Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

In this article, the authors show that the neural representation for control of a neuroprosthetic device undergoes a process of consolidation, after which it is stable, readily recalled, and resistant to interference.

A paradigm is described for recording the activity of single cortical neurons from awake, behaving macaque monkeys. Its unique features include high-density microwire arrays and multichannel instrumentation. Three adult rhesus monkeys received microwire array implants, totaling 96 -704 microwires per subject, in up to five cortical areas, sometimes bilaterally. Recordings 3-4 weeks after implantation yielded 421 single neurons with a mean peak-to-peak voltage of 115 ؎ 3 V and a signal-to-noise ratio of better than 5:1. As many as 247 cortical neurons were recorded in one session, and at least 58 neurons were isolated from one subject 18 months after implantation. This method should benefit neurophysiological investigation of learning, perception, and sensorimotor integration in primates and the development of neuroprosthetic devices.

The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills1, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills2–4, it remains unclear if similar circuits or processes are required for abstract skill learning. We utilized a novel behavioral paradigm in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrated that these learned neuroprosthetic actions were intentional and goal-directed, rather than habitual. Striatal neurons changed their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progressed. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerged. Specific deletion of striatal NMDA receptors impaired the development of this corticostriatal plasticity, and disrupted the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.

The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies.

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.

Cortical neural prostheses require chronically implanted small-area microelectrode arrays that simultaneously record and stimulate neural activity. It is necessary to develop new materials with low interface impedance and large charge transfer capacity for this application and we explore the use of conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) for the same. We subjected PEDOT coated electrodes to voltage cycling between -0.6 and 0.8 V, 24 h continuous biphasic stimulation at 3 mC/cm² and accelerated aging for four weeks. Characterization was performed using cyclic voltammetry, electrochemical impedance spectroscopy, and voltage transient measurements. We found that PEDOT coated electrodes showed a charge injection limit 15 times higher than Platinum Iridium (PtIr) electrodes and electroplated Iridium Oxide (IrOx) electrodes when using constant current stimulation at zero voltage bias. In vivo chronic testing of microelectrode arrays implanted in rat cortex revealed that PEDOT coated electrodes show higher signal-to-noise recordings and superior charge injection compared to PtIr electrodes.

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.