Improved neuronal ensemble inference with generative model and MCMC
Improved neuronal ensemble inference with generative model and MCMC
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DOI：
10.1088/1742-5468/abffd5
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发表时间：
2021-06-01
影响因子：
2.4
通讯作者：
Takeda, Koujin
中科院分区：
文献类型：
--
作者：
Kimura, Shun;Ota, Keisuke;Takeda, Koujin
神经元集群推断是生物神经网络研究中的一个重要问题。人们已经针对从神经元活动的实验数据进行集群推断提出了多种方法。其中，带有生成模型的贝叶斯推断方法是近期提出的。然而，这种方法为了进行恰当的推断需要巨大的计算成本。在这项工作中，我们通过修改马尔可夫链蒙特卡罗方法中的更新规则，并引入模拟退火的思想来控制超参数，给出了一种改进的贝叶斯推断算法。我们比较了我们的算法和原始算法在集群推断方面的性能，并讨论了我们方法的优势。
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.