Deng Xinyi, Liu Daniel F, Kay Kenneth, Frank Loren M, Eden Uri T
Department of Mathematics and Statistics, Boston University, Boston, MA 02215, U.S.A.
University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering, UCSF, San Francisco, CA 94158, U.S.A.
Neural Comput. 2015 Jul;27(7):1438-60. doi: 10.1162/NECO_a_00744. Epub 2015 May 14.
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. Because the unsupervised spike-sorting problem remains unsolved, we took an alternative approach that takes advantage of recent insights into 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's rule to estimate spatial location from hippocampal neural activity. We validate our approach with a simulation study and 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 equivalent to 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.
点过程滤波器已成功应用于解码神经信号和追踪神经动力学。传统上，这些方法假定多单元尖峰活动已经被正确地进行了尖峰分类。因此，这些方法不适用于无法高精度进行分类的情况，例如脑机接口的实时解码。由于无监督尖峰分类问题仍未解决，我们采用了一种替代方法，该方法利用了最近对无聚类解码的见解。在此，我们提出一种新的点过程解码算法，该算法不需要将多单元信号分类为单个单元。我们使用标记点过程理论来构建一个函数，该函数表征感兴趣的协变量（在这种情况下，是大鼠在轨道上的位置）与尖峰波形特征之间的关系。在我们的示例中，我们使用四通道电极记录，标记代表四个电极上每个电极的尖峰波形最大振幅的四维向量。一般来说，标记可以代表尖峰波形的任何特征。然后，我们使用贝叶斯法则从海马神经活动中估计空间位置。我们通过模拟研究和在大鼠在直线环境中移动时海马体记录的实验数据来验证我们的方法。我们的解码算法从未分类的多单元尖峰活动中准确重建大鼠的位置。然后，我们将我们的解码算法的质量与传统的尖峰分类和解码算法进行比较。我们的分析表明，所提出的解码算法的性能与基于分类的单个单元活动的算法相当或更好。这些结果为准确实时解码尖峰模式提供了一条途径，可用于对海马体或大脑其他部位的群体活动进行特定内容的操作。
