van Beest Enny H, Bimbard Célian, Fabre Julie M J, Dodgson Sam W, Takács Flóra, Coen Philip, Lebedeva Anna, Harris Kenneth D, Carandini Matteo
UCL Institute of Ophthalmology, University College London, London, UK.
UCL Queen Square Institute of Neurology, University College London, London, UK.
Nat Methods. 2025 Apr;22(4):778-787. doi: 10.1038/s41592-024-02440-1. Epub 2024 Sep 27.
Neural activity spans multiple time scales, from milliseconds to months. Its evolution can be recorded with chronic high-density arrays such as Neuropixels probes, which can measure each spike at tens of sites and record hundreds of neurons. These probes produce vast amounts of data that require different approaches for tracking neurons across recordings. Here, to meet this need, we developed UnitMatch, a pipeline that operates after spike sorting, based only on each unit's average spike waveform. We tested UnitMatch in Neuropixels recordings from the mouse brain, where it tracked neurons across weeks. Across the brain, neurons had distinctive inter-spike interval distributions. Their correlations with other neurons remained stable over weeks. In the visual cortex, the neurons' selectivity for visual stimuli remained similarly stable. In the striatum, however, neuronal responses changed across days during learning of a task. UnitMatch is thus a promising tool to reveal both invariance and plasticity in neural activity across days.
神经活动跨越多个时间尺度，从毫秒到数月不等。其演变过程可以通过慢性高密度阵列（如神经像素探针）进行记录，这种探针可以在数十个位点测量每个尖峰，并记录数百个神经元。这些探针产生大量数据，需要不同的方法来在不同记录中追踪神经元。在这里，为满足这一需求，我们开发了UnitMatch，这是一种在尖峰排序之后运行的流程，仅基于每个单元的平均尖峰波形。我们在小鼠大脑的神经像素记录中测试了UnitMatch，它在数周内追踪神经元。在整个大脑中，神经元具有独特的峰峰间隔分布。它们与其他神经元的相关性在数周内保持稳定。在视觉皮层中，神经元对视觉刺激的选择性同样保持稳定。然而，在纹状体中，在学习一项任务的过程中，神经元反应在数天内会发生变化。因此，UnitMatch是一个很有前景的工具，可用于揭示数天内神经活动的不变性和可塑性。
