![]() Such improved spatial resolution ( Hornstein et al., 2000 Burton and Laughlin, 2003) and contrast sensitivity ( Gonzalez-Bellido et al., 2011) can enable the detection of smaller and dimmer targets ( Straw et al., 2006 Rigosi et al., 2017). ![]() These include physical adaptations, such as faster photoreceptors ( Weckström and Laughlin, 1995) and more acute and sensitive subregions of the eye ( Horridge, 1978 Olberg et al., 2007). This makes insects an attractive model for investigating the neuronal computations underlying these complex, sensorimotor processes.īiological systems employ a variety of strategies to improve target tracking in clutter. Despite having relatively small brains and limited visual resolution ( Horridge, 1978 Land, 1997), flying insects can detect and pursue small moving targets in natural scenes. Numerous species have developed strategies for detecting small targets across a variety of sensory modalities, including auditory localization in bats ( Arlettaz et al., 2001), the lateral line organ in squid ( York et al., 2016) or visual cues in insects ( Nordström and O’Carroll, 2006 Wiederman and O’Carroll, 2011), archerfish ( Schuster et al., 2006) and humans ( Bravo and Farid, 2004). This task often involves the detection of small targets against highly cluttered scenes, including distracting features such as falling leaves or other animals. In diverse species of animals, the ability to detect prey, predators and mates within the environment is essential to survival. ![]() However, whether this neuronal system could underlie the task of competitively selecting slow moving prey against fast-moving backgrounds remains an open question. In many scenarios, CSTMD1 responds robustly to targets moving through cluttered scenes. Our results highlight that CSTMD1’s competitive responses are to those features best matched to the neuron’s underlying spatiotemporal tuning, whether from the embedded target or other features in the background clutter. Additionally, the background’s direction of motion affects discriminability, though not in the manner observed in STMDs of other flying insects. Here, robust discrimination of our artificially embedded “target” is limited to scenarios when its velocity is matched to, or greater than, the background velocity. We find that background motion affects CSTMD1 responses via the competitive selection between features within the natural scene. We examine CSTMD1 response changes to target contrast, as well as a range of target and background velocities. ![]() Here, we describe intracellular responses of CSTMD1 (an identified STMD) to the visual presentation of targets embedded within cluttered, natural scenes. When presented with a pair of targets, some STMDs generate spiking activity that represent a competitive selection of one target, as if the alternative does not exist (i.e., selective attention). These Small Target Motion Detector (STMD) neurons are tuned to both target size and velocity, whilst also exhibiting facilitated responses to targets traveling along continuous trajectories. This task is likely supported by a group of neurons in the optic lobe which respond to moving targets that subtend less than a few degrees. 2Department of Biology, Lund University, Lund, SwedenĪerial predators, such as the dragonfly, determine the position and movement of their prey even when both are moving through complex, natural scenes.1School of Biomedicine, The University of Adelaide, Adelaide, SA, Australia.Bernard John Essex Evans 1* David Charles O’Carroll 2 Joseph Mahandas Fabian 1 Steven D.
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