Strong and weak principles of neural dimension reduction
Strong and weak principles of neural dimension reduction
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DOI：
10.51628/001c.24619
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发表时间：
2020-11
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影响因子：
--
通讯作者：
M. Humphries
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文献类型：
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作者：
M. Humphries
如果尖峰信号是媒介，那么它传递的信息是什么？这个问题的解决推动了大规模的、单个神经元分辨率的动物行为记录的发展，在数千个神经元的规模上。但这些数据本质上是高维的，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，答案是减少维度的数量。在这里，我认为我们可以区分神经降维的弱原则和强原则。弱原理是，降维是理解复杂神经数据的方便工具。最强的原理是，降维向我们展示了神经回路实际上是如何运作和计算的。阐明这些原则是至关重要的，因为我们所认同的原则对相同的神经活动数据提供了完全不同的解释。我展示了我们如何根据我们如何对数据使用降维的无害决策来使弱或强原则看起来是真的。为了抵消这些混乱，我概述了实验证据的强原则，不来自降维，但也表明有一些神经现象，强的原则未能解决。为了调和这些相互矛盾的数据，我认为大脑有两个原则在起作用。
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.