Neural population partitioning and a concurrent brain-machine interface for sequential motor function

Abstract

While brain-machine interfaces (BMIs) have largely focused on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence prior to its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Here, we show that concurrent BMI decoding is possible. Using population-wide modeling, we discover two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such surprising stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. Based on these findings, we develop a BMI that concurrently decodes a full motor sequence in advance of movement and then can accurately execute it as desired.