论文标题
降低神经维度的强大和弱原则
Strong and weak principles of neural dimension reduction
论文作者
论文摘要
如果尖峰是媒介，那是什么消息？回答这个问题是推动了来自行为动物的大规模，单个神经元分辨率记录的发展，数千个神经元的规模。但是这些数据本质上是高维的，具有与神经元一样多的维度 - 那么我们如何理解它们？对于许多人来说，答案是减少尺寸的数量。在这里，我认为我们可以区分降低神经维度的弱和强大原则。弱原则是，降低是一种方便的工具，可以理解复杂的神经数据。强有力的原则是，降低尺寸向我们展示了神经回路的实际运行和计算。阐明这些原理至关重要，我们为此提供了对同一神经活动数据的根本不同的解释。我展示了如何根据关于如何在数据上使用维度降低的无害决定，使弱原则或强大的原则似乎是正确的。为了抵消这些混杂，我概述了不来自降低尺寸的强有力原则的实验证据。但还表明，强有力的原则无法解决许多神经现象。为了调和这些矛盾的数据，我建议大脑在发挥两种原则。
If spikes are the medium, what is the message? Answering that question is driving the development of large-scale, single neuron resolution recordings from behaving animals, on the scale of thousands of neurons. But these data are inherently high-dimensional, with as many dimensions as neurons - so how do we make sense of them? For many the answer is to reduce the number of dimensions. Here I argue we can distinguish weak and strong principles of neural dimension reduction. The weak principle is that dimension reduction is a convenient tool for making sense of complex neural data. The strong principle is that dimension reduction shows us how neural circuits actually operate and compute. Elucidating these principles is crucial, for which we subscribe to provides radically different interpretations of the same neural activity data. I show how we could make either the weak or strong principles appear to be true based on innocuous looking decisions about how we use dimension reduction on our data. To counteract these confounds, I outline the experimental evidence for the strong principle that do not come from dimension reduction; but also show there are a number of neural phenomena that the strong principle fails to address. To reconcile these conflicting data, I suggest that the brain has both principles at play.
