Improved neuronal ensemble inference with generative model and MCMC. (1st June 2021)

• Record Type: Journal Article
• Authors: Kimura, Shun; Ota, Keisuke; Takeda, Koujin
• Is Part Of: Journal of statistical mechanics. (2021:Jun.)
• Publication Date: 2021-06-01
• Subjects: clustering techniques -- computational neuroscience -- neuronal networks -- statistical inference; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/abffd5
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: 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.