A Synthetic Nervous System Model of the Insect Optomotor Response
323
Our ultimate goal is to implement this model of insect vision onboard our insectlike hexapod robot, MantisBot [12]. In our past work, we have studied how descending
commands from the brain may alter leg-local reflexes to direct walking behaviors [18].
We anticipate that descending pathways that mediate the optomotor response observed
in walking insects will provide additional information with which MantisBot can stabilize its posture. We also anticipate that the gaze stabilization afforded by the optomotor
response will make MantisBot more capable of identifying prey-like visual stimuli moving against the background. Such a system will enable us to generate and test hypothetical
sensorimotor control networks both to consolidate results from neuroethology into one
cohesive model, and to propose novel control algorithms for legged robots.
References
1. Egelhaaf, M., Boeddeker, N., Kern, R., Kurtz, R., Lindemann, J.P.: Spatial vision in insects is
facilitated by shaping the dynamics of visual input through behavioural action. Front. Neural
Circuits. 6, 1–23 (2012)
2. Bagheri, Z.M., Wiederman, S.D., Cazzolato, B.S., Grainger, S., O’Carroll, D.C.: Performance
of an insect-inspired target tracker in natural conditions. Bioinspir. Biomim. 12, 025006 (2017)
3. Borst, Alexander., Haag, Jürgen, Mauss, Alex S.: How fly neurons compute the direction of
visual motion. J. Comp. Physiol. A. 206(2), 109–124 (2019). https://doi.org/10.1007/s00359019-01375-9
4. Webb, B.: Robots with insect brains. Science. 368, 244–245 (2020)
5. Suver, M.P., Huda, A., Iwasaki, N., Safarik, S., Dickinson, M.H.: An array of descending
visual interneurons encoding self-motion in drosophila. J. Neurosci. 36, 11768–11780 (2016)
6. Rossel, S.: Foveal fixation and tracking in the praying mantis. J. Comp. Physiol. A 139,
307–331 (1980)
7. Nityananda, V., Tarawneh, G., Errington, S., Serrano-Pedraza, I., Read, J.: The optomotor
response of the praying mantis is driven predominantly by the central visual field. J. Comp.
Physiol. A. 203(1), 77–87 (2016). https://doi.org/10.1007/s00359-016-1139-3
8. Dürr, V., Ebeling, W.: The behavioural transition from straight to curve walking: kinetics of
leg movement parameters and the initiation of turning. J. Exp. Biol. 208, 2237–2252 (2005)
9. Meyer, H.G., et al.: Resource-efficient bio-inspired visual processing on the hexapod walking
robot HECTOR. PLoS ONE 15, e0230620 (2020)
10. Szczecinski, N.S., Goldsmith, C.A., Young, F.R., Quinn, R.D.: Tuning a robot servomotor to
exhibit muscle-like dynamics. In: Conference on Biomimetic and Biohybrid Systems. (2019)
11. Cofer, D.W., Cymbalyuk, G., Reid, J., Zhu, Y., Heitler, W.J., Edwards, D.H.: AnimatLab: a 3D
graphics environment for neuromechanical simulations. J. Neurosci. Methods. 187, 280–288
(2010)
12. Szczecinski, N.S., et al.: Introducing MantisBot: hexapod robot controlled by a high-fidelity,
real-time neural simulation. In: IEEE International Conference on Intelligent Robots and
Systems. pp. 3875–3881. Hamburg, DE (2015)
13. Joly, J.-S., Recher, G., Brombin, A., Ngo, K., Hartenstein, V.: A conserved developmental
mechanism builds complex visual systems in insects and vertebrates. Curr. Biol. 26, 1–9
(2016)
14. Mihalas, S., Niebur, E.: A generalized linear integrate-and-fire neural model produces diverse
spiking behaviors. Neural Comput. 21, 704–718 (2009)
15. Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: A functional subnetwork approach to designing
synthetic nervous systems that control legged robot locomotion. Front. Neurorobot. 11, 37
(2017)
323
Our ultimate goal is to implement this model of insect vision onboard our insectlike hexapod robot, MantisBot [12]. In our past work, we have studied how descending
commands from the brain may alter leg-local reflexes to direct walking behaviors [18].
