Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of neuronal activity. Among them, Bayesian inference approach with generative model was proposed recently. However, this method requires large computational cost for appropriate inference. In this work, we give an improved Bayesian inference algorithm by modifying update rule in Markov chain Monte Carlo method and introducing the idea of simulated annealing for hyperparameter control. We compare the performance of ensemble inference between our algorithm and the original one, and discuss the advantage of our method.

神经元集成推理是生物神经网络研究中的一个重要问题。已经提出了各种方法来从神经元活动的实验数据进行集成推断。其中，最近提出了具有生成模型的贝叶斯推理方法。然而，这种方法需要大量的计算成本才能进行适当的推理。在这项工作中，我们通过修改马尔可夫链蒙特卡罗方法中的更新规则，并引入模拟退火的思想进行超参数控制，给出了一种改进的贝叶斯推理算法。我们比较了我们的算法和原始算法之间的集成推理性能，并讨论了我们方法的优势。