Generalization of generative model for neuronal ensemble inference method

TL;DR

This paper introduces a generalized Bayesian inference model for neuronal ensemble detection that accommodates non-stationary neuronal activity, enabling more accurate and flexible analysis of brain function from neuroactivity data.

Contribution

It extends existing Bayesian models by enlarging the neuronal state space, allowing for soft clustering and application to non-stationary data, improving inference stability and accuracy.

Findings

Model successfully expresses neuronal states in larger space.

Enables soft clustering of neuronal data.

Validated on synthetic fluorescence data from leaky integrate-and-fire model.

Abstract

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.