Abstract
This paper presents modelling of a fruit fly’s visual neural system for motion recognition employing non-spiking Hodgkin-Huxley neurons. Motion detection operates based on the Hassenstein-Reichardt correlator principle. An array of motion detectors reveals the velocity field pattern, and an additional summation layer allows calculation of the vanishing point. The synthetic nervous system is successfully designed using the functional subnetwork approach. This allows the model to be scaled up to several hundred motion detectors according to the number of ommatidia. The output provides the abstraction of motion on a couple of exit neurons, which can be used in further implementation of control for the mobile robot. The simulation of operation on artificially generated input signals for different types of motion, and a summary of neuronal activities are given.
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Bračun, D., Szczecinski, N.S., Škulj, G., Hunt, A.J., Quinn, R.D. (2018). Artificial Compound Eye and Synthetic Neural System for Motion Recognition. In: Vouloutsi , V., et al. Biomimetic and Biohybrid Systems. Living Machines 2018. Lecture Notes in Computer Science(), vol 10928. Springer, Cham. https://doi.org/10.1007/978-3-319-95972-6_7
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DOI: https://doi.org/10.1007/978-3-319-95972-6_7
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