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 3dB tracking bandwidth of the system was 0.3Hz.