Department of Mechanics Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki, Japan.
PLoS One. 2023 Jun 27;18(6):e0287708. doi: 10.1371/journal.pone.0287708. eCollection 2023.
Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on functional neuronal ensemble and hub, including all areas of neuroscience. In addition, recent study suggests that the existence of functional neuronal ensembles and hubs contributes to the efficiency of information processing. For these reasons, there is a demand for methods to infer functional neuronal ensembles from neuronal activity data, and methods based on Bayesian inference have been proposed. However, there is a problem in modeling the activity in Bayesian inference. The features of each neuron's activity have non-stationarity depending on physiological experimental conditions. As a result, the assumption of stationarity in Bayesian inference model impedes inference, which leads to destabilization of inference results and degradation of inference accuracy. In this study, we extend the range of the variable for expressing the neuronal state, and generalize the likelihood of the model for extended variables. By comparing with the previous study, our model can express the neuronal state in larger space. This generalization without restriction of the binary input enables us to perform soft clustering and apply the method to non-stationary neuroactivity data. In addition, for the effectiveness of the method, we apply the developed method to multiple synthetic fluorescence data generated from the electrical potential data in leaky integrated-and-fire model.
各种维持生命活动所必需的大脑功能都是通过无数神经元的相互作用实现的。因此，分析功能神经元网络非常重要。为了阐明大脑功能的机制，包括神经科学的各个领域在内，许多研究都在积极进行功能神经元集合和枢纽的研究。此外，最近的研究表明，功能神经元集合和枢纽的存在有助于提高信息处理的效率。基于这些原因，人们需要从神经元活动数据中推断功能神经元集合的方法，并且已经提出了基于贝叶斯推理的方法。然而，贝叶斯推理中的建模存在一个问题。每个神经元活动的特征根据生理实验条件具有非平稳性。因此，贝叶斯推理模型中平稳性的假设会阻碍推理，导致推理结果不稳定，推理精度下降。在这项研究中，我们扩展了用于表示神经元状态的变量的范围，并推广了扩展变量的模型似然。与之前的研究相比，我们的模型可以在更大的空间中表示神经元状态。这种没有二元输入限制的推广使我们能够进行软聚类，并将该方法应用于非平稳神经活动数据。此外，为了验证该方法的有效性，我们将开发的方法应用于从漏电积分点火模型中的电势能数据生成的多个合成荧光数据。
