Toward Optimal Target Placement for Neural Prosthetic Devices
Toward Optimal Target Placement for Neural Prosthetic Devices
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DOI：
10.1152/jn.90833.2008
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发表时间：
2008-12-01
影响因子：
2.5
通讯作者：
Shenoy, Krishna V.
中科院分区：
文献类型：
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作者：
Cunningham, John P.;Yu, Byron M.;Shenoy, Krishna V.
Cunningham JP, Yu BM, Gilja V, Ryu SI, Shenoy KV。神经修复装置的最佳目标放置。中国生物医学工程学报（英文版），2009,31(4):557 - 557。首次发表于2008年10月1日；doi: 10.1152 / jn.90833.2008。神经假体系统被设计用于估计连续到达轨迹（运动假体）和预测离散到达目标（通信假体）。在后一种情况下，到达目标通常在到达开始前的指示延迟期间从神经尖峰活动中解码。这种系统将目标放置在径向对称的几何形状中，与可用神经元的调谐特性无关。在这里，我们试图通过基于手头的神经群选择目标位置来实现目标放置过程的自动化，并提高通信假体的解码精度。包含预定目标信息的运动假体也可以从这种考虑中受益。我们提出了一种最优目标放置算法，该算法近似最大化解码精度，相对于目标位置。在两只猴子的模拟神经脉冲数据拟合中，最优目标放置算法对2个和16个目标分别产生了8%和9%的统计显著改善。对于4个和8个目标，收益更适度，因为算法发现的目标布局与规范布局非常相似。我们用这种模式训练了一只猴子，并用实验神经数据测试了算法，以证实在模拟中发现的一些结果。总之，该算法不仅可以创建优于规范布局的新目标布局，还可以确认或帮助在多个规范布局中进行选择。本文提出的最佳目标定位算法是同类算法中的第一个，它既可以提高解码精度，又可以帮助神经假体实现目标定位的自动化。
Cunningham JP, Yu BM, Gilja V, Ryu SI, Shenoy KV. Toward optimal target placement for neural prosthetic devices. J Neurophysiol 100: 3445-3457, 2008. First published October 1, 2008; doi: 10.1152/jn.90833.2008. Neural prosthetic systems have been designed to estimate continuous reach trajectories ( motor prostheses) and to predict discrete reach targets ( communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses.