BAC: Complex Predictive Pursuit by the Eye Compared to a Cerebellar Model of Pursuit

摘要：IBN - 9723846 复杂的预测追求的眼睛相比，小脑模型的追求- R。E.凯特纳 所有动物生命中最显著的特征之一是运动的流动性和协调性，这在熟练的运动员和舞蹈家中达到了很高的水平，但也存在于日常行动中。考虑到大脑中单个神经元的缓慢性，这种能力尤其显着。大脑通过并行执行许多计算来提高其有效处理速度，但这似乎并没有消除处理视觉信息的长时间延迟。当人们试图解释眼睛如何能够跟踪沿着复杂轨迹快速移动的目标而基本上没有滞后时，这个问题尤其尖锐。如果眼睛运动仅由目标当前位置的变化来控制，则可以预期眼睛会滞后于目标100 ms的延迟，这是处理视觉输入所需的。相反，似乎系统能够通过预测控制来补偿延迟。也就是说，它根据高度延迟的信息预测眼睛应该如何移动。该项目将在猴子身上进行实验，以确定在各种条件下眼动预测的极限：（1）当目标速度是恒定的或可变的时，沿着圆形和复杂轨迹的运动，（2）沿沿着被右-与在不可预测的时间和位置处的相同目标偏差相比，在可预测的时间和位置处的目标方向的角度变化，以及（3）当目标短暂关闭时沿沿着圆形和复杂轨迹的运动。 该项目还将继续开发一种生物现实的神经网络模型，用于预测眼球控制，该模型基于已知参与眼球运动的大脑小脑区域。该模型使用比其他追踪模型更多的内部单元（440个输入苔藓纤维，6 000个内部颗粒细胞，2个浦肯野细胞输出）来产生复杂的预测追踪。该模型通过使用视觉误差信号（来自攀爬纤维输入）修改颗粒到浦肯野细胞突触，以生物学上合理的方式学习新的轨迹。行为数据、神经反应特性和解剖连接都是基于实验的。在上述实验中获得的数据将用于测试，并在必要时修改模型。此外，模型性能将与其他实验室的研究结果进行比较。将测试只能使用视觉输入执行的随机目标运动。新的模拟还将测试当学习轨迹的频率改变时模型的表现如何，以及模型是否可以同时学习多个轨迹。所有这些工作都应该提供有关预测在运动控制中的作用的重要信息，并增加我们对大脑系统如何实现预测控制的理解。

ABSTRACT: IBN - 9723846 Complex Predictive Pursuit by the Eye Compared to a Cerebellar Model of Pursuit - R. E. Kettner. One of the most remarkable characteristics of all animal life is the fluidity and coordination of movement that reaches high levels in skilled athletes and dancers, but is also present in everyday action. This ability is particularly remarkable given the slowness of individual neurons in the brain. The brain increases its effective processing speed by performing many computations in parallel, but this does not appear to remove long delays in processing visual information. The problem is particularly acute when one attempts to explain how the eye is able to track a target moving rapidly along a complex trajectory with essentially zero lag. If eye motion were controlled solely by changes in the current location of the target, one would expect the eye to lag the target by the 100 ms delay required to process visual input. Rather, it appears that the system is able to compensate for delays by predictive control. That is, it predicts how the eye should move based on highly delayed information. This project will conduct experiments in monkeys to determine the limits of eye movement prediction under a variety of conditions: (1) motion along circular and complex trajectories when target velocity is either constant or variable, (2) motion along a circular trajectory interrupted by a right-angle change in target direction at either predictable times and locations compared with identical target deviations at unpredictable times and locations, and (3) motion along circular and complex trajectories when the target is briefly turned off. The project will also continue the development of a biologically-realistic neural-network model of predictive eye control based on regions of the brain's cerebellum known to be involved in pursuit eye movements. This model uses a much larger number of internal units than other pursuit models (440 input mossy fibers, 6 000 internal granule cells, 2 Purkinje cell outputs) to generate complex predictive pursuit. The model learns new trajectories in a biologically- reasonable fashion by modifying granule-to-Purkinje cell synapses using visual error signals (from climbing fiber inputs). The behavioral data, neural response properties, and anatomical connections are all based on experiment. Data obtained in the above experiments will be used to test, and if necessary, modify the model. In addition, model performance will be compared with results from studies in other laboratories. Random target motions will be tested that can only be performed using visual input. New simulations will also test how well the model performs when the frequency of a learned trajectory is changed, and whether the model can learn more than one trajectory at the same time. All of this work should provide important information about the role of prediction in motor control, as well as increase our understanding of how brain systems accomplish predictive control.