美国德克萨斯大学Ila R. Fiete研究组发现由强循环网络中的活动推断出的系统性连接错误。2020年9月7日，《自然-神经科学》发表了这一成果。

他们表明，即使是应用于回路中每个单元无限数据的复杂方法，也是倾向于推断未连接但高度相关的神经元之间的连接。当真实的网络动力学与用于推理的模型之间不匹配时，就会发生这种无法“解释”的连接故障，这在对真实世界进行建模时是不可避免的。

因此，当变量高度相关时，因果推理会受到影响，在强连接网络中，应特别谨慎地考虑基于活动的连接性估计。最后，对通过简单的低维抑制驱动器使回路的活动超出平衡而进行的推理，可能会改善推理偏差。

研究人员表示，理解神经计算和学习的机制将需要基础环路知识。由于难以直接测量神经回路的连接图，因此长期以来人们一直从多单元活动记录中，通过算法估算它们来进行研究。

附：英文原文

Title: Systematic errors in connectivity inferred from activity in strongly recurrent networks

Author: Abhranil Das, Ila R. Fiete

Issue&Volume: 2020-09-07

Abstract: Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them algorithmically from multicell activity recordings. We show that even sophisticated methods, applied to unlimited data from every cell in the circuit, are biased toward inferring connections between unconnected but highly correlated neurons. This failure to ‘explain away’ connections occurs when there is a mismatch between the true network dynamics and the model used for inference, which is inevitable when modeling the real world. Thus, causal inference suffers when variables are highly correlated, and activity-based estimates of connectivity should be treated with special caution in strongly connected networks. Finally, performing inference on the activity of circuits pushed far out of equilibrium by a simple low-dimensional suppressive drive might ameliorate inference bias.

DOI: 10.1038/s41593-020-0699-2

Source: https://www.nature.com/articles/s41593-020-0699-2