure 1C, F) variance from these 100 reference neurons (grey data, Figure 1—figure supplement 2A–C) to set strict exclusion criteria (dashed red, Figure 1—figure supplement 2A–C) of the neurons used in the rest of the paper. This resulted in the exclusion of two neurons from males and seven from females (grey, Figure 1—figure supplement 2D, E) due to either low LMS (less than 20 spikes/s, Figure 1—figure supplement 2A), a small number of locations where LMS exceeded 50% of the maximum (four positions or less, Figure 1—figure supplement 2B) or high LPD variance (above 30°, Figure 1—figure supplement 2C). We found no sexual dimorphism in either neuron type when comparing receptive field width and height of the remaining 33 male and 29 female neurons (Figure 1—figure supplement 2F, unpaired t-test, p = 0.52 and 0.09 for width and height of OFS DN1, and p = 0.19 and 0.13 for width and height of OFS DN2). Directional tuning of optic flow sensitive descending neurons exhibits limited sexual dimorphism Some LPTCs, which are upstream of optic flow sensitive descending neurons, show distinct sexual dimorphism, while others do not (Straw et al., 2006; Nordström et al., 2008). The receptive field data used for classifying OFS DN1 and DN2 showed that they are strongly directional (Figure 1G, I). To investigate if this direction tuning is sexually dimorphic, we used a separate experiment quantifying responses to full-screen sinusoidal grating stimuli (wavelength 7°, 5 Hz, Figure 2—figure supplement 1). We found that the resulting preferred direction of both OFS DN1 and OFS DN2 matched their receptive field preferred directions (compare polar plots in Figure 1—figure supplement 2D, E, with Figure 2—figure supplement 1G, H), that is up and away from the visual midline for OFS DN1 (range from 359° to 52°) or downwards for OFS DN2 (range from 273° to 297°, Figure 2A). While OFS DN1 showed no difference in preferred direction between the sexes (Watson–Williams two-sample test, p = 0.38), male OFS DN2 had a slightly more lateral preferred direction compared to females (Figure 2A; median = 285.4° compared to 281.5°, Watson–Williams two-sample test, p = 0.046). We quantified the spontaneous rate and found that neither this nor the responses of OFS DN1 to a stationary starfield pattern differed between the sexes (circles, Figure 2—figure supplement 2A, two-way ANOVA, p = 0.29). Conversely, OFS DN2 exhibited significant sexual dimorphism, with females displaying a higher spontaneous rate and response to stationary stimuli than males (circles, Figure 2—figure supplement 2B, two-way ANOVA, p 180°/s), rather than broadly regulating WBA across all velocities, including those examined in this study. Beyond their potential involvement in wingbeat amplitude modulation, OFS DNs may play a role in coordinating head and body positioning during flight, especially in contexts requiring precise visual alignment and rapid manoeuvring. Indeed, the Drosophila physiological homologs of OFS DN1 and OFS DN2 (Nicholas et al., 2020a), DNHS1 and DNOVS2, project to the neck motor neuropil (Namiki et al., 2018), and have been implicated in controlling head movements, abdominal ruddering, and engagement with the haltere motor system for flight stabilization (Suver et al., 2016). While we did not quantify abdominal or haltere movements, we found that the head turned when viewing sideslip optic flow (Figure 7E), and that the velocity dependence was similar to neural (Figure 3E) and WBAD (Figure 5G) responses. While we did not detect any head movements to roll, lift or thrust, this could be a technical limitation of recording from above (Figure 5A, Videos 4–9). Indeed, many of the 29 descending neuron types that project to the wing motor neuropil also project to the neck motor neuropil (Namiki et al., 2018), suggesting that synchronized responses are not unexpected. Neither are the similar responses of the fore- and hind legs surprising (Figure 7F, G), considering that in Drosophila many of the ~30 descending neuron types that project to the forelegs also project to the mid- and