Inferring entire spiking activity from local field potentials with deep learning
Inferring entire spiking activity from local field potentials with deep learning
复制标题
通过深度学习从局部场势推断整个尖峰活动
DOI：
10.1101/2020.05.02.074104
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ahmadi N
中科院分区：
文献类型：
--
作者：
Ahmadi N
细胞外记录通常通过将其分离为两个不同的信号来分析：局部场电位(LFP)和尖峰。以前的研究表明，单单位活动(SUA)或多单位活动(MUA)形式的尖峰可以仅从LFP中以相当好的准确性推断出来。SUA和MUA通常通过基于阈值的技术来提取，当记录表现出低信噪比(SNR)时，该技术可能不可靠。另一种类型的尖峰活动，称为整个尖峰活动(ESA)，可以通过无阈值、快速和自动化的技术提取，并在几个任务中获得更好的性能。然而，它与LFP的关系尚未得到调查。在这项研究中，我们旨在通过从三只执行不同任务的猴子的运动皮质区域内记录的LFP来推断ESA来解决这个问题。长期记录过程和不同受试者的结果显示，从LFP可以很好地推断ESA。平均而言，ESA的推理性能一致且显著高于SUA和MUA。此外，局部运动电位(LMP)被发现是最具预测性的特征。总体结果表明，LFP包含大量关于尖峰活动的信息，特别是ESA。这可能有助于理解LFP与峰值的关系，并有助于基于LFP的BMI的发展。
Extracellular recordings are typically analysed by separating them into two distinct signals: local field potentials (LFPs) and spikes. Previous studies have shown that spikes, in the form of single-unit activity (SUA) or multiunit activity (MUA), can be inferred solely from LFPs with moderately good accuracy. SUA and MUA are typically extracted via threshold-based technique which may not be reliable when the recordings exhibit a low signal-to-noise ratio (SNR). Another type of spiking activity, referred to as entire spiking activity (ESA), can be extracted by a threshold-less, fast, and automated technique and has led to better performance in several tasks. However, its relationship with the LFPs has not been investigated. In this study, we aim to address this issue by inferring ESA from LFPs intracortically recorded from the motor cortex area of three monkeys performing different tasks. Results from long-term recording sessions and across subjects revealed that ESA can be inferred from LFPs with good accuracy. On average, the inference performance of ESA was consistently and significantly higher than those of SUA and MUA. In addition, local motor potential (LMP) was found to be the most predictive feature. The overall results indicate that LFPs contain substantial information about spiking activity, particularly ESA. This could be useful for understanding LFP-spike relationship and for the development of LFP-based BMIs.