Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

人工操纵神经活动的技术进步促进了研究大脑回路对认知和行为的因果作用的激增。然而，神经回路的复杂性对实验结果的解释提出了挑战，需要新的理论框架来推理因果效应。在这里，我们通过训练执行感知决策的循环神经网络的镜头，朝这个方向迈出了一步。我们表明，了解网络解决方案背后的动态系统结构可以精确解释扰动造成的行为影响的程度。我们的框架通过阐明行为最敏感的特征以及复杂的电路如何补偿和适应扰动来解释过去的经验观察。在此过程中，我们还确定了可以提高灭活实验可解释性的策略。