Improved algorithm for neuronal ensemble inference by Monte Carlo method

TL;DR

This paper presents an improved Bayesian inference algorithm for neuronal ensemble detection that reduces computational cost and avoids local maxima by modifying MCMC sampling with simulated annealing.

Contribution

The authors introduce a novel MCMC-based Bayesian inference method with simulated annealing for more efficient and accurate neuronal ensemble inference.

Findings

Reduced computational cost compared to previous methods

Improved accuracy in avoiding local maxima

Enhanced robustness of ensemble inference

Abstract

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.