A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome^1,2,3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the ‘effectome’. Specifically, we propose an estimator for a linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons—thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly.

神经科学的一个目标是获得神经系统的因果模型。最近报道的全脑蝇连接组^1,2,3 指定了神经元可以相互影响的突触路径，但没有指定它们在体内相互影响的强度。为了克服这一限制，我们引入了一种结合实验和统计的策略，用于有效地学习苍蝇大脑的因果模型，我们称之为“效应组”。具体来说，我们提出了一个用于苍蝇大脑线性动力学模型的估计器，该模型使用随机光遗传学扰动数据来估计因果效应和连接组作为先验，从而大大提高了估计效率。我们在基于连接组的线性模拟中验证了我们的估计器，并表明它恢复了对更生物物理真实模拟的非线性动力学的线性近似。然后，我们分析连接组以提出主导果蝇神经系统动力学的回路。我们发现，主要回路仅涉及相对较小的神经元群——因此，神经元水平的成像、刺激和识别是可行的。这种方法还重新发现了已知的电路，并生成了关于其动力学的可检验假设。总体而言，我们提供的证据表明，苍蝇全脑动力学是由大量小电路产生的，这些电路在很大程度上彼此独立运行。这意味着大脑的因果模型可以在苍蝇中获得。