Literature DB >> 25487153

Internal models direct dragonfly interception steering.

Matteo Mischiati1, Huai-Ti Lin1, Paul Herold1, Elliot Imler2, Robert Olberg3, Anthony Leonardo1.   

Abstract

Sensorimotor control in vertebrates relies on internal models. When extending an arm to reach for an object, the brain uses predictive models of both limb dynamics and target properties. Whether invertebrates use such models remains unclear. Here we examine to what extent prey interception by dragonflies (Plathemis lydia), a behaviour analogous to targeted reaching, requires internal models. By simultaneously tracking the position and orientation of a dragonfly's head and body during flight, we provide evidence that interception steering is driven by forward and inverse models of dragonfly body dynamics and by models of prey motion. Predictive rotations of the dragonfly's head continuously track the prey's angular position. The head-body angles established by prey tracking appear to guide systematic rotations of the dragonfly's body to align it with the prey's flight path. Model-driven control thus underlies the bulk of interception steering manoeuvres, while vision is used for reactions to unexpected prey movements. These findings illuminate the computational sophistication with which insects construct behaviour.

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Year:  2014        PMID: 25487153     DOI: 10.1038/nature14045

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  27 in total

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Authors:  M Kawato
Journal:  Curr Opin Neurobiol       Date:  1999-12       Impact factor: 6.627

2.  Internal models of target motion: expected dynamics overrides measured kinematics in timing manual interceptions.

Authors:  Myrka Zago; Gianfranco Bosco; Vincenzo Maffei; Marco Iosa; Yuri P Ivanenko; Francesco Lacquaniti
Journal:  J Neurophysiol       Date:  2003-11-19       Impact factor: 2.714

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Authors:  James F A Poulet; Berthold Hedwig
Journal:  Nature       Date:  2002-08-22       Impact factor: 49.962

Review 4.  Optimality principles in sensorimotor control.

Authors:  Emanuel Todorov
Journal:  Nat Neurosci       Date:  2004-09       Impact factor: 24.884

Review 5.  Computational mechanisms of sensorimotor control.

Authors:  David W Franklin; Daniel M Wolpert
Journal:  Neuron       Date:  2011-11-03       Impact factor: 17.173

6.  A battery-free multichannel digital neural/EMG telemetry system for flying insects.

Authors:  Stewart J Thomas; Reid R Harrison; Anthony Leonardo; Matthew S Reynolds
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2012-10       Impact factor: 3.833

Review 7.  Studying sensorimotor integration in insects.

Authors:  Stephen J Huston; Vivek Jayaraman
Journal:  Curr Opin Neurobiol       Date:  2011-06-24       Impact factor: 6.627

8.  Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction.

Authors:  Paloma T Gonzalez-Bellido; Hanchuan Peng; Jinzhu Yang; Apostolos P Georgopoulos; Robert M Olberg
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-03       Impact factor: 11.205

9.  Tiger beetles pursue prey using a proportional control law with a delay of one half-stride.

Authors:  Andreas F Haselsteiner; Cole Gilbert; Z Jane Wang
Journal:  J R Soc Interface       Date:  2014-04-09       Impact factor: 4.118

10.  An internal model for sensorimotor integration.

Authors:  D M Wolpert; Z Ghahramani; M I Jordan
Journal:  Science       Date:  1995-09-29       Impact factor: 47.728

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  68 in total

1.  Neuroscience: Dragonflies predict and plan their hunts.

Authors:  Stacey A Combes
Journal:  Nature       Date:  2014-12-10       Impact factor: 49.962

2.  Properties of neuronal facilitation that improve target tracking in natural pursuit simulations.

Authors:  Zahra M Bagheri; Steven D Wiederman; Benjamin S Cazzolato; Steven Grainger; David C O'Carroll
Journal:  J R Soc Interface       Date:  2015-07-06       Impact factor: 4.118

3.  A faithful internal representation of walking movements in the Drosophila visual system.

Authors:  Terufumi Fujiwara; Tomás L Cruz; James P Bohnslav; M Eugenia Chiappe
Journal:  Nat Neurosci       Date:  2016-10-31       Impact factor: 24.884

4.  Insect-machine Hybrid System: Remote Radio Control of a Freely Flying Beetle (Mercynorrhina torquata).

Authors:  T Thang Vo Doan; Hirotaka Sato
Journal:  J Vis Exp       Date:  2016-09-02       Impact factor: 1.355

5.  Learning the trajectory of a moving visual target and evolution of its tracking in the monkey.

Authors:  Clara Bourrelly; Julie Quinet; Patrick Cavanagh; Laurent Goffart
Journal:  J Neurophysiol       Date:  2016-09-28       Impact factor: 2.714

6.  Wiring patterns from auditory sensory neurons to the escape and song-relay pathways in fruit flies.

Authors:  Hyunsoo Kim; Mihoko Horigome; Yuki Ishikawa; Feng Li; J Scott Lauritzen; Gwyneth Card; Davi D Bock; Azusa Kamikouchi
Journal:  J Comp Neurol       Date:  2020-02-19       Impact factor: 3.215

7.  Body orientation contributes to modelling the effects of gravity for target interception in humans.

Authors:  Barbara La Scaleia; Francesco Lacquaniti; Myrka Zago
Journal:  J Physiol       Date:  2019-02-06       Impact factor: 5.182

8.  Convergent Temperature Representations in Artificial and Biological Neural Networks.

Authors:  Martin Haesemeyer; Alexander F Schier; Florian Engert
Journal:  Neuron       Date:  2019-07-31       Impact factor: 17.173

Review 9.  The aerodynamics and control of free flight manoeuvres in Drosophila.

Authors:  Michael H Dickinson; Florian T Muijres
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2016-09-26       Impact factor: 6.237

Review 10.  Quantifying behavior to solve sensorimotor transformations: advances from worms and flies.

Authors:  Adam J Calhoun; Mala Murthy
Journal:  Curr Opin Neurobiol       Date:  2017-08-30       Impact factor: 6.627

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