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RT-Sort:一种基于动作电位传播的算法,用于实时尖峰检测和排序,延迟为毫秒级。

RT-Sort: An action potential propagation-based algorithm for real time spike detection and sorting with millisecond latencies.

作者信息

van der Molen Tjitse, Lim Max, Bartram Julian, Cheng Zhuowei, Robbins Ash, Parks David F, Petzold Linda R, Hierlemann Andreas, Haussler David, Hansma Paul K, Tovar Kenneth R, Kosik Kenneth S

机构信息

Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, California, United States of America.

Department of Molecular, Cellular and Developmental Biology, University of California Santa Barbara, Santa Barbara, California, United States of America.

出版信息

PLoS One. 2024 Dec 5;19(12):e0312438. doi: 10.1371/journal.pone.0312438. eCollection 2024.

Abstract

With the use of high-density multi-electrode recording devices, electrophysiological signals resulting from action potentials of individual neurons can now be reliably detected on multiple adjacent recording electrodes. Spike sorting assigns these signals to putative neural sources. However, until now, spike sorting can only be performed after completion of the recording, preventing true real time usage of spike sorting algorithms. Utilizing the unique propagation patterns of action potentials along axons detected as high-fidelity sequential activations on adjacent electrodes, together with a convolutional neural network-based spike detection algorithm, we introduce RT-Sort (Real Time Sorting), a spike sorting algorithm that enables the sorted detection of action potentials within 7.5ms±1.5ms (mean±STD) after the waveform trough while the recording remains ongoing. RT-Sort's true real-time spike sorting capabilities enable closed loop experiments with latencies comparable to synaptic delay times. We show RT-Sort's performance on both Multi-Electrode Arrays as well as Neuropixels probes to exemplify RT-Sort's functionality on different types of recording hardware and electrode configurations.

摘要

通过使用高密度多电极记录设备,现在可以在多个相邻记录电极上可靠地检测到单个神经元动作电位产生的电生理信号。尖峰分类将这些信号分配到假定的神经源。然而,到目前为止,尖峰分类只能在记录完成后进行,这阻碍了尖峰分类算法的真正实时使用。利用动作电位沿轴突的独特传播模式,在相邻电极上检测为高保真顺序激活,结合基于卷积神经网络的尖峰检测算法,我们引入了RT-Sort(实时分类),这是一种尖峰分类算法,能够在波形波谷后7.5ms±1.5ms(平均值±标准差)内对动作电位进行分类检测,同时记录仍在进行。RT-Sort真正的实时尖峰分类能力使得闭环实验的延迟与突触延迟时间相当。我们展示了RT-Sort在多电极阵列和神经像素探针上的性能,以例证RT-Sort在不同类型记录硬件和电极配置上的功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aeba/11620616/907b008780bb/pone.0312438.g001.jpg

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