Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong.
Neural Comput. 2010 Mar;22(3):730-51. doi: 10.1162/neco.2009.01-09-950.
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.
我们提出了一个简单的优化准则，该准则可以自动开发由哺乳动物视觉皮层中深度的神经表示驱动的感觉运动反馈回路。我们的测试平台是一个主动立体视觉系统，其中两只眼睛之间的会聚角由一群视差选择性神经元的输出控制。通过找到最大化神经元群体总响应的策略，系统最终可以在目标在深度中移动时跟踪目标。我们使用在深度中正弦和随机移动的物体来描述由此产生的策略的跟踪性能。令人惊讶的是，该系统甚至可以根据其无法跟踪的刺激来学习如何跟踪：即使系统的闭环 3 dB 跟踪带宽为 0.3 Hz，也可以为以高达 0.75 Hz 的速度移动的输入刺激学习正确的跟踪策略。
