Helms Tillery S I, Taylor D M, Schwartz A B
Harrington Department of Bioengineering, Arizona State University, Tempe, AZ 85287-9709, USA.
Rev Neurosci. 2003;14(1-2):107-19. doi: 10.1515/revneuro.2003.14.1-2.107.
We have recently developed a closed-loop environment in which we can test the ability of primates to control the motion of a virtual device using ensembles of simultaneously recorded neurons /29/. Here we use a maximum likelihood method to assess the information about task performance contained in the neuronal ensemble. We trained two animals to control the motion of a computer cursor in three dimensions. Initially the animals controlled cursor motion using arm movements, but eventually they learned to drive the cursor directly from cortical activity. Using a population vector (PV) based upon the relation between cortical activity and arm motion, the animals were able to control the cursor directly from the brain in a closed-loop environment, but with difficulty. We added a supervised learning method that modified the parameters of the PV according to task performance (adaptive PV), and found that animals were able to exert much finer control over the cursor motion from brain signals. Here we describe a maximum likelihood method (ML) to assess the information about target contained in neuronal ensemble activity. Using this method, we compared the information about target contained in the ensemble during arm control, during brain control early in the adaptive PV, and during brain control after the adaptive PV had settled and the animal could drive the cursor reliably and with fine gradations. During the arm-control task, the ML was able to determine the target of the movement in as few as 10% of the trials, and as many as 75% of the trials, with an average of 65%. This average dropped when the animals used a population vector to control motion of the cursor. On average we could determine the target in around 35% of the trials. This low percentage was also reflected in poor control of the cursor, so that the animal was unable to reach the target in a large percentage of trials. Supervised adjustment of the population vector parameters produced new weighting coefficients and directional tuning parameters for many neurons. This produced a much better performance of the brain-controlled cursor motion. It was also reflected in the maximum likelihood measure of cell activity, producing the correct target based only on neuronal activity in over 80% of the trials on average. The changes in maximum likelihood estimates of target location based on ensemble firing show that an animal's ability to regulate the motion of a cortically controlled device is not crucially dependent on the experimenter's ability to estimate intention from neuronal activity.
我们最近开发了一种闭环环境，在这种环境中，我们可以测试灵长类动物使用同时记录的神经元集群来控制虚拟设备运动的能力/29/。在此，我们使用最大似然方法来评估神经元集群中包含的有关任务表现的信息。我们训练了两只动物在三维空间中控制计算机光标运动。最初，动物通过手臂运动来控制光标运动，但最终它们学会了直接从皮层活动驱动光标。利用基于皮层活动与手臂运动之间关系的群体向量（PV），动物能够在闭环环境中直接从大脑控制光标，但存在困难。我们添加了一种监督学习方法，该方法根据任务表现修改PV的参数（自适应PV），并发现动物能够从脑信号对光标运动进行更精细的控制。在此，我们描述一种最大似然方法（ML）来评估神经元集群活动中包含的有关目标的信息。使用这种方法，我们比较了在手臂控制期间、自适应PV早期的脑控制期间以及自适应PV稳定且动物能够可靠且精细分级地驱动光标后的脑控制期间，集群中包含的有关目标的信息。在手臂控制任务期间，ML能够在少至10%的试验中、多至75%的试验中确定运动目标，平均为65%。当动物使用群体向量来控制光标运动时，这个平均值下降了。平均而言，我们能够在大约35%的试验中确定目标。这个低百分比也反映在对光标的控制不佳上，以至于动物在很大比例的试验中无法到达目标。对群体向量参数的监督调整为许多神经元产生了新的加权系数和方向调谐参数。这使得脑控光标运动的表现有了很大改善。它也反映在细胞活动的最大似然测量中，平均在超过80%的试验中仅基于神经元活动就能产生正确的目标。基于集群放电的目标位置最大似然估计的变化表明，动物调节皮层控制设备运动的能力并不关键地依赖于实验者从神经元活动估计意图的能力。
