Improved algorithm for neuronal ensemble inference by Monte Carlo method

Autor:	Kimura, Shun, Takeda, Koujin
Rok vydání:	2019
Předmět:	Condensed Matter - Disordered Systems and Neural Networks Computer Science - Machine Learning Quantitative Biology - Neurons and Cognition
Zdroj:	Proceedings of NetSci-X 2020, pp.77-90
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-38965-9_6
Popis:	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. Comment: 14 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.06509 Zobrazit plný text záznamu View this record from Arxiv