The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined MS optogenetic stimulation with local field potential (LFP) recordings from the hippocampus and leveraged machine learning techniques to explore how activating or inhibiting multiple MS neuronal subpopulations using different optical stimulation parameters affects hippocampal LFP biomarkers. First, of the seven different optogenetic viral vectors used for modulating different neuronal subpopulations, only two induced a substantial change in hippocampal LFP. Second, we found hippocampal low-gamma band to be most effectively modulated by the stimulation. Third, the hippocampal biomarkers were sensitive to the optogenetic virus type and the stimulation frequency, establishing those parameters as the critical ones for the regulation of hippocampal biomarker activity. Last, we built a Gaussian process regression model to describe the relationship between stimulation parameters and activity of the biomarker as well as to identify the optimal parameters for biomarker modulation. This new machine learning approach can further our understanding of the effects of neural stimulation and guide the selection of optimal parameters for neural control.

中文翻译：

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一种机器学习方法，通过内侧隔膜的光遗传学刺激来表征海马节律的调节

中隔 (MS) 是调节海马活动的潜在目标。然而，鉴于涉及的多种细胞类型，MS 刺激诱导的海马神经活动变化尚未完全表征。我们将 MS 光遗传学刺激与来自海马的局部场电位 (LFP) 记录相结合，并利用机器学习技术来探索使用不同的光刺激参数激活或抑制多个 MS 神经元亚群如何影响海马 LFP 生物标志物。首先，在用于调节不同神经元亚群的七种不同的光遗传学病毒载体中，只有两种诱导了海马 LFP 的显着变化。其次，我们发现海马低伽马波段最有效地受到刺激的调节。第三，海马生物标志物对光遗传学病毒类型和刺激频率敏感，将这些参数确定为调节海马生物标志物活性的关键参数。最后，我们建立了一个高斯过程回归模型来描述刺激参数与生物标志物活性之间的关系，并确定生物标志物调节的最佳参数。这种新的机器学习方法可以进一步加深我们对神经刺激效果的理解，并指导神经控制的最佳参数的选择。我们建立了一个高斯过程回归模型来描述刺激参数与生物标志物活性之间的关系，并确定生物标志物调节的最佳参数。这种新的机器学习方法可以进一步加深我们对神经刺激效果的理解，并指导选择神经控制的最佳参数。我们建立了一个高斯过程回归模型来描述刺激参数与生物标志物活性之间的关系，并确定生物标志物调节的最佳参数。这种新的机器学习方法可以进一步加深我们对神经刺激效果的理解，并指导选择神经控制的最佳参数。