hind legs (Namiki et al., 2018). Our WBA recordings would also be affected by filming from above and thus lacking other nuanced changes in wing angles. Thus, the sexual dimorphism in neural responses (Figure 3) could reflect an evolutionary tuning of visuomotor pathways in males, optimized for fast, directional adjustments rather than gross changes in WBA in the horizontal plane. For instance, while changes in WBAS are similar when generating either lift or thrust (Figure 5H), body pitch dynamically adjusts their ratio, driving behavioural variation (Zanker, 1990). Thus, OFS DNs may serve as integral components of a broader flight control architecture, interfacing optic flow detection with dynamic body and head positioning systems to support complex, sex-specific behavioural outcomes, particularly in males engaging in high-speed pursuits. Indeed, flight speed in insects results from a complex integration of multiple kinematic parameters, not solely from changes in wingbeat amplitude. Many species refine their aerodynamic output by modulating wingbeat frequency, angle of attack, wing tip trajectory, deviations from the mean stroke plane and through precise adjustments to the timing and duration of the up- and downstrokes (Sane, 2003). These control strategies support agile manoeuvring, particularly during visually guided behaviours such as the high-speed pursuits undertaken by male hoverflies. Furthermore, the smaller body size of male hoverflies compared to females (Daňková et al., 2023; Nicholas et al., 2018b) may confer biomechanical advantages, including reduced mass, facilitating faster acceleration, heightened responsiveness, and lower metabolic costs for executing flight manoeuvres (Dudley, 2002; Niven and Scharlemann, 2005), which do not appear in a tethered flight set-up. Such enhanced agility is likely crucial for rapid direction changes required during courtship and may enable male hoverflies to outperform female flies without relying on increased wingbeat amplitude. Taken together, our findings reveal significant differences between sexually dimorphic sensory encoding and conserved motor output in hoverfly flight at cruising speeds. Although OFS DNs and their upstream visual circuits display clear sex-specific tuning, wingbeat amplitude and head angle changes in response to optic flow stimuli remain relatively similar between the sexes, suggesting additional mechanisms downstream of sensory input. This likely reflects a complex interplay of biomechanical properties, multisensory integration and circuit-level modulation, each shaping and refining behavioural outcomes to meet distinct demands, preserving the consistency of low-speed manoeuvres while enabling sex-specific tuning during high-speed pursuits. Methods Animals For all experiments, male and female E. tenax were reared and housed as described previously (Nicholas et al., 2018b). Briefly, eggs were collected from females captured under permit in Wittunga Botanic Garden, Adelaide, South Australia. Upon hatching, larvae were reared in a rabbit dung slurry until third instar larvae emerged to pupate. Eclosion occurred 1–2 weeks post-pupation. Adult hoverflies were used for behavioural experiments at 17–87 days post-eclosion, for intracellular electrophysiology and subsequent morphological reconstruction at 38–54 days, and for extracellular electrophysiology at 8–204 days. Electrophysiology Before recording the animal was immobilised, mounted dorsal side down (Figure 5—figure supplement 1A) and secured using a mixture of beeswax and resin. A small region of cuticle was removed at the anterior end of the thorax to expose the cervical connective. If required, any excessive gut or tracheal tissue obstructing the recording site was removed and a small volume of PBS was added to prevent drying within the ventral cavity. A fine wire hook was positioned under the cervical connective for mechanical support, and a silver wire was inserted into the cavity to serve as a reference electrode and grounding wire (Nicholas et al., 2020a). For extracellular recordings, a sharp