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

理解大脑是如何工作的可以说是我们这个时代最重要的科学挑战之一，也是brain计划的重点。人们普遍认为，神经回路的功能是突现的，是各成分之间复杂相互作用的结果，单个神经元与成千上万的其他神经元形成突触连接。这些复杂电路的映射实际上是不可能的，因为依赖于对这些网络采样极其稀疏的电生理记录。这些细胞外脉冲记录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。然后检测并提取假定的尖峰事件。最后，通常根据波形形状对这些时间序列事件片段进行分类。即使在这些系统非常有限的带宽下，计算系统也难以保存数据并处理产生的数据集。这些测量技术在记录密度和通道方面的可扩展性将对未来理解神经元回路的进展至关重要。该项目正在开发新兴的电生理记录系统，其中电极（和通道）数量比传统系统增加了近三个数量级，数据带宽超过1GB/秒。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算法，用于峰值检测和排序，使这些任务能够在接近记录系统的情况下实时执行。利用这些数据集中特殊的时空局部结构，可以实现三个数量级以上的压缩；利用尖峰信号的强先验信息，相应降低问题的维数；并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外，这里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（或者更一般地说，包括多个数据流的任何数据集，其中流可能涉及不同的数据模式），其中感兴趣的对象在空间和时间上具有固定的空间足迹。这样的例子在细胞和分子生物学、粒子和固态物理学、金融监测、电网监测和传感器网络中比比皆是。

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.