Closed-Loop Decoder Adaptation Shapes Neural Plasticity for Skillful Neuroprosthetic Control

Neuroplasticity may play a critical role in developing robust, naturally controlled neuroprostheses. This learning, however, is sensitive to system changes such as the neural activity used for control. The ultimate utility of neuroplasticity in real-world neuroprostheses is thus unclear. Adaptive decoding methods hold promise for improving neuroprosthetic performance in nonstationary systems. Here, we explore the use of decoder adaptation to shape neuroplasticity in two scenarios relevant for real-world neuroprostheses: nonstationary recordings of neural activity and changes in control context. Nonhuman primates learned to control a cursor to perform a reaching task using semistationary neural activity in two contexts: with and without simultaneous arm movements. Decoder adaptation was used to improve initial performance and compensate for changes in neural recordings. We show that beneficial neuroplasticity can occur alongside decoder adaptation, yielding performance improvements, skill retention, and resistance to interference from native motor networks. These results highlight the utility of neuroplasticity for real-world neuroprostheses. •Combined neural and decoder adaptation can yield skillful neuroprosthetic control•Decoder adaptation shapes neural representations of neuroprosthetic control•Learning yields changes in the timing of neural recruitment•Neuroprosthetic skill formation reduces interference from native motor networks Orsborn et al. demonstrate that neural and decoder adaptation can be combined to achieve and maintain skillful neuroprosthetic control despite changes in neural recordings and control contexts. They show that these adaptation mechanisms interact to shape long-term neuroprosthetic performance.