Tracking the Time Varying Neural Tuning via Adam on Point Process Observations.

Abstract

Brain machine interfaces(BMIs) translate the neural activity into the control of movement by understanding how the neural activity responds to the movement intension. However, the neural tuning property, where the modulation depth and preferred direction describe how a neuron responses to stimuli, is time varying gradually and abruptly during the interaction with environment. There has been some research to address such an issue considering either one of the cases, but never address them in a general framework. We propose a novel optimization algorithm based on the point process observations to capture these two changes at the same time. At each time index, the tuning parameter is updated stochastically according to the gradient based Adam searching method, which maximizes the likelihood of point process. Our algorithm is compared with the Adaptive Point Process Estimation (APPE), where the abrupt change is addressed by sampling all the possibilities globally, on synthetic neural data. The results show that our algorithm leads to a better prediction of tuning parameters as well as kinematics over 16.8% and 20% respectively.