tungsten microelectrode (2 MΩ, polyimide-insulated; Microprobes) was inserted into the cervical connective (Nicholas et al., 2020a). Signals were amplified 1000 times and band-pass filtered between 10 and 3000 Hz using a DAM50 differential amplifier (World Precision Instruments), followed by noise reduction with a HumBug (Quest Scientific). Data were digitized via a PowerLab 4/30 interface (ADInstruments) and acquired at 40 kHz. Spike sorting was performed in LabChart 7 Pro (ADInstruments) based on the amplitude and width of individual action potentials. For intracellular recordings, aluminosilicate electrodes were pulled using a Sutter P-1000 micropipette puller, achieving a resistance of approximately 40–70 MΩ. Electrode tips were filled with 3% neurobiotin (Vector Laboratories), then backfilled with 1 M KCl using a syringe, leaving a small air bubble between the two solutions. Electrodes were inserted into the cervical connective for recording, and the resulting signal was amplified using an Axoclamp-2B amplifier (Axon Instruments), followed by 50 Hz noise reduction with a HumBug (Quest Scientific). Data acquisition and digitization were performed at 10 kHz using an NI USB-6210 16-bit data acquisition card (National Instruments) and the MATLAB Data Acquisition Toolbox (Mathworks), using in house software (https://github.com/HoverflyLab/SampSamp, copy archived at HoverflyLab, 2026). Morphological reconstructions Following intracellular recordings, neurons were stained iontophoretically with neurobiotin using currents in the 1 nA range for 3–12 min. The nervous system was then carefully dissected and fixed in 4% paraformaldehyde overnight. Tissue was incubated with a Cy3-streptavidin conjugate (1:100; Jackson ImmunoResearch) for 2 hr, then dehydrated through an ethanol series (50–100%) for 15–20 min per step. After washing in PBT, the tissue was cleared in RapiClear (SUNJIN Lab) and mounted with spacers. Imaging was performed using a Zeiss LSM 880 Fast Airyscan confocal microscope at the institutional microscopy facility. Neuron morphology and cervical connective width were quantified using ImageJ (Schneider et al., 2012). Tethered flight Prior to flight recordings, hoverflies were tethered at a 32° angle (Figure 5—figure supplement 1B) using a beeswax–resin mixture to a small pin inserted into a hypodermic needle (BD Microlance, 23G × 1¼"). Flight was initiated by manually providing airflow for 1–10 min until consistent flight behaviour was observed. Once positioned facing the centre of the visual monitor (Figure 5—figure supplement 1B), hoverflies were filmed from above at 100 Hz using a Sony PlayStation 3 Move Eye Camera (SLEH-00448, Sony) with the IR filter removed, and equipped with an infrared pass filter (R72 INFRARED, 49 mm, HOYA; for details see Ogawa et al., 2025). Illumination was provided by infrared LEDs inserted into USB lights (JANSJÖ LED USB lamp, IKEA) and a Musou Black (Shin Kokushoku Musou black, KOYO Orient Japan) surface was placed beneath the hoverfly to enhance contrast and minimize optical interference. We used DeepLabCut (DLC) version 2.3.3 (Mathis et al., 2018) to train a model to track the thorax and the peak downstroke angle, referred to as the WBA, of the left and right wing (WBAL and WBAR; Figure 5A, B), as described previously (Ogawa et al., 2025). In addition, we tracked the dorso-medial head (yellow, green, Figure 7A, B), the proximal and distal points of the forelegs (blue, Figure 7A, C), the hind leg knee and the lateral, mid-abdomen (magenta, green, Figure 7A, D). For this purpose, we first manually labelled the anatomical landmarks (Figures 5A and 7A), across 16 extracted video frames per individual from four hoverflies (2 males, 2 females). In addition to examples where the hoverfly was not flying, these frames included responses to yaw rotation and forward translation. The DLC model was trained for 300,000 iterations, yielding train and test errors of 1.2 and 1.16 pixels, respectively. To identify potential tracking errors from DLC, we smoothed the WBA time series using MATLAB’s smooth function