Experimental and computational analysis of monkey smooth pursuit eye movements
Experimental and computational analysis of monkey smooth pursuit eye movements
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DOI：
10.1152/jn.2001.86.2.741
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发表时间：
2001-08-01
影响因子：
2.5
通讯作者：
Lisberger, SG
中科院分区：
文献类型：
--
作者：
Churchland, MM;Lisberger, SG
平滑追踪眼球运动是由视觉反馈引导的，尽管视觉输入和运动输出之间存在时间延迟，但它的准确性令人惊讶。先前的模型通过使用复杂的视觉信号或通过添加运动反馈源来再现追踪的准确性。我们的目标是通过获得区分这两种建模方法（由“图像运动模型”和“转速计反馈”模型表示）的数据来约束驱动追击的信号类型。我们的第一组实验探讨了视觉特性的追求与简短的方脉冲和正弦波扰动的目标速度。对脉冲扰动的反应几乎与脉冲幅度成线性关系，而对正弦波扰动的反应则随着刺激幅度的增加而表现出较强的饱和性。对正弦波扰动的响应强烈依赖于扰动时的基线图像速度。响应小得多，如果基线图像速度是自然的大，或人为增加叠加正弦波脉冲扰动。图像运动模型，而不是转速反馈模型，可以重现这些功能的追求。我们使用的图像运动模型的修订版，像原来的，敏感的图像速度和图像加速度。由于饱和非线性，图像加速度的灵敏度下降，增加图像速度。包含这种非线性的动机是我们的实验结果，是至关重要的占扰动的响应，并提供了一个解释的意外稳定的追求在谐振频率附近的扰动的存在。作为一个新兴的属性，修改后的图像运动模型能够再现人工反馈延迟期间记录的振荡的频率和阻尼。我们的第二组实验复制了先前记录的对多周期正弦波扰动的追踪响应，在一定频率范围内呈现。图像运动模型能够在所有频率上再现对正弦波扰动的响应，而转速计反馈模型在高频下失败。这些故障是由于转速表模型中没有图像加速度信号造成的。我们的结论是，图像加速相关的视觉信号是重要的驾驶追求眼球运动，这些信号的非线性提供稳定性。因此，平滑追踪说明，一个合理的神经策略，以打击自然延迟的感觉反馈是利用信息的衍生物的感觉输入。
Smooth pursuit eye movements are guided by visual feedback and are surprisingly accurate despite the time delay between visual input and motor output. Previous models have reproduced the accuracy of pursuit either by using elaborate visual signals or by adding sources of motor feedback. Our goal was to constrain what types of signals drive pursuit by obtaining data that would discriminate between these two modeling approaches, represented by the "image motion model" and the "tachometer feedback" model. Our first set of experiments probed the visual properties of pursuit with brief square-pulse and sine-wave perturbations of target velocity. Responses to pulse perturbations increased almost linearly with pulse amplitude, while responses to sine wave perturbations showed strong saturation with increasing stimulus amplitude. The response to sine wave perturbations was strongly dependent on the baseline image velocity at the time of the perturbation. Responses were much smaller if baseline image velocity was naturally large, or was artificially increased by superimposing sine waves on pulse perturbations. The image motion model, but not the tachometer feedback model, could reproduce these features of pursuit. We used a revision of the image motion model that was, like the original, sensitive to both image velocity and image acceleration. Due to a saturating nonlinearity, the sensitivity to image acceleration declined with increasing image velocity. Inclusion of this nonlinearity was motivated by our experimental results, was critical in accounting for the responses to perturbations, and provided an explanation for the unexpected stability of pursuit in the presence of perturbations near the resonant frequency. As an emergent property, the revised image motion model was able to reproduce the frequency and damping of oscillations recorded during artificial feedback delays. Our second set of experiments replicated prior recordings of pursuit responses to multiple-cycle sine wave perturbations, presented over a range of frequencies. The image motion model was able to reproduce the responses to sine wave perturbations across all frequencies, while the tachometer feedback model failed at high frequencies. These failures resulted from the absence of image acceleration signals in the tachometer model. We conclude that visual signals related to image acceleration are important in driving pursuit eye movements and that the nonlinearity of these signals provides stability. Smooth pursuit thus illustrates that a plausible neural strategy for combating natural delays in sensory feedback is to employ information about the derivative of the sensory input.