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The ability to learn new actions and perfect them with practice allows us to master skills like playing the piano or riding a bicycle. Learning these skills usually implies moving [...], The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills (1), like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills (2-4), it remains unclear if similar circuits or processes are required for abstract skill learning. Here we use a novel behavioural task 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, demonstrate that these learned neuroprosthetic actions are intentional and goal-directed, rather than habitual. Striatal neurons change their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progresses. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerge. Specific deletion of striatal NMDA receptors impairs the development of this corticostriatal plasticity, and disrupts 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.

There is growing interest in intracortical microstimulation as a means of providing sensory input in neuroprosthetic systems. We believe that precisely controlling the timing and parameters of stimulation in closed loop can significantly improve the efficacy of this technique. Here, we present a system for closed-loop microstimulation in awake rodents chronically implanted with multielectrode arrays. The system interfaces with existing commercial recording and stimulating hardware. Using custom-made hardware, we can stimulate and record from electrodes on the same implanted array and significantly reduce the stimulation artifact. Stimulation sequences can either be preprogrammed or triggered by neural or behavioral events. Specifically, this system can provide feedback stimulation in response to action potentials or features in the local field potential recorded on any of the electrodes within 15 ms. It can also trigger stimulation based on behavioral events, such as real-time tracking of rat whiskers captured with highspeed video. We believe that this system, which can be recreated easily, will help to significantly refine the technique of intracortical microstimulation and advance the field of neuroprostheses. Index Terms--Artifact reduction, microstimulation, video tracking.

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms Index Terms--Brain-machine Interfaces, impedance control, musculoskeletal model, neuro-.

Research on brain-machine interfaces (BMI's) is directed toward enabling paralyzed individuals to manipulate their environment through slave robots. Even for able-bodied individuals, using a robot to reach and grasp objects in unstructured environments can be a difficult telemanipulation task. Controlling the slave directly with neural signals instead of a hand-master adds further challenges, such as uncertainty about the intended trajectory coupled with a low update rate for the command signal. To address these challenges, a continuous shared control (CSC) paradigm is introduced for BMI where robot sensors produce reflex-like reactions to augment brain-controlled trajectories. To test the merits of this approach, CSC was implemented on a 3-degree-of-freedom robot with a gripper bearing three co-located range sensors. The robot was commanded to follow eighty-three reach-and-grasp trajectories estimated previously from the outputs of a population of neurons recorded from the brain of a monkey. Five different levels of sensor-based reflexes were tested. Weighting brain commands 70% and sensor commands 30% produced the best task performance, better than brain signals alone by more than seven-fold. Such a marked performance improvement in this test case suggests that some level of machine autonomy will be an important component of successful BMI systems in general. Index Terms--Brain-machine interface, neuroprosthesis, shared control, telerobotics.

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble. Index Terms--Brain-machine interface, cosine tuning, information in neural ensembles, sensitivity-based model pruning.

To be clinically viable, a brain-machine interface (BMI) requires transcutaneous telemetry. Spike-based compression algorithms can be used to reduce the amount of telemetered data, but this type of system is subject to queuing-based transmission delays. This paper examines the relationships between the ratio of output to average input bandwidth of an implanted device and transmission latency and required queue depth. The examination was performed with a computer model designed to simulate the telemetry link. The input to the model was presorted spike data taken from a macaque monkey performing a motor task. The model shows that when the output bandwidth/average input bandwidth is in unity, significant transmission latencies occur. For a 32-neuron system, transmitting 50 bytes of data per spike and with an average neuron firing rate of 8.93 spikes/s, the average maximum delay was approximately 3.2 s. It is not until the output bandwidth is four times the average input bandwidth that average maximum delays are reduced to less than 10 ms. A comparison of neuron firing rate and resulting latencies shows that high latencies result from neuron bursting. These results will impact the design of transcutaneous telemetry in a BMI. Index Terms--Brain-machine interface (BMI), data compression, neural prosthesis, neural spike sorting, neural spike trains, real-time systems, telemetry.

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 [+ or -] 3[micro]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.