Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter.
Clusterless Decoding of Position from Multiunit Activity Using a Marked Point Process Filter.
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使用标记的点过程过滤器对多单位活动的位置无聚类解码。
DOI：
10.1162/neco_a_00744
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发表时间：
2015-07
影响因子：
2.9
通讯作者：
Eden UT
中科院分区：
文献类型：
--
作者：
Deng X;Liu DF;Kay K;Frank LM;Eden UT
点过程滤波器已成功地应用于神经信号的解码和神经动力学的跟踪。传统上，这些方法假设多单位尖峰活动已经被正确地尖峰分类。因此，这些方法不适用于不能以高精度执行排序的情况，例如脑机接口的实时解码。由于无人监督的脉冲排序问题仍未解决，我们采取了另一种方法，该方法利用了最近对无簇解码的见解。这里，我们提出了一种新的点过程译码算法，该算法不需要将多个单元的信号分成单独的单元。我们使用标记点过程的理论来构造一个函数，该函数描述感兴趣的协变量(在这种情况下，是老鼠在轨道上的位置)与尖峰波形特征之间的关系。在我们的例子中，我们使用四极管记录，标记表示四个电极上的尖峰波形的最大幅度的四维向量。通常，标记可以表示尖峰波形的任何特征。然后，我们使用贝叶斯规则从海马神经活动中估计空间位置。我们通过一项模拟研究和一只在线性环境中移动的大鼠的海马区记录的实验数据来验证我们的方法。我们的解码算法从未排序的多单位放电活动中准确地重建了老鼠的位置。然后，我们将我们的解码算法的质量与传统的尖峰排序和解码算法的质量进行了比较。分析表明，该译码算法的性能与基于排序的单单元活动译码算法相当或更好。这些结果提供了一条准确实时解码尖峰模式的途径，可以用来对海马体或大脑其他地方的群体活动进行内容特定的操纵。
Point process filters have been applied successfully to decode neural signals and track neural dynamics. Traditionally, these methods assume that multiunit spiking activity has already been correctly spike-sorted. As a result, these methods are not appropriate for situations where sorting cannot be performed with high precision such as real-time decoding for brain-computer interfaces. As the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights about clusterless decoding. Here we present a new point process decoding algorithm that does not require multiunit signals to be sorted into individual units. We use the theory of marked point processes to construct a function that characterizes the relationship between a covariate of interest (in this case, the location of a rat on a track) and features of the spike waveforms. In our example, we use tetrode recordings, and the marks represent a four-dimensional vector of the maximum amplitudes of the spike waveform on each of the four electrodes. In general, the marks may represent any features of the spike waveform. We then use Bayes’ rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and with experimental data recorded in the hippocampus of a rat moving through a linear environment. Our decoding algorithm accurately reconstructs the rat’s position from unsorted multiunit spiking activity. We then compare the quality of our decoding algorithm to that of a traditional spike-sorting and decoding algorithm. Our analyses show that the proposed decoding algorithm performs equivalently or better than algorithms based on sorted single-unit activity. These results provide a path toward accurate real-time decoding of spiking patterns that could be used to carry out content-specific manipulations of population activity in hippocampus or elsewhere in the brain.