Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spik...

the present investigation was focused to formulate semi-solid capsules (sscs) of hydrophobic drug telmisartan (tlms) by encapsulating semi-solid matrix of its solid dispersion (sd) in hpmc capsules. the combinational approach was used to reduce the lag time in drug release and improvise its dissolution. sds of tlms was prepared using hot fusion method by varying the combinations of pluronic-f68...

Time series forecasting based on deep architectures has been gaining popularity in recent years due to their ability model complex non-linear temporal dynamics. The recurrent neural network is one such capable of handling variable-length input and output. In this paper, we leverage advances generative models the concept state space propose a stochastic adaptation for multistep-ahead time foreca...

We propose to employ a recurrent neural network estimate fluctuating magnetic field from continuous optical Faraday rotation measurement on an atomic ensemble. show that encoder-decoder architecture can process data and learn accurate map between recorded signals the time-dependent field. The performance of this method is comparable Kalman filters while it free theory assumptions restrict their...

A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic con...