  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.
