Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 units per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years), and recording of a broad range of behaviors, e.g. social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research, while providing a framework for the development and testing of clinically relevant neuroprostheses.

Rewards are known to influence neural activity associated with both motor preparation and execution. This influence can be exerted directly upon the primary motor (M1) and somatosensory (S1) cortical areas via the projections from reward-sensitive dopaminergic neurons of the midbrain ventral tegmental areas. However, the neurophysiological manifestation of reward-related signals in M1 and S1 are not well understood. Particularly, it is unclear how the neurons in these cortical areas multiplex their traditional functions related to the control of spatial and temporal characteristics of movements with the representation of rewards. To clarify this issue, we trained rhesus monkeys to perform a center-out task in which arm movement direction, reward timing, and magnitude were manipulated independently. Activity of several hundred cortical neurons was simultaneously recorded using chronically implanted microelectrode arrays. Many neurons (9-27%) in both M1 and S1 exhibited activity related to reward anticipation. Additionally, neurons in these areas responded to a mismatch between the reward amount given to the monkeys and the amount they expected: A lower-than-expected reward caused a transient increase in firing rate in 60-80% of the total neuronal sample, whereas a larger-than-expected reward resulted in a decreased firing rate in 20-35% of the neurons. Moreover, responses of M1 and S1 neurons to reward omission depended on the direction of movements that led to those rewards. These observations suggest that sensorimotor cortical neurons corepresent rewards and movement-related activity, presumably to enable reward-based learning.

Accumulatormodelsthatintegrateincomingsensoryinformationintomotorplansprovidearobustframeworktounderstanddecisionmaking.However, their applicability to situations that demand a change of plan raises an interesting problem for the brain. This is because interruption of the current motor plan must occur by a competing motor plan, which is necessarily weaker in strength. To understand how changes of mind get expressed in behavior, we used a version of the double-step task called the redirect task, in which monkeys were trained to modify a saccade plan. We microstimulated the frontal eye fields during redirect behavior and systematically measured the deviation of the evoked saccade from the response field to causally track the changing saccade plan. Further, to identify the underlying mechanisms, eight different computational models of redirect behavior were assessed. It was observed that the model that included an independent, spatially specific inhibitory process, in addition to the two accumulators representing the preparatory processes of initial and final motor plans, best predicted the performance and the pattern of saccade deviation profile in the task. Such an inhibitory process suppressed the preparation of the initial motor plan, allowing the final motorplantoproceedunhindered.Thus,changesofmindareconsistentwiththenotionofaspatiallyspecific,inhibitoryprocessthatinhibitsthe current inappropriate plan, allowing expression of the new plan.

Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

Sharika KM, Ramakrishnan A, Murthy A. Control of predictive error correction during a saccadic double-step task. J Neurophysiol 100: 2757-2770, 2008. First published September 24, 2008 doi:10.1152/jn.90238.2008. We explored the nature of control during error correction using a modified saccadic double-step task in which subjects cancelled the initial saccade to the first target and redirected gaze to a second target. Failure to inhibit was associated with a quick corrective saccade, suggesting that errors and corrections may be planned concurrently. However, because saccade programming constitutes a visual and a motor stage of preparation, the extent to which parallel processing occurs in anticipation of the error is not known. To estimate the time course of error correction, a triple-step condition was introduced that displaced the second target during the error. In these trials, corrective saccades directed at the location of the target prior to the third step suggest motor preparation of the corrective saccade in parallel with the error. To estimate the time course of motor preparation of the corrective saccade, further, we used an accumulator model (LATER) to fit the reaction times to the triple-step stimuli; the best-fit data revealed that the onset of correction could occur even before the start of the error. The estimated start of motor correction was also observed to be delayed as target step delay decreased, suggesting a form of interference between concurrent motor programs. Taken together we interpret these results to indicate that predictive error correction may occur concurrently while the oculomotor system is trying to inhibit an unwanted movement and suggest how inhibitory control and error correction may interact to enable goal-directed behaviors.