Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
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DOI：
10.1016/j.knosys.2015.09.013
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Danaipat Sodkomkham;D. Ciliberti;M. Wilson;Ken-ichi Fukui;K. Moriyama;M. Numao;F. Kloosterman
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文献类型：
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作者：
Danaipat Sodkomkham;D. Ciliberti;M. Wilson;Ken-ichi Fukui;K. Moriyama;M. Numao;F. Kloosterman
为了更好地了解神经集成是如何交流和处理信息的，神经解码算法被用来提取在其放电活动中编码的信息。贝叶斯译码是从大鼠海马神经元整体放电活动中提取信息的最常用的神经群体译码方法之一。最近已经展示了如何在没有将尖峰波形分类成单个单元组的中间步骤的情况下实现贝叶斯解码。在此，我们对该方法进行了扩展，使其适用于需要实时解码的在线编解码场景，如脑机接口。我们提出了一种贝叶斯译码的在线算法，减少了译码神经种群所需的时间，从而产生了一个具有实时能力的译码框架。更具体地说，我们通过开发核密度压缩算法来提高概率密度估计步骤的速度，这是无尖峰排序译码过程中最基本和最昂贵的计算。与现有的在线核压缩技术不同，该方法不是以核压缩引起的最小估计误差为优化目标，而是根据合并分量与其最相似邻域之间的距离来压缩核。因此，在没有代价高昂的优化的情况下，所提出的方法具有非常低的压缩延迟，并且估计误差很小且可控。此外，提出的用于高斯核合并的带宽匹配方法具有有趣的数学特性，由此可以有效地执行概率密度函数估计中的优化，从而导致更快的解码速度。我们成功地将提出的核压缩算法应用到贝叶斯解码框架中，从海马未排序的棘波中重建出自由移动的大鼠的位置，在解码速度和可接受的解码错误方面都有了显著的提高。
To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decoding can be implemented without the intermediate step of sorting spike waveforms into groups of single units. Here we extend the approach in order to make it suitable for online encoding/decoding scenarios that require real-time decoding such as brain-machine interfaces. We propose an online algorithm for the Bayesian decoding that reduces the time required for decoding neural populations, resulting in a real-time capable decoding framework. More specifically, we improve the speed of the probability density estimation step, which is the most essential and the most expensive computation of the spike-sorting-less decoding process, by developing a kernel density compression algorithm. In contrary to existing online kernel compression techniques, rather than optimizing for the minimum estimation error caused by kernels compression, the proposed method compresses kernels on the basis of the distance between the merging component and its most similar neighbor. Thus, without costly optimization, the proposed method has very low compression latency with a small and manageable estimation error. In addition, the proposed bandwidth matching method for Gaussian kernels merging has an interesting mathematical property whereby optimization in the estimation of the probability density function can be performed efficiently, resulting in a faster decoding speed. We successfully applied the proposed kernel compression algorithm to the Bayesian decoding framework to reconstruct positions of a freely moving rat from hippocampal unsorted spikes, with significant improvements in the decoding speed and acceptable decoding error.