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Improved Algorithm for Neuronal Ensemble Inference by Monte Carlo Method

  • Shun Kimura
  • Koujin TakedaEmail author
Conference paper
  • 62 Downloads
Part of the Springer Proceedings in Complexity book series (SPCOM)

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.

Keywords

Neural network Bayesian inference Markov chain Monte Carlo method Simulated annealing Neuronal dynamics and structure inference 

Notes

Acknowledgements

We appreciate the comments from Giovanni Diana and Yuishi Iwasaki. This work is supported by KAKENHI Nos. 18K11175, 19K12178.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mechanical Systems EngineeringGraduate School of Science and Engineering, Ibaraki UniversityIbarakiJapan

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