Various brain functions that are necessary to maintain life activities materialize through the interaction of countless neurons. Therefore, it is important to analyze the structure of functional neuronal network. To elucidate the mechanism of brain function, many studies are being actively conducted on the structure of 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.

翻译：维持生命活动所需的各种脑功能是通过无数神经元的相互作用实现的。因此，分析功能性神经网络的结构具有重要意义。为阐明脑功能机制，涉及神经科学各领域的大量研究正积极探讨功能性神经集群和中枢的结构。此外，近期研究表明，功能性神经集群和中枢的存在有助于信息处理效率的提升。基于这些原因，从神经元活动数据中推断功能性神经集群的方法需求日益增长，其中基于贝叶斯推断的方法已被提出。然而，贝叶斯推断在建模活动时存在问题。每个神经元活动的特征会根据生理实验条件产生非平稳性。这导致贝叶斯推断模型中的平稳性假设阻碍推断，使推断结果不稳定且精度下降。本研究扩展了表达神经元状态的变量范围，并泛化了扩展变量对应的模型似然函数。与先前研究相比，本模型可在更大空间中表达神经元状态。这种不受二值输入限制的泛化使我们能够实现软聚类，并将该方法应用于非平稳神经活动数据。此外，为验证方法的有效性，我们将所开发的方法应用于从泄漏积分点火模型电位数据生成的多个合成荧光数据。