论文标题
通过对齐其低维的潜在表示，比较高维神经记录
Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations
论文作者
论文摘要
神经科学中的许多问题涉及了解大量神经元的反应。但是，当处理大规模的神经活动时，解释变得困难，两只动物之间或在不同时间点之间进行比较变得具有挑战性。我们在现代神经科学中面临的一个主要挑战是通讯，例如在完全相同的时间，我们不会记录完全相同的神经元。如果没有某种方式链接两个或多个数据集，则不可能比较不同的神经活动模式集合。在这里，我们描述了利用神经记录跨神经记录的共享潜在结构的方法，以应对这一通信挑战。我们回顾了将两个数据集映射到可以直接比较的共享空间中的算法，并认为对齐是比较跨时间，神经元和个体的高维神经活动的关键。
Many questions in neuroscience involve understanding of the responses of large populations of neurons. However, when dealing with large-scale neural activity, interpretation becomes difficult, and comparisons between two animals, or across different time points becomes challenging. One major challenge that we face in modern neuroscience is that of correspondence, e.g. we do not record the exact same neurons at the exact same times. Without some way to link two or more datasets, comparing different collections of neural activity patterns becomes impossible. Here, we describe approaches for leveraging shared latent structure across neural recordings to tackle this correspondence challenge. We review algorithms that map two datasets into a shared space where they can be directly compared, and argue that alignment is key for comparing high-dimensional neural activities across times, subsets of neurons, and individuals.
