Learning invariance from transformation sequences
Neural Computation
Technical Note: \cal Q-Learning
Machine Learning
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Bayesian approach to the stereo correspondence problem
Neural Computation
Vergence Control and Disparity Estimation with Energy Neurons: Theory and Implementation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Computing stereo disparity and motion with known binocular cell properties
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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We present a simple optimization criterion that leads to autonomous development of a sensorimotor feedback loop driven by the neural representation of the depth in the mammalian visual cortex. Our test bed is 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. By finding a policy that maximizes the total response across the neuron population, the system eventually tracks 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. Surprisingly, the system can even learn how to track based on stimuli it cannot track: even though the closed loop 3 dB tracking bandwidth of the system is 0.3 Hz, correct tracking policies are learned for input stimuli moving as fast as 0.75 Hz.