Multitaper Spectral Analysis of Neuronal Spiking Activity Driven by Latent Stationary Processes
Multitaper Spectral Analysis of Neuronal Spiking Activity Driven by Latent Stationary Processes
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DOI：
10.1016/j.sigpro.2019.107429
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发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
P. Das;B. Babadi
中科院分区：
文献类型：
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作者：
P. Das;B. Babadi
研究神经协变量的频谱特性是系统神经科学中的一个重要问题，因为它可以研究大脑节律在认知功能中的作用。虽然连续时间序列的谱估计是一个公认的领域，但由于所涉及的固有非线性，从尖峰数据计算这些神经协变量的谱表示提出了各种挑战。在本文中，我们解决这个问题，提出了一个变种的multitaper方法，专门为点过程数据。为此，我们构造辅助尖峰统计，从该统计的潜在过程的特征谱可以直接推断使用最大似然估计，从而可以有效地计算multitaper估计。我们提出的技术与现有的方法，使用模拟和实验记录的数据比较显示显着的收益的偏差方差权衡。
Investigating the spectral properties of the neural covariates that underlie spiking activity is an important problem in systems neuroscience, as it allows to study the role of brain rhythms in cognitive functions. While the spectral estimation of continuous time-series is a well-established domain, computing the spectral representation of these neural covariates from spiking data sets forth various challenges due to the intrinsic non-linearities involved. In this paper, we address this problem by proposing a variant of the multitaper method specifically tailored for point process data. To this end, we construct auxiliary spiking statistics from which the eigen-spectra of the underlying latent process can be directly inferred using maximum likelihood estimation, and thereby the multitaper estimate can be efficiently computed. Comparison of our proposed technique to existing methods using simulated and experimentally recorded data reveals significant gains in terms of the bias-variance trade-off.