Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging
Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging
复制标题
用于神经记录的紧凑型独立平台，具有实时尖峰排序和数据记录功能
DOI：
10.1101/186627
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Luan S
中科院分区：
文献类型：
--
作者：
Luan S
纵向观察清醒和移动的动物中大量皮层神经元的单个神经元活动通常是研究神经网络行为和构建有效脑机接口（BMI）的前景的重要一步。这些记录产生了大量的数据用于传输和存储，并且通常需要离线处理来梳理单个神经元的行为。我们的目标是创建一个紧凑的系统，能够：（1）将数据带宽减少约2到3个数量级（大大提高电池寿命，并在未来版本中实现低功耗无线传输）;（2）产生实时、低延迟、尖峰排序的数据;（3）长期不受约束的操作。在短训练阶段，连接计算机并执行经典尖峰排序以生成模板。在第二阶段中，系统被解除束缚并执行模板匹配以创建记录到微型SD卡的事件驱动尖峰输出。为了验证该系统是能够记录的高带宽原始神经信号数据以及尖峰排序data.Main resultsThe系统可以成功地记录32个通道的原始神经信号数据和/或尖峰排序事件远远超过24小时的时间，是强大的电源跌落在电池更换以及SD卡更换。一个24小时的初始记录在非人类灵长类动物M1表现出一致的尖峰形状与预期的神经活动的变化在清醒的行为和睡眠cycles.SignificanceThe提出的平台允许神经活动进行不引人注目的监测和处理的实时自由行为untethered动物揭示的见解，是无法通过预定的记录会话。该系统实现了迄今为止最低的每通道功率，并提供了适用于BMI、闭环神经调制、无线传输和长期数据记录的鲁棒、低延迟、低带宽和可验证输出。
ObjectiveLongitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective brain–machine interfaces (BMIs). These recordings generate enormous amounts of data for transmission and storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of:(1) reducing the data bandwidth by circa 2 to 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission in future versions);(2) producing real-time, low-latency, spike sorted data; and (3) long term untethered operation.ApproachWe have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data.Main resultsThe system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 h at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24 h initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles.SignificanceThe presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals—revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.