Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity.
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity.
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DOI：
10.1016/j.conb.2021.08.002
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发表时间：
2021-10
影响因子：
5.7
通讯作者：
Ostojic S
中科院分区：
文献类型：
--
作者：
Jazayeri M;Ostojic S
记录容量的持续指数增长需要新的方法来分析和解释神经数据。有效维度已成为神经元群体神经活动的重要属性，但不同的研究依赖于对该量的不同定义和解释。在这里，我们关注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。回顾最近的研究，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处理这些信息的方式。最后，我们强调网络模型作为理想基础的作用，用于更具体地测试通过内在维度和嵌入维度反映的计算原理的各种假设。
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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