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.

了解大脑如何工作可以说是我们这个时代最重大的科学挑战之一，也是BRAIN计划的重点。人们普遍认为，神经回路功能是涌现的，是成分之间复杂相互作用的结果，单个神经元与数千个其他神经元形成突触连接。这些复杂的电路的映射几乎是不可能的，因为依赖于电生理记录，这些网络的样本非常稀疏。这些用于细胞外锋电位记录的工具只能同时记录几十到几百个神经元。来自这些记录电极的原始信号首先被滤波以去除带外信号。然后检测并提取推定的尖峰事件。最后，这些时间序列事件的片段通常基于波形形状被分类为集群。即使在这些系统的非常适度的带宽下，计算系统也难以保存数据并处理所得到的数据集。这些测量技术在记录密度和通道方面的许多数量级的可扩展性对于未来理解神经元Circuits.This project is exploiting emerging electrophysiological recording systems in which the electrode（and channel）count is increased by almost three orders over conventional systems with data bandwidth exceeding 1GB/sec.为了处理这些数据带宽和由此产生的数据量并提供可扩展性，该项目将开发用于尖峰检测和排序的专用硬件和相关算法，使这些任务能够在记录系统附近实时执行。压缩超过三个数量级是可能的，通过这些手段，利用这些数据集的特殊时空局部结构;通过利用强先验信息的尖峰信号，并相应地减少问题的维数;和通过适应和扩展现代可扩展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之外，这里开发的用于可扩展的实时事件检测和注释的工具对其他时空数据集（或更一般地，包括多个数据流的任何数据集，其中流可以涉及不同的数据模态）具有广泛的适用性，其中感兴趣的对象在空间和时间上以固定的空间足迹定位。在细胞和分子生物学、粒子和固态物理学、金融监控、电力网络监控和传感器网络中有大量的例子。