Smith Michael A, Crawford J Douglas
York Centre for Vision Research, Canadian Institute of Health Research Group for Action and Perception, Department of Psychology, York University, Computer Science Building, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada.
J Neurophysiol. 2005 Mar;93(3):1742-61. doi: 10.1152/jn.00306.2004. Epub 2004 Nov 10.
Human saccades require a nonlinear, eye orientation-dependent reference frame transformation to transform visual codes to the motor commands for eye muscles. Primate neurophysiology suggests that this transformation is performed between the superior colliculus and brain stem burst neurons, but provides little clues as to how this is done. To understand how the brain might accomplish this, we trained a 3-layer neural net to generate accurate commands for kinematically correct 3-D saccades. The inputs to the network were a 2-D, eye-centered, topographic map of Gaussian visual receptive fields and an efference copy of eye position in 6-dimensional, push-pull "neural integrator" coordinates. The output was an eye orientation displacement command in similar coordinates appropriate to drive brain stem burst neurons. The network learned to generate accurate, kinematically correct saccades, including the eye orientation-dependent tilts in saccade motor error commands required to match saccade trajectories to their visual input. Our analysis showed that the hidden units developed complex, eye-centered visual receptive fields, widely distributed fixed-vector motor commands, and "gain field"-like eye position sensitivities. The latter evoked subtle adjustments in the relative motor contributions of each hidden unit, thereby rotating the population motor vector into the correct correspondence with the visual target input for each eye orientation: a distributed population mechanism for the visuomotor reference frame transformation. These findings were robust; there was little variation across networks with between 9 and 49 hidden units. Because essentially the same observations have been reported in the visuomotor transformations of the real oculomotor system, as well as other visuomotor systems (although interpreted elsewhere in terms of other models) we suggest that the mechanism for visuomotor reference frame transformations identified here is the same solution used in the real brain.
人类的扫视需要一种非线性的、依赖于眼睛方位的参考系变换，以便将视觉编码转换为用于眼肌的运动指令。灵长类动物神经生理学表明，这种变换是在上丘和脑干爆发神经元之间进行的，但对于其实现方式几乎没有提供线索。为了理解大脑可能如何完成这一过程，我们训练了一个三层神经网络，以生成用于运动学上正确的三维扫视的精确指令。网络的输入是一个二维的、以眼睛为中心的高斯视觉感受野地形图，以及在六维推挽式“神经积分器”坐标中的眼睛位置的传出副本。输出是一个在类似坐标中的眼睛方位位移指令，适合于驱动脑干爆发神经元。该网络学会了生成精确的、运动学上正确的扫视，包括为使扫视轨迹与其视觉输入相匹配而在扫视运动误差指令中所需的依赖于眼睛方位的倾斜。我们的分析表明，隐藏单元形成了复杂的、以眼睛为中心的视觉感受野、广泛分布的固定向量运动指令以及类似“增益场”的眼睛位置敏感性。后者引起每个隐藏单元相对运动贡献的细微调整，从而将群体运动向量旋转到与每个眼睛方位的视觉目标输入的正确对应关系：一种用于视觉运动参考系变换的分布式群体机制。这些发现是稳健的；在具有9到49个隐藏单元的网络中几乎没有变化。因为在真实的动眼神经系统以及其他视觉运动系统的视觉运动变换中也报道了基本相同的观察结果（尽管在其他地方根据其他模型进行了解释），所以我们认为这里确定的视觉运动参考系变换机制与真实大脑中使用相同的解决方案。
