Department of Electrical and Computer Engineering, University of California-San Diego, La Jolla, CA, United States of America.
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA, United States of America.
J Neural Eng. 2021 May 19;18(4). doi: 10.1088/1741-2552/abfd0f.
Background noise experienced during extracellular neural recording limits the number of spikes that can be reliably detected, which ultimately limits the performance of next-generation neuroscientific work. In this study, we aim to utilize stochastic resonance (SR), a technique that can help identify weak signals in noisy environments, to enhance spike detectability.Previously, an SR-based pre-emphasis algorithm was proposed, where a particle inside a 1D potential well is exerted by a force defined by the extracellular recording, and the output is obtained as the displacement of the particle. In this study, we investigate how the well shape and damping status impact the output signal-to-noise ratio (SNR). We compare the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets. Then, we assess the spike detection performance when thresholding is applied on the output of the well shape-damping status configuration giving the best SNR enhancement.The SNR depends on the well-shape and damping-status type as well as the input noise level. The underdamped solution of the shallow-wall monostable well can yield to more than four orders of magnitude greater SNR improvement compared to other configurations for low noise intensities. Using this configuration also results in better spike detection sensitivity and positive predictivity than the state-of-the-art spike detection algorithms for a public synthetic dataset. For larger noise intensities, the overdamped solution of the steep-wall monostable well provides better spike enhancement than the others.The dependence of SNR improvement on the input signal noise level can be used to design a detector with multiple outputs, each more sensitive to a certain distance from the electrode. Such a detector can potentially enhance the performance of a successive spike sorting stage.
背景噪声是在细胞外神经记录过程中产生的，它限制了可靠检测到的尖峰数量，从而限制了下一代神经科学工作的性能。在本研究中，我们旨在利用随机共振（SR）技术来增强尖峰的可检测性，该技术可以帮助识别嘈杂环境中的弱信号。
先前提出了一种基于 SR 的预强调算法，其中，一维势阱中的粒子受到由细胞外记录定义的力的作用，输出是粒子的位移。在本研究中，我们研究了势阱形状和阻尼状态如何影响输出信噪比（SNR）。我们比较了浅壁和陡壁单稳态阱以及双稳态阱的过阻尼和欠阻尼解，以评估在使用两个合成数据集时 SNR 提高情况。然后，我们评估了在应用于势阱形状-阻尼状态配置的输出的阈值处理时的尖峰检测性能，该配置可提供最佳 SNR 增强。
SNR 取决于阱形状和阻尼状态类型以及输入噪声水平。与其他配置相比，浅壁单稳态阱的欠阻尼解在低噪声强度下可产生超过四个数量级的 SNR 提高。对于公共合成数据集，使用此配置还可以提高尖峰检测灵敏度和阳性预测性，优于最新的尖峰检测算法。对于更大的噪声强度，陡壁单稳态阱的过阻尼解比其他配置提供更好的尖峰增强。
SNR 改善对输入信号噪声水平的依赖性可用于设计具有多个输出的检测器，每个输出对离电极的某个距离更敏感。这样的检测器可以潜在地增强连续尖峰分类阶段的性能。
