Department of Physics, The University of Texas, Austin, TX, USA.
Center for Learning and Memory, The University of Texas, Austin, TX, USA.
Nat Neurosci. 2020 Oct;23(10):1286-1296. doi: 10.1038/s41593-020-0699-2. Epub 2020 Sep 7.
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需要了解基础电路。由于很难直接测量神经回路的布线图，因此人们一直有兴趣从多细胞活动记录中通过算法来估计它们。我们表明，即使是复杂的方法，应用于来自电路中每个细胞的无限数据，也会偏向于推断未连接但高度相关的神经元之间的连接。当真实网络动态与用于推断的模型之间不匹配时，就会出现这种无法“解释”连接的情况，而在对现实世界进行建模时，这种不匹配是不可避免的。因此，当变量高度相关时，因果推断就会受到影响，并且在强连接网络中，基于活动的连接估计值应该特别小心地对待。最后，通过简单的低维抑制驱动将电路的活动推向远离平衡的状态进行推断，可能会减轻推断偏差。
