Despite the existence of various neural recording and mapping techniques, there is an open territory for the emergence of novel techniques. The current neural imaging and recording techniques suffer from invasiveness, a time-consuming labeling process, poor spatial/ temporal resolution, and noisy signals. Among others, neuroplasmonics is a label-free and nontoxic recording technique with no issue of photo-bleaching or signal-averaging. We introduced an integrated plasmonic-ellipsometry platform for membrane activity detection with cost-effective and high-quality grating extracted from commercial DVDs. With ellipsometry technique, one can measure both amplitude (intensity) and phase difference of reflected light simultaneously with high signal to noise ratio close to surface plasmon resonances, which leads to the enhancement of sensitivity in plasmonic techniques. We cultured three different types of cells (primary hippocampal neurons, neuroblastoma SH-SY5Y cells, and human embryonic kidney 293 (HEK293) cells) on the grating surface. By introducing KCl solution as a chemical stimulus, we can differentiate the neural activity of distinct cell types and observe the signaling event in a label-free, optical recording platform. This method has potential applications in recording neural signal activity without labeling and stimulation artifacts.

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This paper develops a new class of spatio-temporal process models that can simultaneously capture skewness and non-stationarity. The proposed approach which is based on using the closed skew-normal distribution in the low-rank representation of stochastic processes, has several favorable properties. In particular, it greatly reduces the dimension of the spatio-temporal latent variables and induces flexible correlation structures. Bayesian analysis of the model is implemented through a Gibbs MCMC algorithm which incorporates a version of the Kalman filtering algorithm. All fully conditional posterior distributions have closed forms which show another advantageous property of the proposed model. We demonstrate the efficiency of our model through an extensive simulation study and an application to a real data set comprised of precipitation measurements.