The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models and explore the limitations of reconstructing stored patterns from synaptic connectivity.

翻译：大型神经电路综合合成电线图的出现创造了连结工程领域,并引起了一些开放的研究问题,其中一个问题是,鉴于神经元的神经元的循环连接矩阵,是否有可能重建存储在反复存在的神经元网络中的信息。在这里,我们通过确定在特定吸引者网络模型中何时解决这种推论问题在理论上是可能的,并且为此提供了一种实用的算法。算法以统计物理学的理论为基础,得出了近似贝叶斯的推论,并可以进行精确的分析。我们研究其三个不同模型的性能,并探索从合成连接中重建存储模式的局限性。