LAUSR

Objective. Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment. Approach. To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the variational Bayesian inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters. Main results. Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved. Significance. Our work can be applied to rapidly decode large-scale multichannel neural spike trains in BMIs.