  Neuronal ensemble inference is one of the significant problems in the study
of biological neural networks. Various methods have been proposed for ensemble
inference from their activity data taken experimentally. Here we focus on
Bayesian inference approach for ensembles with generative model, which was
proposed in recent work. However, this method requires large computational
cost, and the result sometimes gets stuck in bad local maximum solution of
Bayesian inference. In this work, we give improved Bayesian inference algorithm
for these problems. We modify ensemble generation rule in Markov chain Monte
Carlo method, and introduce the idea of simulated annealing for hyperparameter
control. We also compare the performance of ensemble inference between our
algorithm and the original one.
