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

基本信息

批准号：
10680473
负责人：
John L Sherwood
金额：
$ 78.88万
依托单位：
LEAFLABS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-21 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10680473
关键词：
Acceleration Address Adoption Alzheimer&apos s Disease Animals Architecture BRAIN initiative Biotechnology Bite Brain Brain Diseases Budgets Cells Chronic Cognition Collaborations Complex Computer Architectures Computer software Data Data Analyses Data Storage and Retrieval Development Electrophysiology (science)Elements Engineering Epilepsy Funding Generations Goals Head Image Individual Learning Letters Mammals Marketing Memory Mental Depression Modeling Modification National Institute of Mental Health Neurons Neurosciences Noise Parkinson Disease Performance Phase Physiological Pilot Projects Population Process Production Research Personnel Resolution Rodent Sampling Services Signal Transduction Silicon Site Small Business Innovation Research Grant Sorting Speed Stream System Techniques Technology Testing Time Tissues Visualization Weight Willow Work analog cluster computing commercialization computerized data processing cost data acquisition data integration design digital electron beam lithography expectation experimental study fabrication improved in vivo live stream manufacture manufacturing process millisecond neural neural circuit neural stimulation novel research facility response sensor solid state terabyte theories tool

项目摘要

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).

项目概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的神经计算理论。此前，我们引入了直接到磁盘的数据采集架构（Kinney et al., 2015），旨在与密堆积硅探针（Scholvin et al., 2016）可在头部固定中实现 1000 通道神经记录动物（初步数据）。通过试点研究，我们证明了 TB 级数据的成功记录神经尖峰活动（初步数据），但也发现了该架构的一些缺点。二设计元素尤其受到限制。首先，我们的探头对于自由移动的实验来说太大了。其次，我们的采集硬件不具备快速分析所有1000通道数据的能力。作为一个结果，需要几天到几周的时间才能了解 TB 大小的录音的神经活动内容。为了要使超高通道数神经记录成为常规，采集架构必须允许和促进大量数据的快速在线和离线分析。具有本地数据的计算机体系结构存储和分析受到青睐，因为 1000 通道记录（例如，以 16 位采样的 1000 个通道） 30 kHz）以超过典型（千兆位以太网）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，我们提出了一种用于自由移动电生理学的 1024 通道硅探针与经过优化的数据采集系统相结合进行实验，以方便数据分析。小说硅探针将记录和刺激 1024 个密闭位置，足够紧凑，适合自由移动的啮齿动物实验，并将探头成本降低 5 倍（每通道 < 1 美元）。此外，重新设计的采集硬件不仅可以捕获 1024 个通道的神经数据并通过高速总线，但现在还将数据复制到 GPU 进行峰值排序和 RAM 进行可视化线上和线下。为了测试该系统，我们将对啮齿动物进行 1024 通道自由移动神经记录，与（至少）3 个具有专业知识的实验室合作（参见支持信）。