Improved neuronal ensemble inference with generative model and MCMC

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