McGovern Institute for Brain Research, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Laboratoire de Neurosciences Cognitives, INSERM U960, École Normale Supérieure - PSL Research University, 75005, Paris, France.
Curr Opin Neurobiol. 2021 Oct;70:113-120. doi: 10.1016/j.conb.2021.08.002. Epub 2021 Sep 17.
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.
持续指数级增长的记录容量要求我们采用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活动的一个重要特性，但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。在回顾最近的工作时，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，而嵌入维度则揭示了信息处理的方式。最后，我们强调了网络模型的作用，它是作为一个理想的基质，用于更具体地测试内在维度和嵌入维度所反映的计算原理的各种假设。
