In vivo neural spike detection with adaptive noise estimation
In vivo neural spike detection with adaptive noise estimation
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DOI：
10.1088/1741-2552/ac8077
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发表时间：
2022-07
影响因子：
4
通讯作者：
D. Valencia;P. Mercier;A. Alimohammad
中科院分区：
文献类型：
--
作者：
D. Valencia;P. Mercier;A. Alimohammad
目的，可靠的神经元可靠地污染了噪声，这对于可靠的记录神经元的方法必须分析提出了一个可以自主适合记录通道统计的变化的准确且计算高效的尖峰检测模块。主要的结果。使用合成和实际的神经记录评估了所选的候选峰值检测技术。 - 标准180 nm CMOS过程中的通道尖峰检测模块是最多的区域和功率高效尖峰检测ASIC和在脑部的组织安全约束中运行，同时提供自适应噪声估算。
Objective. The ability to reliably detect neural spikes from a relatively large population of neurons contaminated with noise is imperative for reliable decoding of recorded neural information. Approach. This article first analyzes the accuracy and feasibility of various potential spike detection techniques for in vivo realizations. Then an accurate and computationally-efficient spike detection module that can autonomously adapt to variations in recording channels’ statistics is presented. Main results. The accuracy of the chosen candidate spike detection technique is evaluated using both synthetic and real neural recordings. The designed detector also offers the highest decoding performance over two animal behavioral datasets among alternative detection methods. Significance. The implementation results of the designed 128-channel spike detection module in a standard 180 nm CMOS process is among the most area and power-efficient spike detection ASICs and operates within the tissue-safe constraints for brain implants, while offering adaptive noise estimation.