An Improved LPTC Neural Model for Background Motion Direction Estimation

Wang, Hongxin and Peng, Jigen and Yue, Shigang (2018) An Improved LPTC Neural Model for Background Motion Direction Estimation. In: 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 18-21 Sept. 2017, Lisbon, Portugal.

Full content URL: https://ieeexplore.ieee.org/document/8329786

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An Improved LPTC Neural Model for Background Motion Direction Estimation
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Item Type:Conference or Workshop contribution (Poster)
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Abstract

A class of specialized neurons, called lobula plate tangential cells (LPTCs) has been shown to respond strongly to wide-field motion. The classic model, elementary motion detector (EMD) and its improved model, two-quadrant detector (TQD) have been proposed to simulate LPTCs. Although EMD and TQD can percept background motion, their outputs are so cluttered that it is difficult to discriminate actual motion direction of the background. In this paper, we propose a max operation mechanism to model a newly-found transmedullary neuron Tm9 whose physiological properties do not map onto EMD and TQD. This proposed max operation mechanism is able to improve the detection performance of TQD in cluttered background by filtering out irrelevant motion signals. We will demonstrate the functionality of this proposed mechanism in wide-field motion perception.

Keywords:Background Motion Direction Estimation, LPTC Model, Neural Modeling
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:33421
Deposited On:19 Oct 2018 20:29

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