A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

The visual world projects a complex and rapidly changing image onto the retina of many animal species. This presents computational challenges for those animals reliant on visual processing to provide an accurate representation of the world. One such challenge is parsing a visual scene for the most salient targets, such as the selection of prey amid a swarm. The ability to selectively prioritize processing of some stimuli over others is known as 'selective attention'. We recently identified a dragonfly visual neuron called 'Centrifugal Small Target Motion Detector 1′ (CSTMD1) that exhibits selective attention when presented with multiple, equally salient targets. Here we conducted in vivo, electrophysiological recordings from CSTMD1 in wild-caught male dragonflies (Hemicordulia tau), while presenting visual stimuli on an LCD monitor. To identify the target selected in any given trial, we uniquely modulated the intensity of the moving targets (frequency tagging). We found that the frequency information of the selected target is preserved in the neuronal response, while the distracter is completely ignored. We also show that the competitive system that underlies selection in this neuron can be biased by the presentation of a preceding target on the same trajectory, even when it is of lower contrast than an abrupt, novel distracter. With this improved method for identifying and biasing target selection in CSTMD1, the dragonfly provides an ideal animal model system to probe the neuronal mechanisms underlying selective attention. SIGNIFICANCE STATEMENT We present the first application of frequency tagging to intracellular neuronal recordings, demonstrating that the frequency component of a stimulus is encoded in the spiking response of an individual neuron. Using this technique as an identifier, we demonstrate that CSTMD1 'locks on' to a selected target and encodes the absolute strength of this target, even in the presence of abruptly appearing, high-contrast distracters. The underlying mechanism also permits the selection mechanism to switch between targets mid-trial, even among equivalent targets. Together, these results demonstrate greater complexity in this selective attention system than would be expected in a winner-takes-all network. These results are in contrast to typical findings in the primate and avian brain, but display intriguing resemblance to observations in human psychophysics.

[1]  S. Combes,et al.  Linking biomechanics and ecology through predator–prey interactions: flight performance of dragonflies and their prey , 2012, Journal of Experimental Biology.

[2]  M. Carandini,et al.  Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.

[3]  M. Juusola,et al.  Intrinsic Activity in the Fly Brain Gates Visual Information during Behavioral Choices , 2010, PloS one.

[4]  David C O'Carroll,et al.  Correlation between OFF and ON Channels Underlies Dark Target Selectivity in an Insect Visual System , 2013, The Journal of Neuroscience.

[5]  Michael D Greenfield,et al.  Females prefer leading males: relative call timing and sexual selection in katydid choruses , 1998, Animal Behaviour.

[6]  Eric Ruthruff,et al.  Immunity to attentional capture at ignored locations , 2017, Attention, Perception, & Psychophysics.

[7]  G. Pollack,et al.  Selective attention in an insect auditory neuron , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  S. Treue,et al.  Attentional Modulation Strength in Cortical Area MT Depends on Stimulus Contrast , 2002, Neuron.

[9]  D. Simons,et al.  Do New Objects Capture Attention? , 2005, Psychological science.

[10]  C. Chabris,et al.  Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.

[11]  J. Terborgh,et al.  Oddity and the ‘confusion effect’ in predation , 1986, Animal Behaviour.

[12]  Bruno van Swinderen,et al.  Competing visual flicker reveals attention-like rivalry in the fly brain , 2012, Front. Integr. Neurosci..

[13]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[14]  Anthony Leonardo,et al.  Internal models direct dragonfly interception steering , 2014, Nature.

[15]  S. Yantis,et al.  Abrupt visual onsets and selective attention: evidence from visual search. , 1984, Journal of experimental psychology. Human perception and performance.

[16]  B. Hedwig,et al.  Contralateral inhibition as a sensory bias: the neural basis for a female preference in a synchronously calling bushcricket, Mecopoda elongata , 2002, The European journal of neuroscience.

[17]  Bart R. H. Geurten,et al.  Neural mechanisms underlying target detection in a dragonfly centrifugal neuron , 2007, Journal of Experimental Biology.

[18]  M. Heisenberg,et al.  Vision in Flies: Measuring the Attention Span , 2016, PloS one.

[19]  Steven Grainger,et al.  Performance of an insect-inspired target tracker in natural conditions , 2017, Bioinspiration & biomimetics.

[20]  Eli Brenner,et al.  Flexibility in intercepting moving objects. , 2007, Journal of vision.

[21]  David C. O'Carroll,et al.  Spatial facilitation by a high-performance dragonfly target-detecting neuron , 2011, Biology Letters.

[22]  K. Hausen Motion sensitive interneurons in the optomotor system of the fly , 1982, Biological Cybernetics.

[23]  M. May,et al.  Foraging behavior ofPachydiplax longipennis (Odonata: Libellulidae) , 1997, Journal of Insect Behavior.

[24]  M. Heisenberg,et al.  Attracting the attention of a fly , 2011, Proceedings of the National Academy of Sciences.