with both loess and rloess methods. Because rloess is more resistant to outliers, we used it to detect abnormal data points. For each wing, if the absolute difference between the loess- and rloess-smoothed signals exceeded 5% for more than 1 s, the data were excluded due to suspected tracking artefacts. The smoothing was used only for error detection; all subsequent analyses were performed on the unsmoothed data. In addition, we excluded data if the WBA of either wing dropped below 40° for at least 0.5 s, as this indicated a cessation of flight. Finally, entire trials were excluded if the hoverfly was not flying for more than 50% of the trial duration. Head and leg kinematic data were excluded whenever the corresponding WBA data were excluded, ensuring consistent trial inclusion across behavioural metrics. Visual stimuli Visual stimuli were generated using custom software (https://github.com/HoverflyLab/FlyFly, HoverflyLab, 2025) written in MATLAB, incorporating the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997). All screens had a refresh rate of 165 Hz and a linearized contrast with a mean illuminance of 200 Lux. For intracellular recordings, the hoverfly was placed 13 cm away from a ViewSonic screen with a resolution of 2560 × 1440 pixels, corresponding to 143° × 107° of the visual field. For extracellular recordings, the hoverfly was placed 6.5 cm away from a 2560 × 1440 pixel Asus screen, yielding a projected visual field of 155° × 138° (Figure 5—figure supplement 1A). For behavioural recordings, the hoverfly was placed 10 cm from a vertically orientated Asus screen (1440 × 2560 pixel, Figure 5—figure supplement 1B) producing a projected size of 118° × 142°. To evaluate the impact of different screen orientations in electrophysiology and behaviour, visual stimuli were presented either full-screen or using the central 1440 × 1440 pixel square (Figure 5—figure supplement 1C, D). Receptive field mapping To map each neuron’s receptive field, we presented local sinusoidal gratings (average 38° × 38°) drifting in 8 directions for 0.36 s each, across 48 overlapping locations, as described previously (Nicholas et al., 2020a). Stimuli were full contrast, with an average spatial frequency of 0.14 cycles/° and a temporal frequency of 5 Hz. At each location, we calculated the local maximum spike frequency (red, inset, Figure 1B, E). After subtracting the spontaneous rate, calculated for 0.8 s preceding stimulus onset (dotted line, inset, Figure 1B, E), we interpolated the resulting local maximum responses to a tenfold higher spatial resolution (colour coding, Figure 1A, D). We then quantified the 50% response maximum using MATLAB’s contour function (black, Figure 1B, E). We created a polygon-shape of this 50% contour line using MATLAB’s polyshape function to calculate the receptive field size, defined as its width and height (Figure 1—figure supplement 2F), and used the centroid function to identify the centre of this polyshape (red circle, Figure 1B, E). As recordings were performed with the animal ventral side up (Figure 2—figure supplement 2A, Figure 5—figure supplement 1A), receptive fields were rotated to display them with the dorsal visual field up. Next, at each location, we fit a cosine function to the responses to the different directions of motion, where the LPD and LMS are the peak position and amplitude of the cosine fit, respectively (inset, Figure 1C, F). These were visualized as vectors where angle indicates LPD and length indicates LMS (arrows, Figure 1A, D). We defined the receptive field preferred direction as the median of LPDs at locations where LMS exceeded 50% of the maximum (black and red arrows; Figure 1C, F), using circ_median from the CircStat toolbox for MATLAB (Berens, 2009). We calculated the LPD variance using the circ_var functions from CircStat toolbox in MATLAB (Berens, 2009), of LPDs at locations where LMS exceeded 50% of the maximum (black and red arrows; Figure 1C, F). We extracted unpublished receptive field data from 100 reference neurons (other data from five of these optic flow