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Research Articles: Systems/Circuits Integration of Small- and Wide-Field Visual Features in Target-Selective Descending Neurons of both Predatory and Non-Predatory Dipterans Sarah Nicholas1, Jack Supple3, Richard Leibbrandt1, Paloma T Gonzalez-Bellido2,3 and Karin Nordström1 1Centre for Neuroscience, Flinders University, GPO Box 2100, 5001 Adelaide, SA, Australia 2Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108, USA 3Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3EG, UK https://doi.org/10.1523/JNEUROSCI.1695-18.2018 Received: 9 July 2018 Revised: 18 October 2018 Accepted: 21 October 2018 Published: 29 October 2018 Author contributions: S.J.N., J.S., R.L., P.T.G.-B., and K.N. designed research; S.J.N., J.S., and P.T.G.-B. performed research; S.J.N., J.S., P.T.G.-B., and K.N. analyzed data; S.J.N., J.S., R.L., P.T.G.-B., and K.N. edited the paper; S.J.N., J.S., P.T.G.-B., and K.N. wrote the paper; K.N. wrote the first draft of the paper. Conflict of Interest: The authors declare no competing financial interests. This work was funded by the Air Force Office of Scientific Research (FA9550-15-1-0188 to P.T. Gonzalez- Bellido and K. Nordström) and the Australian Research Council (DP170100008 to P.T. Gonzalez-Bellido and K. Nordström and DP180100144 to K. Nordström). We thank Vanshika Sinh for constructive feedback on the manuscript, Malin Thyselius for the Eristalis pictograms and The Adelaide Botanic Gardens for continuous support. Corresponding author: Karin Nordström, [email protected] Cite as: J. Neurosci 2018; 10.1523/JNEUROSCI.1695-18.2018 Alerts: Sign up at www.jneurosci.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2018 Nicholas et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. 1 Integration of Small- and Wide-Field Visual Features in Target-Selective Descending Neurons of 2 both Predatory and Non-Predatory Dipterans 3 Sarah Nicholas*1, Jack Supple*3, Richard Leibbrandt1, Paloma T Gonzalez-Bellido#2,3, Karin 4 Nordström#1 5 1Centre for Neuroscience, Flinders University, GPO Box 2100, 5001 Adelaide, SA, Australia; 6 2Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108, USA; 7 3Department of Physiology, Development and Neuroscience, University of Cambridge, CB2 3EG, UK 8 Corresponding author: Karin Nordström, [email protected] 9 *These authors contributed equally 10 #These authors contributed equally 11 Number of pages: 21 pages 12 Number of figures, tables, multimedia and 3D models (separately): 4 figures, 1 table 13 Number of words for Abstract, Introduction, and Discussion (separately) 14 Abstract: 245 15 Introduction: 641 16 Discussion: 1363 17 Conflict of Interest 18 The authors declare no conflict of interest. 19 Acknowledgements 20 This work was funded by the Air Force Office of Scientific Research (FA9550-15-1-0188 to P.T. 21 Gonzalez-Bellido and K. Nordström) and the Australian Research Council (DP170100008 to P.T. 22 Gonzalez-Bellido and K. Nordström and DP180100144 to K. Nordström). We thank Vanshika Sinh for 23 constructive feedback on the manuscript, Malin Thyselius for the Eristalis pictograms and The Adelaide 24 Botanic Gardens for continuous support. 25 Keywords 26 Motion vision, target detection, target tracking, optic flow, dipteran TSDN, electrophysiology, neural 27 tuning, background clutter 1 28 Abstract 29 For many animals, target motion carries high ecological significance as this may be generated by a 30 predator, prey or potential mate. Indeed, animals whose survival depends on early target detection are 31 often equipped with a sharply tuned visual system, yielding robust performance in challenging 32 conditions. For example, many fast-flying insects use visual cues for identifying targets, such as prey 33 (e.g. predatory dragonflies and robberflies) or conspecifics (e.g. non-predatory hoverflies), and can 34 often do so against self-generated background optic flow. Supporting these behaviors, the optic lobes 35 of insects that pursue targets harbor neurons that respond robustly to the motion of small moving 36 objects, even when displayed against syn-directional background clutter. However, in diptera, the 37 encoding of target information by the descending neurons, which are more directly involved in 38 generating the behavioral output, has received less attention. We characterized target selective 39 neurons by recording in the ventral nerve cord of male and female predatory Holcocephala fusca 40 robberflies and of male non-predatory Eristalis tenax hoverflies. We show that both species have 41 dipteran target-selective descending neurons (dTSDNs) that only respond to target motion if the 42 background is stationary or moving slowly, moves in the opposite direction, or has un-naturalistic 43 spatial characteristics. The response to the target is suppressed when background and target move at 44 similar velocities, which is strikingly different to the response of target neurons in the optic lobes. As the 45 neurons we recorded from are pre-motor, our findings affect our interpretation of the neurophysiology 46 underlying target-tracking behaviors. 