通过检查神经活动的内在维度和嵌入维度来解释神经计算
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity
摘要：
记录能力的持续指数增长需要新的方法来分析和解释神经数据。有效维度已经成为神经元群体中神经活动的一个重要属性，然而不同的研究依赖于对这个数量的不同定义和解释。在这里，我们专注于内在维度和嵌入维度，并讨论它们如何从数据中揭示计算原理。回顾最近的工作，我们提出，内在维度反映了集体活动中编码的潜在变量的信息，而嵌入维度揭示了这些信息的处理方式。最后，我们强调网络模型的作用，作为一个理想的基板，更具体地测试各种假设的计算原理，反映通过内在和嵌入的维度。
英文摘要：
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity, while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
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