美国霍华德休斯医学研究所Marius Pachitariu团队近期取得重要工作进展，他们研究开发了Rastermap：一种神经群体记录的发现方法。相关研究成果2024年10月16日在线发表于《自然—神经科学》杂志上。

据介绍，神经生理学长期以来一直通过探索性实验和偶然发现取得进展。研究人员实时听到尖峰信号，并注意到与持续刺激或行为相关的活动模式，这其中有很多轶事。随着大规模记录的出现，对数据进行如此密切的观察变得困难。

为了在大规模神经数据中找到模式，研究人员开发了“Rastermap”，这是一种可视化方法，在根据神经元的活动模式沿一维轴对其进行排序后，将神经元显示为光栅图。研究人员在现实模拟中对Rastermap进行了基准测试，然后用它来探索自发、刺激诱发和任务诱发时期小鼠皮层数万个神经元的记录。

研究人员还将Rastermap应用于斑马鱼的全脑记录；宽视场成像数据；大鼠海马、猴额叶皮层和小鼠各种皮质和皮质下区域的电生理记录；以及人工神经网络。最后，研究人员举例说明了Rastermap和类似算法无法有效使用的高维场景

附：英文原文

Title: Rastermap: a discovery method for neural population recordings

Author: Stringer, Carsen, Zhong, Lin, Syeda, Atika, Du, Fengtong, Kesa, Maria, Pachitariu, Marius

Issue&Volume: 2024-10-16

Abstract: Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed ‘Rastermap’, a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

DOI: 10.1038/s41593-024-01783-4

Source: https://www.nature.com/articles/s41593-024-01783-4