Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains

Abstract

Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.