Data-driven framework for joint estimation of neural membrane potential and calcium dynamics

抄録

Extracting nonlinear neuronal dynamics is one of the important subjects in neuroscience. However, we can access only limited and partially observable low-dimensional data with noise in many situations. In this study, we propose a data-driven method for simultaneously estimating membrane potential dynamics and calcium dynamics from partially observable noisy time-series data. We derive a sequential Monte Carlo method for estimating multi-dimensional neuronal dynamics from a conductance-based spiking neuron model. Furthermore, we derive an expectation-maximization algorithm for estimating membrane conductances by reflecting both membrane potential dynamics and calcium dynamics. Using the proposed method, we show that the proposed framework is effective for extracting neuronal membrane potential and calcium dynamics simultaneously.