通过对齐它们的低维潜在表征来比较高维神经记录
Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations
摘要：
神经科学中的许多问题涉及对大量神经元的反应的理解。然而，当处理大规模神经活动时，解释变得困难，并且在两个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对应性，例如我们不能在完全相同的时间记录完全相同的神经元。如果没有某种方法来链接两个或多个数据集，比较不同的神经活动模式集合就变得不可能。在这里，我们描述了利用跨神经记录的共享潜在结构来解决这种对应性挑战的方法。我们回顾了将两个数据集映射到一个共享空间的算法，在这个空间中，它们可以直接进行比较，并认为对齐是跨时间、神经元子集和个体比较高维神经活动的关键。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
