| 研究実績の概要 |
The primary achievement of our past research was a successful construction of a sophisticated framework to resolve the problem of estimating gap-junctional and inhibitory conductance from spike trains of inferior olive neurons (Hoang et al., 2015). Traditionally, the problem to estimate model parameters of a network of neurons based on spike timings of a number of neurons is severely ill-posed. That inverse problem thus needs a stochastic approach which finds most likely solution among many possible ones. We developed a theoretical framework of Bayesian estimation, which allows segmental fluctuations of parameter estimates in the neuronal constraint base, in order to compensate the modeling errors. The neuronal constraint avoids over-fitting as happened in our previous study (Onizuka et al., 2013) for highly non-stationary experimental data. We further validated that method in (Hoang and Tokuda, 2015), which utilized simulated spike data as the test data. The results on the both data sets confirmed that the Bayesian method has the potential to overcome the problem of non-stationary dynamics and resolved the ill-posedness of the inverse problem. We thus argue that our proposed Bayesian framework is a useful tool to evaluate the parameters of interest in neuroscience.
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