Methods for estimating neural firing rates, and their application to brain-machine interfaces.
Methods for estimating neural firing rates, and their application to brain-machine interfaces.
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DOI：
10.1016/j.neunet.2009.02.004
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Shenoy KV
中科院分区：
文献类型：
--
作者：
Cunningham JP;Gilja V;Ryu SI;Shenoy KV
神经尖峰序列由于其噪声、尖峰性质而提出了分析挑战。许多神经科学和神经假体的重要性的研究依赖于一个平滑的，去噪的估计尖峰列车的基本发射率。近年来，人们开发了许多估计神经放电率的方法，但迄今为止还没有对它们进行系统的比较。在这项研究中，我们回顾了经典和当前的射击率估计技术。我们比较了这些方法的优点和缺点。然后，在努力了解他们的相关性，神经假体领域，我们也适用于这些估计实验收集的神经数据从假肢手臂达到范例。使用这些估计的发射率，我们采用标准的假肢解码算法来比较不同的发射率估计的性能，也许令人惊讶的是，我们发现最小的差异。这项研究作为一个可用的尖峰序列平滑和第一次定量比较他们的性能脑机接口的审查。
Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally-gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.
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