Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
Qinbing Fu, Hongxin Wang, Cheng Hu, Shigang Yue

TL;DR
This review discusses computational models inspired by insect visual systems for motion perception, highlighting biological mechanisms, neural network models, and their applications in robotics and vehicles, emphasizing the integration and hardware realization.
Contribution
It provides a comprehensive overview of biologically inspired motion perception models from insects and their applications, summarizing methodologies and discussing system integration and hardware implementation.
Findings
Neural models include LGMDs, DSNs, and STMDs.
Models effectively simulate biological motion detection.
Applications extend to robotics and autonomous vehicles.
Abstract
Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement…
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Taxonomy
TopicsNeurobiology and Insect Physiology Research · Insect and Arachnid Ecology and Behavior · Plant and animal studies