47 48 Significance Statement 49 Many animals use sensory cues to detect moving targets that may represent predators, prey or 50 conspecifics. For example, birds of prey show superb sensitivity to the motion of small prey, and 51 intercept these at high speeds. In a similar manner, predatory insects visually track moving prey, often 52 against cluttered backgrounds. Accompanying this behavior, the brains of insects that pursue targets 53 contain neurons that respond exclusively to target motion. We here show that dipteran insects also 54 have target selective descending neurons in the part of their nervous system that corresponds to the 55 vertebrate spinal cord. Surprisingly, and in contrast to the neurons in the brain, these pre-motor 56 neurons are inhibited by background patterns moving in the same direction as the target. 57 2 58 Introduction 59 Target detection and tracking serve important biological functions for animals to efficiently avoid 60 predators, find prey or identify conspecifics. Target detection can be performed by different senses. 61 Bats find prey with echolocation (Falk et al., 2014), squid embryos avoid predators with their lateral line 62 system (York et al., 2016), and insects visually identify conspecifics (Land and Collett, 1974). Often, 63 targets need to be visualized against self-generated optic flow, which is a difficult computational task 64 (Yang et al., 2012; Held et al., 2016), especially in conditions where both local luminance and relative 65 contrast may change rapidly (Mohamed et al., 2014; Ma et al., 2015). Nonetheless, many insects 66 appear to have solved this efficiently, as evidenced by their high-speed pursuits of targets, which is 67 particularly impressive considering that insects carry low-spatial resolution compound eyes and small 68 brains (Land, 1997). 69 Notably, many insects that pursue targets display local adaptations. For example, the eyes of target- 70 pursuing insects often have faster photoreceptors (Weckström and Laughlin, 1995; Burton and 71 Laughlin, 2003; Gonzalez-Bellido et al., 2011) and a region with increased spatial resolution, a fovea, in 72 which they try to place the image of the target during aerial pursuits (Collett, 1980; Olberg et al., 2007; 73 Wardill et al., 2017). The optic lobes harbor target sensitive neurons with receptive fields that often 74 collocate with the optical fovea (Strausfeld, 1980; Barnett et al., 2007), such as small target motion 75 detector (STMD) neurons (O'Carroll, 1993; Nordström et al., 2006). STMDs could relay signals to 76 target-selective descending neurons (TSDNs; Olberg, 1981; Olberg, 1986; Gronenberg and Strausfeld, 77 1990; Namiki et al., 2018) whose receptive fields also colocate with the optical fovea (Gonzalez-Bellido 78 et al., 2013). 79 In the lobula, some hoverfly and dragonfly STMDs show remarkably robust responses to targets 80 moving in visual clutter (Nordström and O'Carroll, 2009). In fact, the STMD response to target motion is 81 unaffected by the addition of background motion with the same velocity (i.e. syn-directional motion, 82 without relative velocity differences; Nordström et al., 2006; Wiederman and O'Carroll, 2011). This can 83 be modelled (Wiederman et al., 2008) using the associated unique spatio-temporal profile, where the 84 target’s dark contrast edge (OFF) is immediately followed by a bright contrast change (ON). Models 85 incorporating temporal correlation of half-wave rectified ON and OFF signals from the same point in 86 space, together with fast adaptation and strong spatial inhibition, lead to robust responses to target 87 motion in visual clutter (Wiederman et al., 2008). To date it is largely unclear how TSDNs respond to 88 targets moving in clutter (except one data trace, see Olberg, 1986). This is important as TSDNs are 89 closer to the motor output (Gonzalez-Bellido et al., 2013), and may thus be more pertinent for 90 interpreting the behavioral relevance of neurophysiological recordings. Besides the TSDNs, there are 3 91 many other descending neurons (Namiki et al., 2018), such as those that respond to background 92 motion (Wertz et al., 2009b; Wertz et al., 2009a). These project to areas of the thoracic ganglia with 93 motor neurons of the neck, wings and halteres (Suver et al., 2016). 94 As the ventral nerve cord forms an information bottleneck between the central processing of visually 95 driven information and peripheral transformation into motor output (Namiki et al., 2018), we here 96 quantified the response to moving targets and visual clutter.