Continuous Neural Decoding Method Based on General Regression Neural Network

Neural decoding is an important task for understanding how the biological nervous system performs computation and communication. This paper introduces a novel continuous neural decoding method based on general regression neural network (GRNN). GRNN does not require an iterative training procedure as in other neural networks and allows the appropriate regression form to be expressed as a probability density function (pdf) that is empirically determined from the observed data using nonparametric estimators with no underlying assumptions. These characteristics make the GRNN decoder appropriate for continuous neural decoding problem. We compared the performance of the GRNN model with commonly used Wiener filter model in decoding rats’ neural activity of Motor Cortex (M1) during rats’ lever pressing task. Experiments show that GRNN has superior capacity for neural decoding.