An Improved LPTC Neural Model for Background Motion Direction Estimation
This work addresses a specific issue in neural modeling for motion perception, representing an incremental improvement over existing methods like EMD and TQD.
The paper tackled the problem of cluttered outputs in background motion direction estimation by proposing a max operation mechanism based on the Tm9 neuron, which improved the detection performance of the two-quadrant detector (TQD) by filtering out irrelevant motion signals.
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.