Improved Neuronal Ensemble Inference with Generative Model and MCMC

TL;DR

This paper presents an improved Bayesian inference algorithm for neuronal ensemble detection that reduces computational cost by modifying MCMC updates and incorporating simulated annealing for hyperparameter tuning.

Contribution

The authors introduce a novel MCMC update rule and hyperparameter control method that enhances efficiency in neuronal ensemble inference.

Findings

Reduced computational cost compared to previous methods

Enhanced accuracy in neuronal ensemble detection

Effective hyperparameter tuning with simulated annealing

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.