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.

理解神经计算和学习的机制将需要基础电路的知识。由于很难直接测量神经回路的接线图，因此长期以来人们一直对从多细胞活动记录中通过算法估算它们进行研究。我们表明，即使将复杂的方法应用于电路中每个单元的无限数据，也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不匹配时，就会发生这种无法“解释”连接的故障，而在对真实世界进行建模时，这是不可避免的。因此，当变量高度相关时，因果推理会受到影响，在高度连接的网络中，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推离平衡状态远的电路的活动进行推理可能会改善推理偏差。