BIGDATA: Collaborative Research: IA: Hardware and Software for Spike Detection and Sorting in Massively Parallel Electrophysiological Recording Systems for the Brain

BIGDATA：协作研究：IA：用于大脑大规模并行电生理记录系统中尖峰检测和排序的硬件和软件

• 批准号: 1546296
• 负责人: Kenneth Shepard
• 金额: $ 85万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546296&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Hardware

Understanding how the brain works is arguably one of the most significant scientific challenges of our time and the focus of the BRAIN initiative. It is widely believed that neural circuit function is emergent, the result of complex interactions between constituents with individual neurons forming synaptic connections with thousands of other neurons. Mapping of these complex circuits has been virtually impossible because of the reliance on electrophysiological recordings which sample these networks extremely sparsely. These tools for extracellular spike recordings are only able to simultaneously record from several tens to a few hundred neurons. Raw signals from these recording electrodes are first filtered to remove out-of-band signals. Putative spike events are then detected and extracted. Finally, these snippets of time-series event are sorted, typically on the basis of waveform shapes, into clusters. Even at the very modest bandwidths for these systems, computing systems struggle to save the data and process the resulting data sets. Scalability of these measurement techniques by many orders of magnitude in recording density and channels will be essential to future progress in understanding neuron circuits.This project is exploiting emerging electrophysiological recording systems in which the electrode (and channel) count is increased by almost three orders of magnitude over conventional systems with data bandwidths exceeding 1GB/sec. To handle these data bandwidths and resulting data volumes and deliver scalability, this project will develop dedicated hardware and associated algorithms for spike detection and sorting that allow these tasks to be performed in real-time in close proximity to the recording system. Compression by more than three orders of magnitude is possible by these means by taking advantage of the special spatiotemporal local structure in these data sets; by exploiting strong prior information about the spiking signal and reducing the dimensionality of the problem accordingly; and by adapting and extending modern scalable nonparametric Bayesian inference methods. In addition to providing important new tools for neuroscience, the tools developed here for scalable real-time event detection and annotation have broad applicability to other spatiotemporal data sets (or more generally, any data set comprising multiple streams of data, in which the streams could involve different data modalities) in which objects of interest are spatially and temporally localized with fixed spatial footprints. Examples abound in cell and molecular biology, particle and solid-state physics, financial monitoring, monitoring of power networks, and sensor networks.

可以说，了解大脑的工作原理是我们这个时代最重要的科学挑战之一，也是大脑计划的重点。人们普遍认为，神经回路功能是紧急的，这是成分与单个神经元与数千个其他神经元形成突触连接的复杂相互作用的结果。这些复杂电路的映射实际上是不可能的，因为依赖电生理记录的这些网络非常稀少。这些用于细胞外峰值记录的工具只能同时记录几十至数百个神经元。这些记录电极的原始信号首先被过滤以删除带外信号。然后检测并提取假定的尖峰事件。最后，这些时间序列事件的这些片段通常是根据波形形状分类为簇。即使在这些系统的非常适中的带宽下，计算系统也很难保存数据并处理所得的数据集。这些测量技术在记录密度和通道中通过许多数量级的可伸缩性对于理解神经元电路的未来进步至关重要。该项目正在利用新兴的电生理记录系统，其中电极（和通道）计数在具有超过1gb/sec的传统系统中增加了几乎三个数量级。为了处理这些数据带宽和产生的数据量并提供可扩展性，该项目将开发专用硬件和相关的峰值检测和排序算法，以允许这些任务可以在录制系统的附近实时执行。通过利用这些数据集中的特殊时空局部结构，这些方法可以通过三个以上的数量级来压缩。通过利用有关峰值信号的强大先验信息并相应地降低问题的维度；并通过适应和扩展现代可伸缩的非参数贝叶斯推理方法。除了为神经科学提供重要的新工具外，此处开发的用于可扩展的实时事件检测和注释的工具还广泛适用于其他时空数据集（或更一般而言，任何包含多个数据流的数据集，其中流可以涉及不同的数据模式），其中感兴趣的对象具有空间和时间上的固定固定的固定型号脚部图。细胞和分子生物学，粒子和固态物理学，财务监测，电力网络监测和传感器网络中的示例比比皆是。