sensitive descending neurons has been published previously Nicholas and Nordström, 2021) to set exclusion criteria (red shading, Figure 1—figure supplement 2A–C) and to classify the OFS DNs used in the rest of this study. OFS DN1 on the LHS were defined by receptive field preferred directions between 120° and 200° (light green, Figure 1G, I). OFS DN1 on the RHS were defined by receptive field preferred directions between 340° and 60° (dark green, Figure 1G, I). OFS DN2 were classified by receptive field preferred directions between 220° and 320° (yellow and orange, Figure 1G, I), with the azimuthal location of the receptive field centre determining whether they were LHS or RHS (yellow and orange, Figure 1H). A polar plot where the distance from origo indicates azimuthal position, and the location along the radius the preferred direction, confirms that the LHS and RHS of OFS1 and OFS2 cluster distinctly (Figure 1—figure supplement 1A). To test whether incorporating additional receptive field parameters would reveal further neuronal subtypes, we performed k-means clustering in MATLAB using z-score normalised data. The additional parameters included receptive field centre elevation, receptive field height and width (see Figure 1—figure supplement 2F), maximum LMS (see Figure 1—figure supplement 2A), the number of positions with LMS values exceeding 50% of the maximum (see Figure 1—figure supplement 2B), and LPD variance (see Figure 1—figure supplement 2C). However, the (Caliński and Harabasz, 1974) values indicate that adding additional receptive field parameters actually reduced clustering performance (Figure 1—figure supplement 1B). In contrast, clustering based solely on preferred direction and azimuthal location yielded four well-defined groups (Figure 1—figure supplement 1C) and produced the highest Callinski–Harabasz value (Figure 1—figure supplement 1B). Directional sensitivity Full-screen sinusoidal gratings were presented at full contrast, using the same spatial (0.14 cycles/°) and temporal (5 Hz) frequencies as in receptive field mapping. For each stimulus direction, mean spike frequency was calculated over the 1-s stimulus duration, excluding the first 100 ms to avoid onset transients (Nicholas and Nordström, 2020b). We fit a cosine function to the responses to the eight different directions of motion, to extract the preferred direction and response amplitude (Figure 2—figure supplement 1B), and then plotted these values for OFS DN1 (Figure 2—figure supplement 1C) and DN2 neurons (Figure 2—figure supplement 1D). To account for the ventral-side-up recording position (Figure 2—figure supplement 1A) directional responses were rotated 180° (Figure 2—figure supplement 1F–H). In addition, to display all neurons as RHS, and assuming mirror-symmetry, we flipped LHS neurons across the midline (Figure 2—figure supplement 1I–L). Responses to optic flow We used a starfield stimulus to generate the type of perspective-corrected optic flow that would have been seen by the hoverfly if it was moving through a space of 2-cm diameter spheres (for details, see Nicholas et al., 2020a; Leibbrandt et al., 2021). These simulated translations at 0.5 m/s (sideslip, lift, and thrust) or rotations (pitch, yaw, and roll), at 50°/s. To quantify neural responses, the mean spike frequency was calculated over the 0.97 s stimulus duration, excluding the first 0.1 s to avoid onset-related transients (Nicholas and Nordström, 2020b). The spontaneous firing rate, averaged across 0.48 s immediately preceding stimulus onset (open circles, Figure 2—figure supplement 2A, B), was subtracted from the response. Velocity response functions We used four types of optic flow: three translations (sideslip, lift, and thrust, Videos 2–7) and one rotation (roll, Video 8, Video 8 and Video 9). Translations were presented at velocities of −2, –1.5, −1, –0.4, −0.2, –0.1, 0, 0.1, 0.2, 0.4, 1, 1.5, and 2 m/s, while rotations were presented at 200, –150, −100, –40, −20, –10, 0, 10, 20, 40, 100, 150, and 200°/s. The sign of the velocity indicates the direction of motion as seen by