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.

SUMMARY Muscle synergies have been proposed as a mechanism to simplify movement control. Whether these coactivation patterns have any physiological reality within the nervous system remains unknown. Here we applied electrical microstimulation to motor cortical areas of rhesus macaques to evoke hand movements. Movements tended to converge towards particular postures, driven by synchronous bursts of muscle activity. Across stimulation sites, the muscle activations were reducible to linear sums of a few basic patterns—each corresponding to a muscle synergy evident in voluntary reach, grasp, and transport movements made by the animal. These synergies were represented non-uniformly over the cortical surface. We argue that the brain exploits these properties of synergies—postural equivalence, low dimensionality, and topographical representation—to simplify motor planning, even for complex hand movements.

In grasping, the CNS controls a particularly large number of degrees of freedom. We tested the idea that this control is facilitated by the presence of muscle synergies. According to the strong version of this concept, these synergies are invariant, hard-wired patterns of activation across muscles. Synergies may serve as modules that linearly sum, each with specific amplitude and timing coefficients, to generate a large array of muscle patterns. We tested two predictions of the synergy model. A small number of synergies should (1) account for a large fraction of variation in muscle activity, and (2) be modulated in their recruitment by task variables, even in novel behavioral contexts. We also examined whether the synergies would (3) have broadly similar structures across animals. We recorded from 15 to 19 electrodes implanted in forelimb muscles of two rhesus macaques as they grasped and transported 25 objects of variable shape and size. We show that three synergies accounted for 81% of the electromyographic data variation in each monkey. Each synergy was modulated in its recruitment strength and/or timing by object shape and/or size. Even when synergies were extracted from a small subset of object shape and size conditions and then used to reconstruct the entire dataset, we observed highly similar synergies and patterns of modulation. The synergies were well conserved between monkeys, with two of the synergies exceeding chance structural similarity, and the third being recruited, in both animals, in proportion to the size of the object handled.

Although multiple lines of evidence implicate the primary motor cortex (M1) in motor learning, the precise role of M1 in the adaptation to novel movement dynamics and in the subsequent consolidation of a memory of those dynamics remains unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to dissociate the contribution of M1 to these distinct aspects of motor learning. Subjects performed reaching movements in velocity-dependent force fields over three epochs: a null-field baseline epoch, a clockwisefield learning epoch (15 min after the baseline epoch), and a clockwise-field retest epoch (24 h after the learning epoch). Half of the subjects received 15 min of 1 Hz rTMS to M1 between the baseline and learning epochs. Subjects given rTMS performed identically to control subjects during the learning epoch. However, control subjects performed with significantly less error than rTMS subjects in the retest epoch on the following day. These results suggest that M1 is not critical to the network supporting motor adaptation per se but that, within this network, M1 may be important for initiating the development of long-term motor memories.

Evidence suggests that the CNS uses motor primitives to simplify movement control, but whether it actually stores primitives instead of computing solutions on the fly to satisfy task demands is a controversial and still-unanswered possibility. Also in contention is whether these primitives take the form of time-invariant muscle coactivations ("spatial" synergies) or time-varying muscle commands ("spatiotemporal" synergies). Here, we examined forelimb muscle patterns and motor cortical spiking data in rhesus macaques (Macaca mulatta) handling objects of variable shape and size. From these data, we extracted both spatiotemporal and spatial synergies using non-negative decomposition. Each spatiotemporal synergy represents a sequence of muscular or neural activations that appeared to recur frequently during the animals' behavior. Key features of the spatiotemporal synergies (including their dimensionality, timing, and amplitude modulation) were independently observed in the muscular and neural data. In addition, both at the muscular and neural levels, these spatiotemporal synergies could be readily reconstructed as sequential activations of spatial synergies (a subset of those extracted independently from the task data), suggestive of a hierarchical relationship between the two levels of synergies. The possibility that motor cortex may execute even complex skill using spatiotemporal synergies has novel implications for the design of neuroprosthetic devices, which could gain computational efficiency by adopting the discrete and low-dimensional control that these primitives imply.