Maximizing neural responses leads to sensori-motor coordination of binocular vergence

We present a simple optimization criterion based on the neural representation of the depth in the mammalian visual cortex that leads to self organization of a sensori-motor feedback loop. We study an active stereo vision system where the vergence angle between the two eyes is controlled by the output of a population of disparity selective neurons. We show that finding a control policy that maximizes the total response across an entire population of disparity selective neurons results in a system that automatically learns to track a target as it moves in depth. We characterized the tracking performance of the resulting policy using objects moving both sinusoidally and randomly in depth. The closed loop 3 dB tracking bandwidth of the system was 0.3 Hz.