the fly when corrected for its position, with positive values corresponding to counterclockwise roll, leftward sideslip, downward lift, and thrust moving away. Conversely, negative values indicate clockwise roll, rightward sideslip, upward lift, and thrust moving toward the hoverfly. The upper limit was defined by the movement of the individual dots within the starfield stimulus between frames, that is it was limited by the refresh rate of the screen. Each trial consisted of 39 stimuli (13 unique velocities × 3 repetitions), presented in a random order, with each stimulus lasting 2 s, immediately followed by the next velocity. Trials began with a 1-s blank screen, which served as the pre-stimulation baseline (Figures 3A and 5C). Each optic flow condition was repeated multiple times, resulting in at least nine repetitions for each velocity and neuron, and five repetitions for each velocity and animal in behaviour. For neuronal recordings, velocity response function trials were interleaved with the optic flow stimuli described above. In behavioural experiments, a flight refresher sequence interspersed each velocity tuning trial. This consisted of sinusoidal gratings (200° wavelength, 5 Hz) drifting rightward, leftward, and rightward again for 4 s each. Neural responses were quantified as the mean spike frequency during the final second of stimulation (grey shaded areas, Figure 3A–C). We then calculated the median across trials for each velocity (Figure 3D). Pre-stimulation activity (spontaneous rate) was measured over a 2-s window immediately preceding stimulus onset. Neural responses are displayed after subtracting the response when viewing a stationary stimulus (filled symbols, Figure 2—figure supplement 2A, B). In behavioural experiments, we extracted the mean WBA of the left and right wings (, , Figure 5B) during the final second of stimulation (grey shaded areas, in Figure 5C–E). For each velocity, we calculated the WBA difference (WBAD, defined as ) and the WBA sum (WBAS, defined as , Figure 5B). We then calculated the median across repetitions for each individual (Figure 5F). Pre-stimulation responses were averaged over a 0.5-s time window immediately preceding stimulus onset. WBA responses are displayed after subtracting the response when viewing a stationary stimulus (filled symbols, Figure 2—figure supplement 2C). We defined the head angle as the angle between a straight line joining the 2 tracked points on the head (using polyfit in MATLAB, Figure 7A, B) and the longitudinal axis of the thorax (Figure 7A, B). As we filmed in one plane only, this measurement can be caused by a combination of head rotations but seems to be dominated by roll rotations (Videos 2–9). We measured the distance between the proximal and distal points of the forelegs (Figure 7A, C). Note that the forelegs were mostly hidden under the animal from our dorsal view and could only be seen when extended anteriorly (e.g. Figure 5A, see also Videos 2–9). Data are displayed as the mean extension of the left and right foreleg. We measured the distance between the hind leg knee and a lateral point on the mid-abdomen (Figure 7A, D). Hind leg data are displayed as the mean or the difference of the left and right hind leg. All body parts data are displayed after subtracting the response when viewing a stationary stimulus (filled symbols show hindleg data, Figure 2—figure supplement 2D). Response onset For neuronal recordings, we compared onset times for clockwise roll (+50°/s) and either upwards lift (+0.5 m/s) for OFS DN 1 or downwards lift (–0.5 m/s) for OFS DN2, stimuli which generated strong responses in these neurons (Figure 3E, F). Mean spike frequency was calculated over the 0.97 s stimulus duration, excluding the first 0.1 s to avoid onset transients (Nicholas and Nordström, 2020b). Onset was defined as the first time point after the first 0.1 s, where spike rate exceeded 80% of the mean. For behaviour, we used WBA responses to roll (–200°/s) and lift (+2 m/s). Mean WBAS was defined as the average response during the final second of stimulation, and