Yu Han, Lyu Hanrui, Xu Ethan Yixun, Windolf Charlie, Lee Eric Kenji, Yang Fan, Shelton Andrew M, Olsen Shawn, Minavi Sahar, Winter Olivier, Dyer Eva L, Chandrasekaran Chandramouli, Steinmetz Nicholas A, Paninski Liam, Hurwitz Cole
Columbia University, New York, NY, USA.
Northwestern University, Evanston, IL, USA.
bioRxiv. 2024 Nov 5:2024.11.05.622159. doi: 10.1101/2024.11.05.622159.
Current electrophysiological approaches can track the activity of many neurons, yet it is usually unknown which cell-types or brain areas are being recorded without further molecular or histological analysis. Developing accurate and scalable algorithms for identifying the cell-type and brain region of recorded neurons is thus crucial for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this approach to jointly embed the activity autocorrelations and extracellular waveforms of individual neurons. We demonstrate that our embedding approach, euronal mbeddings via ultimdal contrastive learning (NEMO), paired with supervised fine-tuning, achieves state-of-the-art cell-type classification for an opto-tagged visual cortex dataset and brain region classification for the public International Brain Laboratory brain-wide map dataset. Our method represents a promising step towards accurate cell-type and brain region classification from electrophysiological recordings.
当前的电生理方法可以追踪许多神经元的活动，但如果没有进一步的分子或组织学分析，通常无法确定记录的是哪些细胞类型或脑区。因此，开发准确且可扩展的算法来识别记录神经元的细胞类型和脑区，对于增进我们对神经计算的理解至关重要。在这项工作中，我们开发了一种用于神经数据的多模态对比学习方法，该方法可以针对不同的下游任务进行微调，包括细胞类型和脑位置的推断。我们利用这种方法将单个神经元的活动自相关和细胞外波形联合嵌入。我们证明，我们的嵌入方法，即通过多模态对比学习实现的神经元嵌入（NEMO），与监督微调相结合，在光标记视觉皮层数据集上实现了细胞类型分类的最先进水平，在公开的国际脑实验室全脑图谱数据集上实现了脑区分类的最先进水平。我们的方法代表了从电生理记录中进行准确的细胞类型和脑区分类的有希望的一步。
