Stringer Carsen, Zhong Lin, Syeda Atika, Du Fengtong, Kesa Maria, Pachitariu Marius
Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA, USA.
Nat Neurosci. 2025 Jan;28(1):201-212. doi: 10.1038/s41593-024-01783-4. Epub 2024 Oct 16.
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.
长期以来，神经生理学是通过探索性实验和偶然发现取得进展的。有许多轶事讲述研究人员实时聆听神经元放电，并注意到与正在进行的刺激或行为相关的活动模式。随着大规模记录技术的出现，对数据进行如此细致的观察变得困难起来。为了在大规模神经数据中找到模式，我们开发了“Rastermap”，这是一种可视化方法，它根据神经元的活动模式将它们沿着一维轴排序后，以光栅图的形式展示神经元。我们在逼真的模拟上对Rastermap进行了基准测试，然后用它来探索小鼠皮层在自发、刺激诱发和任务诱发阶段的数万个神经元的记录。我们还将Rastermap应用于斑马鱼全脑记录；宽场成像数据；大鼠海马体、猴子额叶皮层以及小鼠各种皮层和皮层下区域的电生理记录；以及人工神经网络。最后，我们展示了Rastermap和类似算法无法有效使用的高维场景。