onset was defined as the first time point where WBAS exceeded 80% of this mean. We also quantified WBAS, head, fore-and hind leg onsets to sideslip (+2 m/s). For each body part, we quantified the mean response during the final second of stimulation, and onset was defined as the first time point where the response exceeded 80% of this mean. Statistics Throughout the text n refers to individual repetitions, whereas N refers to individual neurons (electrophysiology) or animals (behaviour). All data are presented as median and interquartile range, unless otherwise indicated. Statistical analyses were performed in Prism 10.4.0 (GraphPad Software), except for circular statistics, which were conducted using the CircStat toolbox for MATLAB (Berens, 2009). The results of the statistical tests are given in the text, figure legends and in Tables 1 and 2. For behavioural data the p-values were Bonferroni adjusted (Abdi, 2007) for multiple comparisons (Table 2). Data availability All data and analysis scripts have been submitted to DataDryad: https://doi.org/10.5061/dryad.tb2rbp0fd. - Dryad Digital RepositorySexual dimorphism in sensorimotor transformation of insect optic flow.https://doi.org/10.5061/dryad.tb2rbp0fd References - Motion adaptation and the velocity coding of natural scenesCurrent Biology 20:994–999.https://doi.org/10.1016/j.cub.2010.03.072 - CircStat: a MATLAB toolbox for circular statisticsJournal of Statistical Software 31:1–21.https://doi.org/10.18637/jss.v031.i10 - A dendrite method for cluster analysisCommunications in Statistics 3:1–27.https://doi.org/10.1080/03610927408827101 - Visual control of flight behaviour in the hoverflySyritta pipiens LJournal of Comparative Physiology? A 99:1–66.https://doi.org/10.1007/BF01464710 - How hoverflies compute interception coursesJournal of Comparative Physiology ? A 125:191–204.https://doi.org/10.1007/BF00656597 - Mechanisms and implications of animal flight maneuverabilityIntegrative and Comparative Biology 42:135–140.https://doi.org/10.1093/icb/42.1.135 - Wide-field, motion-sensitive neurons and matched filters for optic flow fieldsBiological Cybernetics 83:185–197.https://doi.org/10.1007/s004220000163 - Understanding the mechanism of facilitation in hoverfly TSDNsPLOS Computational Biology 21:e1012986.https://doi.org/10.1371/journal.pcbi.1012986 - Integration of lobula plate output signals by DNOVS1, an identified premotor descending neuronThe Journal of Neuroscience 27:1992–2000.https://doi.org/10.1523/JNEUROSCI.4393-06.2007 - Optogenetic control of fly optomotor responsesThe Journal of Neuroscience 33:13927–13934.https://doi.org/10.1523/JNEUROSCI.0340-13.2013 - Sexual dimorphism matches photoreceptor performance to behavioural requirementsProceedings of the Royal Society of London. Series B 267:2111–2117.https://doi.org/10.1098/rspb.2000.1257 - Dendritic structure and receptive-field organization of optic flow processing interneurons in the flyJournal of Neurophysiology 79:1902–1917.https://doi.org/10.1152/jn.1998.79.4.1902 - The impulse response of optic flow-sensitive descending neurons to roll m-sequencesThe Journal of Experimental Biology 224:jeb242833.https://doi.org/10.1242/jeb.242833 - Active flight increases the gain of visual motion processing in DrosophilaNature Neuroscience 13:393–399.https://doi.org/10.1038/nn.2492 - DeepLabCut: markerless pose estimation of user-defined body parts with deep learningNature Neuroscience 21:1281–1289.https://doi.org/10.1038/s41593-018-0209-y - Optic flow-based course control in insectsCurrent Opinion in Neurobiology 60:21–27.https://doi.org/10.1016/j.conb.2019.10.007 - Rearing and long-term maintenance of eristalis tenax hoverflies for research studiesJournal of Visualized Experiments e57711:57711.https://doi.org/10.3791/57711 - Visual motion sensitivity in descending neurons in the hoverflyJournal of Comparative Physiology A 206:149–163.https://doi.org/10.1007/s00359-020-01402-0 - Sexual dimorphism in the hoverfly motion vision pathwayCurrent Biology 18:661–667.https://doi.org/10.1016/j.cub.2008.03.061 - Combining Unity with machine