# Improved algorithm for neuronal ensemble inference by Monte Carlo method

arXiv: https://arxiv.org/abs/1911.06509 · HTML: https://mlsanity.com/paper/1911.06509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Improved Neuronal Ensemble Inference with Generative Model and MCMC](https://mlsanity.com/paper/2105.09679/) (2021)

## Abstract (arXiv)

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.