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.
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Acknowledgements
We appreciate the comments from Giovanni Diana and Yuishi Iwasaki. This work is supported by KAKENHI Nos. 18K11175, 19K12178.
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Kimura, S., Takeda, K. (2020). Improved Algorithm for Neuronal Ensemble Inference by Monte Carlo Method. In: Masuda, N., Goh, KI., Jia, T., Yamanoi, J., Sayama, H. (eds) Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science. NetSci-X 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-38965-9_6
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DOI: https://doi.org/10.1007/978-3-030-38965-9_6
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