vision to create low latency, flexible and simple virtual realitiesMethods in Ecology and Evolution 16:126–144.https://doi.org/10.1111/2041-210X.14449 - The VideoToolbox software for visual psychophysics: transforming numbers into moviesSpatial Vision 10:437–442. - A look into the cock-pit of the fly. The architecture of the lobular plateCell and Tissue Research 171:101–122.https://doi.org/10.1007/BF00219703 - The aerodynamics of insect flightThe Journal of Experimental Biology 206:4191–4208.https://doi.org/10.1242/jeb.00663 - NIH Image to ImageJ: 25 years of image analysisNature Methods 9:671–675.https://doi.org/10.1038/nmeth.2089 - Convergence of visual, haltere, and prosternai inputs at neck motor neurons of Calliphora erythrocephalaCell and Tissue Research 240:601–615.https://doi.org/10.1007/BF00216350 - A “bright zone” in male hoverfly (Eristalis tenax) eyes and associated faster motion detection and increased contrast sensitivityThe Journal of Experimental Biology 209:4339–4354.https://doi.org/10.1242/jeb.02517 - An array of descending visual interneurons encoding self-motion in DrosophilaThe Journal of Neuroscience 36:11768–11780.https://doi.org/10.1523/JNEUROSCI.2277-16.2016 - Spatial organization of visuomotor reflexes in DrosophilaThe Journal of Experimental Biology 207:113–122.https://doi.org/10.1242/jeb.00724 - Dynamics of optomotor responses in Drosophila to perturbations in optic flowThe Journal of Experimental Biology 213:1366–1375.https://doi.org/10.1242/jeb.037945 - Visual approach computation in feeding hoverfliesThe Journal of Experimental Biology 221:jeb177162.https://doi.org/10.1242/jeb.177162 - Hoverfly (Eristalis tenax) pursuit of artificial targetsThe Journal of Experimental Biology 226:jeb244895.https://doi.org/10.1242/jeb.244895 - The bright zone, a specialized dorsal eye region in the male blowflyChrysomyia megacephalaJournal of Comparative Physiology A 164:297–308.https://doi.org/10.1007/BF00612990 - The diversity of lobula plate tangential cells (LPTCs) in the Drosophila motion vision systemJournal of Comparative Physiology A 206:139–148.https://doi.org/10.1007/s00359-019-01380-y - Teritoriality in the drone fly, Eristalis tenax (Diptera: Syrphidae)The Canadian Entomologist 113:695–704.https://doi.org/10.4039/Ent113695-8 - Nonlinear integration of binocular optic flow by DNOVS2, a descending neuron of the flyThe Journal of Neuroscience 28:3131–3140.https://doi.org/10.1523/JNEUROSCI.5460-07.2008 - The wing beat of Drosophila melanogaster.3Control. Philos T R Soc B 327:45–64.https://doi.org/10.1098/rstb.1990.0042 Article and author information Author details Funding US Air Force of Scientific Research (FA9550-19-1-0294) - Karin Nordström US Air Force of Scientific Research (FA9550-23-1-0473) - Yuri Ogawa - Karin Nordström Australian Research Council (DP210100740) - Karin Nordström Australian Research Council (DP230100006) - Karin Nordström Australian Research Council (DP250104770) - Yuri Ogawa Australian Research Council (DP250100698) - Karin Nordström The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication. Acknowledgements We thank Biomedical Engineering at SAHLN and the Botanic Gardens of Adelaide for their ongoing support. We thank the editor and the two Reviewers for their feedback which greatly improved our paper. This research was funded by the US Air Force Office of Scientific Research (AFOSR, FA9550-19-1-0294 and FA9550-23-1-0473) and the Australian Research Council (ARC, DP210100740, DP230100006, DP250100698, and DP250104770). Version history - Preprint posted: - Sent for peer review: - Reviewed Preprint version 1: - Reviewed Preprint version 2: - Version of Record published: Cite all versions You can cite all versions using the DOI https://doi.org/10.7554/eLife.109795. This DOI represents all versions, and will always resolve to the latest one. Copyright © 2026, Nicholas et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Metrics - - 442 - views - - 12 - downloads - - 0 - citations Views, downloads and citations are aggregated across all versions of this paper published by eLife.