Arai Kensuke, Kass Robert E
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.
PLoS Comput Biol. 2017 Oct 6;13(10):e1005596. doi: 10.1371/journal.pcbi.1005596. eCollection 2017 Oct.
Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.
在诸如脑电图（EEG）和大脑整体物质中的局部场电位（LFP）等连续值神经记录中，可以观察到不同频段的振荡，并且对尖峰-场相干性的分析表明，单个神经元的放电通常发生在全局振荡的特定阶段。振荡调制已针对连续值振荡信号进行了研究，并且独立于单独的尖峰序列进行研究，但是行为或刺激触发的放电率调制、尖峰稀疏性、不与刺激锁定的缓慢调制的存在以及振荡周期具有大变化性的不规则振荡，对寻找尖峰序列中存在的时间结构提出了挑战。为了研究在各种实验条件下收集的真实数据中的振荡调制，我们描述了一个灵活的点过程框架，我们称之为潜在振荡尖峰序列（LOST）模型，以将瞬时放电率分解为生物学和行为相关因素：尖峰不应性、事件锁定放电率的非平稳性以及由基线偏移和随机振荡调制引起的试验间变异性。我们还扩展了LOST模型，以适应实验过程中调制结构的变化，从而发现大鼠初级运动皮层神经元与LFP θ节律的尖峰-场相干性的试验间变异性。由于LOST包含一个潜在的随机自回归项，因此当放电率较低、调制较弱且调制振荡具有较宽的频谱峰值时，LOST能够检测到振荡。
