  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.
