Decoding hindlimb kinematics from primate motor cortex using long short-term memory recurrent neural networks

Recent machine learning techniques have become a powerful tool in a variety of tasks, including neural decoding. Deep neural networks, particularly recurrent models, leverage the temporal evolution of neural ensemble activity to decode complex movement and sensory signals. Using single-unit recordings from microelectrode arrays implanted in the leg area of primary motor cortex in non-human primates, we decode the positions and angles of hindlimb joints during a locomotion task using a long short-term memory (LSTM) network. The LSTM decoder improved decoding over traditional filtering methods, such asWiener and Kalman filters. However, dramatic improvements over other machine learning (e.g. XGBoost) and latent state-space methods were not observed.