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

自由移动动物的高通道数电生理学和数据处理

• 批准号: 9409295
• 负责人: JUSTIN P KINNEY
• 金额: $ 69.9万
• 依托单位: LEAFLABS, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-21 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9409295
• 关键词: Alzheimer' s Disease; Animals; Architecture; Brain; Brain Diseases; Chronic; Cognition; Collaborations; Computer Architectures; Computer software; Data; Data Analyses; Data Storage and Retrieval; Development; Devices; Electrophysiology (science); Elements; Epilepsy; Goals; Head; Imagery; Learning; Letters; Mammals; Memory; Mental Depression; Neurons; Neurosciences; Parkinson Disease; Performance; Pilot Projects; Population; Preparation; Research Personnel; Rodent; Sampling; Silicon; Site; Sorting - Cell Movement; Speed; Stream; System; Technology; Testing; Time; Willow; base; cluster computing; computerized data processing; cost; data acquisition; design; experimental study; in vivo; novel; relating to nervous system; solid state; terabyte; theories; tool

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 close-packed silicon probes (Scholvin et al., 2016) and a direct-to-disk data acquisition architecture (Kinney et al., 2015) 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 1000-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 1000 closed-packed sites, be compact enough for freely-moving rodent experiments, and reduce headstage cost by a factor of 10, down to $1 per channel. Furthermore, the re-designed acquisition hardware will not only capture 1000 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 1000-channel freely-moving neural recordings in rodents, in collaboration with (at least) 3 labs with expertise (see letters of support).

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 引入了紧密堆积的硅探针（Scholvin等人，2016）和直接到磁盘的数据采集架构 （Kinney等人，2015年），使1000通道神经记录在头部固定的动物（初步数据）。 通过初步研究，我们证明了成功记录TB的神经尖峰活动 （初步数据），但也发现了架构的一些缺点。两个设计元素， 特别是限制。首先，我们的云台对于自由移动的实验来说太笨重了。二是我们 采集硬件没有能力快速分析所有1000个通道的数据。结果， 几天到几周的时间来理解TB大小的记录的神经活动内容。用于超高通道 计数神经记录成为常规，采集架构必须允许并促进快速在线 和大量数据的离线分析。一种具有本地数据存储和分析的计算机体系结构， 这是有利的，因为1000通道记录（例如，以30 kHz的16位采样的1000通道）生成神经元。 以超过典型（千兆以太网）网络连接速度的持续速率传输数据到计算群集， 云。 因此，我们提出了一个1000通道的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1000个密集地点，足够紧凑，可以自由移动啮齿动物 实验，并减少了10倍的成本，降低到每通道1美元。而且 重新设计的采集硬件不仅可以捕获1000个神经数据通道并存储到固态 驱动器通过高速总线，但现在也将数据复制到GPU进行尖峰排序和RAM， 在线和离线的可视化。为了测试这个系统，我们将进行1000通道的自由移动神经网络测试。 啮齿动物中的记录，与（至少）3个具有专业知识的实验室合作（见支持函）。