[25]  J. C. Johnston,et al.  Involuntary attentional capture by abrupt onsets , 1992, Perception & psychophysics.

[26]  Edward F. Ester,et al.  Substitution and pooling in visual crowding induced by similar and dissimilar distractors. , 2015, Journal of vision.

[27]  Steven Mark Miller,et al.  Attentional Switching in Humans and Flies: Rivalry in Large and Miniature Brains , 2012, Front. Hum. Neurosci..

[28]  Steven Grainger,et al.  An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments , 2017, Journal of neural engineering.

[29]  Justin M. Ales,et al.  The steady-state visual evoked potential in vision research: A review. , 2015, Journal of vision.

[30]  Eric I. Knudsen,et al.  STIMULUS-DRIVEN COMPETITION IN A CHOLINERGIC MIDBRAIN NUCLEUS , 2010, Nature Neuroscience.

[31]  R. Olberg,et al.  Eye movements and target fixation during dragonfly prey-interception flights , 2007, Journal of Comparative Physiology A.

[32]  M. Srinivasan,et al.  Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees , 2015, Journal of Experimental Biology.

[33]  David O'Carroll,et al.  Feature-detecting neurons in dragonflies , 1993, Nature.

[34]  Leslie G. Ungerleider,et al.  Microsaccadic eye movements and firing of single cells in the striate cortex of macaque monkeys , 2000, Nature Neuroscience.

[35]  Katherine F. Weiner,et al.  Inattention Blindness to Motion in Middle Temporal Area , 2013, The Journal of Neuroscience.

[36]  George A Alvarez,et al.  How many objects can you track? Evidence for a resource-limited attentive tracking mechanism. , 2007, Journal of vision.

[37]  David C. O'Carroll,et al.  Properties of predictive gain modulation in a dragonfly visual neuron , 2018, Journal of Experimental Biology.

[38]  R. Tollrian,et al.  Prey swarming: which predators become confused and why? , 2007, Animal Behaviour.

[39]  David C. O'Carroll,et al.  Salience invariance with divisive normalization in higher-order insect neurons , 2016, 2016 6th European Workshop on Visual Information Processing (EUVIP).

[40]  S. Treue Neural correlates of attention in primate visual cortex , 2001, Trends in Neurosciences.

[41]  J. Maunsell,et al.  Spatial Summation Can Explain the Attentional Modulation of Neuronal Responses to Multiple Stimuli in Area V4 , 2008, The Journal of Neuroscience.

[42]  V. Nityananda Attention-like processes in insects , 2016, Proceedings of the Royal Society B: Biological Sciences.

[43]  Taylor L. Bobrow,et al.  Transient Signals and Inattentional Blindness in a Multi-object Tracking Task , 2018, i-Perception.

[44]  S. Morad,et al.  Ceramide-orchestrated signalling in cancer cells , 2012, Nature Reviews Cancer.

[45]  R. Desimone,et al.  Interacting Roles of Attention and Visual Salience in V4 , 2003, Neuron.

[46]  R. Olberg,et al.  Prey size selection and distance estimation in foraging adult dragonflies , 2005, Journal of Comparative Physiology A.

[47]  David C. O'Carroll,et al.  Contrast sensitivity and the detection of moving patterns and features , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[48]  David C O'Carroll,et al.  A predictive focus of gain modulation encodes target trajectories in insect vision , 2017, eLife.

[49]  Klaus Hausen,et al.  Motion sensitive interneurons in the optomotor system of the fly , 1982, Biological Cybernetics.

[50]  R. Wurtz,et al.  Responses of MT and MST neurons to one and two moving objects in the receptive field. , 1997, Journal of neurophysiology.

[51]  Patrick A. Shoemaker,et al.  Facilitation of dragonfly target-detecting neurons by slow moving features on continuous paths , 2012, Front. Neural Circuits.

[52]  B. Swinderen,et al.  Evidence for selective attention in the insect brain , 2016 .

[53]  T. Labhart,et al.  The dorsal eye of the dragonfly Sympetrum: specializations for prey detection against the blue sky , 1995, Journal of Comparative Physiology A.

[54]  R. Desimone,et al.  Attention Increases Sensitivity of V4 Neurons , 2000, Neuron.

[55]  P. H. Schiller,et al.  The role of the primate extrastriate area V4 in vision. , 1991, Science.

[56]  J. Maunsell,et al.  Effects of Attention on the Processing of Motion in Macaque Middle Temporal and Medial Superior Temporal Visual Cortical Areas , 1999, The Journal of Neuroscience.

[57]  Steven D. Wiederman,et al.  Selective Attention in an Insect Visual Neuron , 2013, Current Biology.

[58]  D. O’Carroll,et al.  Differential Tuning to Visual Motion Allows Robust Encoding of Optic Flow in the Dragonfly , 2019, The Journal of Neuroscience.