Neural ensemble activity from multiple brain regions predicts kinematic and dynamic variables in a multiple force field reaching task
Neural ensemble activity from multiple brain regions predicts kinematic and dynamic variables in a multiple force field reaching task
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DOI：
10.1109/tnsre.2006.875553
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发表时间：
2006-06-01
影响因子：
4.9
通讯作者：
Chapin, John K.
中科院分区：
文献类型：
--
作者：
Francis, Joseph T.;Chapin, John K.
在日常生活中，我们接触、抓握和操纵各种不同的物体，这些物体都有自己的动态特性。这种程度的适应性对于大脑控制的假肢在现实世界中工作至关重要。在这项研究中，训练老鼠在握住扭矩操纵器的同时抵抗两种不同的负载，以进行伸手动作。从横跨运动皮层的 32 个微电极阵列获得的神经记录用于预测几个与运动相关的变量。在本文中，我们证明了一个简单的线性回归模型可以将神经活动转化为机器人操纵器的终点位置，即使控制它的动物在不同的负载下工作。第二个回归模型可以 100% 准确地预测动物正在操纵两个负载中的哪一个。最后，第三个模型预测移动操作器端点所需的工作量。该预测明显优于位置预测。在每种情况下，回归模型都使用一组权重。因此，神经元集成能够提供必要的信息。补偿至少两种不同的负载条件。
In everyday life,we reach, grasp, and manipulate a variety of different objects all with their own dynamic properties. This degree of adaptability is essential for a brain-controlled prosthetic arm to work in the real world. In this study, rats were trained to make reaching movements while holding a torque manipulandum working against two distinct loads. Neural recordings obtained from arrays of 32 microelectrodes spanning the motor cortex were used to predict several movement related variables. In this paper, we demonstrate that a simple linear regression model can translate neural activity into endpoint position of a robotic manipulandurn even while the animal controlling it works against different loads. A second regression model can predict, with 100% accuracy, which of the two loads is being manipulated by the animal. Finally, a third model predicts the work needed to move the manipulandum endpoint. This prediction is significantly better than that for position. In each case, the regression model uses a single set of weights. Thus, the neural ensemble is capable of providing the information necessary. to compensate for at least two distinct load conditions.