Improved Neuronal Ensemble Inference with Generative Model and MCMC
This work addresses a computational bottleneck in neuronal ensemble inference for neuroscience researchers, but it is incremental as it builds on an existing Bayesian approach.
The paper tackled the problem of high computational cost in Bayesian inference for neuronal ensemble inference by modifying the MCMC update rule and using simulated annealing for hyperparameter control, resulting in improved performance compared to the original method.
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.