Abstract
We seek to increase the sophistication of our insect-like hexapod robot MantisBot’s visual system. We assembled and tested a benchtop robotic testbed with which to test our dynamical neural model of the insect visual system. Here we specifically model wide-field vision and the optomotor response. The system is composed of a Raspberry Pi with a camera outfitted with a 360° lens. The camera sits on a motorized turntable, which represents the “robot”. Above the turntable sits another motorized system that rotates a drum with printed patterns around the camera, which represents the visual “background”. The camera downsamples the visual scene and sends it to a synthetic nervous system (SNS) model of the insect optic lobe. The optic lobe is columnar. Each column detects changes in receptor intensity (retina), inhibits adjacent columns to increase dynamic range (lamina), compares time-delayed activities of adjacent columns to detect motion (medulla), then pools the motion of each column in a directionally-specific connectivity to compute the direction and speed of the wide-field scene (lobula plate). Our robotic model successfully encodes lateral wide-field visual speed into the activity of a pair of opposing Lobula Plate Tangential Cells (LPTCs). Furthermore, the optomotor response can be recreated by using the LPTCs to stimulate the neck motor neurons (MNs), producing a real-time, closed-loop dynamical model of the optomotor response.
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Sedlackova, A., Szczecinski, N.S., Quinn, R.D. (2020). A Synthetic Nervous System Model of the Insect Optomotor Response. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_30
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DOI: https://doi.org/10.1007/978-3-030-64313-3_30
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