High channel count electrophysiology and data processing for freely-moving animals

项目摘要 同时记录和刺激分布在整个大脑中的大量神经元， 需要严格评估哺乳动物细胞水平上的神经计算理论。此前我们 引入了直接到磁盘的数据采集体系结构（Kinney等人，2015年，设计与 紧密堆积的硅探针（Scholvin等人，2016年），使1000通道神经记录在头部固定 动物（初步数据）。通过试点研究，我们证明了成功记录TB的 神经尖峰活动（初步数据），但也发现了一些不足之处的架构。两 设计元素尤其具有局限性。首先，我们的云台对于自由移动的实验来说太笨重了。 其次，我们的采集硬件没有能力快速分析所有1000个通道的数据。作为 结果，需要几天到几周的时间才能理解太字节大小的记录的神经活动内容。为 超高通道数神经记录成为常规，采集架构必须允许， 便于快速在线和离线分析大量数据。具有本地数据的计算机体系结构 存储和分析是有利的，因为1000通道记录（例如，1000通道以16位采样， 30 kHz）以超过典型（千兆以太网）网络连接的持续速率生成神经数据 计算集群或云的速度。 因此，我们提出了一个1024通道硅探针自由移动电生理 实验与数据采集系统相结合，优化了数据分析。小说 硅探针将记录和刺激1024个封闭密集部位，其紧凑程度足以让啮齿动物自由移动 实验，并减少5倍的成本（<1美元每通道）。此外，重新设计的 采集硬件不仅可以捕获1024个神经数据通道，并存储到固态驱动器上， 高速总线，但现在也将数据复制到GPU进行尖峰排序和RAM进行可视化， 线上线下为了测试这个系统，我们将在啮齿动物身上进行1024通道的自由移动神经记录， 与（至少）3个具有专业知识的实验室合作（参见支持函）。

PROJECT SUMMARY Simultaneous recording and stimulation of larger populations of neurons distributed throughout the brain is needed to rigorously evaluate theories of neural computation at the cellular level in mammals. Previously, we introduced a direct-to-disk data acquisition architecture (Kinney et al., 2015) designed to work with close-packed silicon probes (Scholvin et al., 2016) to enable 1000-channel neural recording in head-fixed animals (Preliminary Data). Through pilot studies we demonstrated the successful recording of terabytes of neural spiking activity (Preliminary Data), but also discovered some shortcomings of the architecture. Two design elements in particular were limiting. First, our headstages were too bulky for freely-moving experiments. Second, our acquisition hardware did not have the ability to quickly analyze all 1000 channels of data. As a result, it took days to weeks to understand the neural activity content of the terabyte-size recordings. For ultra-high-channel count neural recordings to become routine, the acquisition architecture must allow and facilitate rapid online and offline analysis of large amounts of data. A computer architecture with local data storage and analysis is favored, since a 1000-channel recording (e.g. 1000 channels sampled with 16 bits at 30 kHz) generates neural data at a sustained rate that exceed typical (gigabit ethernet) network connection speed to compute clusters or the cloud. Accordingly, we propose a 1024-channel silicon probe for freely-moving electrophysiology experiments in combination with a data acquisition system optimized for easy data analysis. The novel silicon probe will record and stimulate 1024 closed-packed sites, be compact enough for freely-moving rodent experiments, and reduce headstage cost by a factor of 5 (<$1 per channel). Furthermore, the redesigned acquisition hardware will not only capture 1024 channels of neural data and store to solid-state drive over a high-speed bus, but will now also copy the data to a GPU for spike sorting and RAM for visualization both online and offline. To test the system, we will perform 1024-channel freely-moving neural recordings in rodents, in collaboration with (at least) 3 labs with expertise (see Letters of Support).