Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control

The human motor system is capable of remarkably precise control of movements-consider the skill of professional baseball pitchers or surgeons. This precise control relies upon stable representations of movements in the brain. Here, we investigated the stability of cortical activity at multiple spatial and temporal scales by recording local field potentials (LFPs) and action potentials (multiunit spikes, MSPs) while two monkeys controlled a cursor either with their hand or directly from the brain using a brain-machine interface. LFPs and some MSPs were remarkably stable over time periods ranging from 3d to over 3 years; overall, LFPs were significantly more stable than spikes. We then assessed whether the stability of all neural activity, or just a subset of activity, was necessary to achieve stable behavior. We showed that projections of neural activity into the subspace relevant to the task (the task-relevant space) were significantly more stable than were projections into the task-irrelevant (or task-null) space. This provides cortical evidence in support of the minimum intervention principle, which proposes that optimal feedback control (OFC) allows the brain to tightly control only activity in the task-relevant space while allowing activity in the task-irrelevant space to vary substantially from trial to trial. We found that the brain appears capable of maintaining stable movement representations for extremely long periods of time, particularly so for neural activity in the task-relevant space, which agrees with OFC predictions.