We anticipate that descending pathways that mediate the optomotor response observed
in walking insects will provide additional information with which MantisBot can stabilize its posture. We also anticipate that the gaze stabilization afforded by the optomotor
response will make MantisBot more capable of identifying prey-like visual stimuli moving against the background. Such a system will enable us to generate and test hypothetical
sensorimotor control networks both to consolidate results from neuroethology into one
cohesive model, and to propose novel control algorithms for legged robots.
References
1. Egelhaaf, M., Boeddeker, N., Kern, R., Kurtz, R., Lindemann, J.P.: Spatial vision in insects is
facilitated by shaping the dynamics of visual input through behavioural action. Front. Neural
Circuits. 6, 1–23 (2012)
2. Bagheri, Z.M., Wiederman, S.D., Cazzolato, B.S., Grainger, S., O’Carroll, D.C.: Performance
of an insect-inspired target tracker in natural conditions. Bioinspir. Biomim. 12, 025006 (2017)
3. Borst, Alexander., Haag, Jürgen, Mauss, Alex S.: How fly neurons compute the direction of
visual motion. J. Comp. Physiol. A. 206(2), 109–124 (2019). https://doi.org/10.1007/s00359019-01375-9
4. Webb, B.: Robots with insect brains. Science. 368, 244–245 (2020)
5. Suver, M.P., Huda, A., Iwasaki, N., Safarik, S., Dickinson, M.H.: An array of descending
visual interneurons encoding self-motion in drosophila. J. Neurosci. 36, 11768–11780 (2016)
6. Rossel, S.: Foveal fixation and tracking in the praying mantis. J. Comp. Physiol. A 139,
307–331 (1980)
7. Nityananda, V., Tarawneh, G., Errington, S., Serrano-Pedraza, I., Read, J.: The optomotor
response of the praying mantis is driven predominantly by the central visual field. J. Comp.
Physiol. A. 203(1), 77–87 (2016). https://doi.org/10.1007/s00359-016-1139-3
8. Dürr, V., Ebeling, W.: The behavioural transition from straight to curve walking: kinetics of
leg movement parameters and the initiation of turning. J. Exp. Biol. 208, 2237–2252 (2005)
9. Meyer, H.G., et al.: Resource-efficient bio-inspired visual processing on the hexapod walking
robot HECTOR. PLoS ONE 15, e0230620 (2020)
10. Szczecinski, N.S., Goldsmith, C.A., Young, F.R., Quinn, R.D.: Tuning a robot servomotor to
exhibit muscle-like dynamics. In: Conference on Biomimetic and Biohybrid Systems. (2019)
11. Cofer, D.W., Cymbalyuk, G., Reid, J., Zhu, Y., Heitler, W.J., Edwards, D.H.: AnimatLab: a 3D
graphics environment for neuromechanical simulations. J. Neurosci. Methods. 187, 280–288
(2010)
12. Szczecinski, N.S., et al.: Introducing MantisBot: hexapod robot controlled by a high-fidelity,
real-time neural simulation. In: IEEE International Conference on Intelligent Robots and
Systems. pp. 3875–3881. Hamburg, DE (2015)
13. Joly, J.-S., Recher, G., Brombin, A., Ngo, K., Hartenstein, V.: A conserved developmental
mechanism builds complex visual systems in insects and vertebrates. Curr. Biol. 26, 1–9
(2016)
14. Mihalas, S., Niebur, E.: A generalized linear integrate-and-fire neural model produces diverse
spiking behaviors. Neural Comput. 21, 704–718 (2009)
15. Szczecinski, N.S., Hunt, A.J., Quinn, R.D.: A functional subnetwork approach to designing
synthetic nervous systems that control legged robot locomotion. Front. Neurorobot. 11, 37
(2017)