DECODING THE ACTIVITY OF GRASPING NEURONS RECORDED FROM THE VENTRAL PREMOTOR AREA F5 OF THE MACAQUE MONKEY
DECODING THE ACTIVITY OF GRASPING NEURONS RECORDED FROM THE VENTRAL PREMOTOR AREA F5 OF THE MACAQUE MONKEY
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DOI：
10.1016/j.neuroscience.2011.04.062
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发表时间：
2011-08-11
期刊：
影响因子：
3.3
通讯作者：
Raos, V.
中科院分区：
文献类型：
--
作者：
Carpaneto, J.;Umilta, M. A.;Raos, V.
当猴子用特定的抓握方式抓握物体时，猴子腹侧运动前区F5中的许多神经元选择性地放电。其中，运动神经元仅在抓握执行期间活跃，而视觉神经元也对物体呈现做出反应。在这里，我们评估了在视觉引导抓取任务的执行过程中，从两只猕猴记录的90个任务相关的F5神经元的活动是否可以用作模式识别算法的输入，旨在解码不同的抓地力。利用解码的功能是平均放电率和平均interspike间隔计算在不同的时间跨度的运动期（所有神经元）或对象呈现期（视觉神经元）。一个支持向量机（SVM）算法被应用到神经活动记录，而猴子抓住两组对象。最初的一组包含三个用不同手形抓握的物体，加上另外三个用相同手形抓握的物体，而特殊组的六个物体用六种不同的手形抓握。该算法预测的准确性大于95%的所有不同的抓地力用于抓住的对象。使用运动周期的前25%获得的分类率为90%，而使用整个周期几乎是完美的。准确的性能至少需要16个神经元，随着包括更多的神经元，准确性逐渐增加。发现混淆矩阵揭示的分类错误反映了用于抓住物体的手柄的相似性。使用视觉神经元对物体呈现的反应产生了类似于从实际抓握执行中获得的抓握分类准确性。我们认为，F5抓握相关的活动可能被神经假体用于调整手的形状，以抓住特定的对象，甚至在运动开始之前。(C)2011年IBRO。由爱思唯尔有限公司出版。保留所有权利。
Many neurons in the monkey ventral premotor area F5 discharge selectively when the monkey grasps an object with a specific grip. Of these, the motor neurons are active only during grasping execution, whereas the visuomotor neurons also respond to object presentation. Here we assessed whether the activity of 90 task-related F5 neurons recorded from two macaque monkeys during the performance of a visually-guided grasping task can be used as input to pattern recognition algorithms aiming to decode different grips. The features exploited for the decoding were the mean firing rate and the mean interspike interval calculated over different time spans of the movement period (all neurons) or of the object presentation period (visuomotor neurons). A support vector machine (SVM) algorithm was applied to the neural activity recorded while the monkey grasped two sets of objects. The original set contained three objects that were grasped with different hand shapes, plus three others that were grasped with the same grip, whereas the six objects of the special set were grasped with six distinctive hand configurations. The algorithm predicted with accuracy greater than 95% all the distinct grips used to grasp the objects. The classification rate obtained using the first 25% of the movement period was 90%, whereas it was nearly perfect using the entire period. At least 16 neurons were needed for accurate performance, with a progressive increase in accuracy as more neurons were included. Classification errors revealed by confusion matrices were found to reflect similarities of hand grips used to grasp the objects. The use of visuomotor neurons' responses to object presentation yielded grip classification accuracy similar to that obtained from actual grasping execution. We suggest that F5 grasping-related activity might be used by neural prostheses to tailor hand shape to the specific object to be grasped even before movement onset. (C) 2011 IBRO. Published by Elsevier Ltd. All rights reserved.