Learning leaves a memory trace in motor cortex

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.

Learning is shaped by abrupt changes in neural engagement

Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.

Intracortical Microstimulation Feedback Improves Grasp Force Accuracy in a Human Using a Brain-Computer Interface

After a spinal cord injury, a person may grasp objects using a brain-computer interface (BCI) to control a robot arm. However, most BCIs do not restore somatosensory percepts that would enable someone to sense grasp force. Intracortical microstimulation (ICMS) in the somatosensory cortex can evoke tactile sensations and may therefore offer a viable solution to provide grasp force feedback. We investigated whether a bidirectional BCI could improve grasp force control over a BCI using only visual feedback. When evaluating the error of the applied force during a force matching task, we found that ICMS feedback improved overall applied grasp force accuracy.

The Motor Cortex Has Independent Representations for Ipsilateral and Contralateral Arm Movements But Correlated Representations for Grasping

Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms.

The critical stability task: quantifying sensory-motor control during ongoing movement in nonhuman primates

Everyday behaviors require that we interact with the environment, using sensory information in an ongoing manner to guide our actions. Yet, by design, many of the tasks used in primate neurophysiology laboratories can be performed with limited sensory guidance. As a consequence, our knowledge about the neural mechanisms of motor control is largely limited to the feedforward aspects of the motor command. To study the feedback aspects of volitional motor control, we adapted the critical stability task (CST) from the human performance literature (Jex H, McDonnell J, Phatak A. IEEE Trans Hum Factors Electron 7: 138-145, 1966). In the CST, our monkey subjects interact with an inherently unstable (i.e., divergent) virtual system and must generate sensory-guided actions to stabilize it about an equilibrium point. The difficulty of the CST is determined by a single parameter, which allows us to quantitatively establish the limits of performance in the task for different sensory feedback conditions. Two monkeys learned to perform the CST with visual or vibrotactile feedback. Performance was better under visual feedback, as expected, but both monkeys were able to utilize vibrotactile feedback alone to successfully perform the CST. We also observed changes in behavioral strategy as the task became more challenging. The CST will have value for basic science investigations of the neural basis of sensory-motor integration during ongoing actions, and it may also provide value for the design and testing of bidirectional brain computer interface systems. NEW & NOTEWORTHY Currently, most behavioral tasks used in motor neurophysiology studies require primates to make short-duration, stereotyped movements that do not necessitate sensory feedback. To improve our understanding of sensorimotor integration, and to engineer meaningful artificial sensory feedback systems for brain-computer interfaces, it is crucial to have a task that requires sensory feedback for good control. The critical stability task demands that sensory information be used to guide long-duration movements.

Constraints on neural redundancy

Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.

When you swing a tennis racket, muscles in your arm contract in a specific sequence. For this to happen, millions of neurons in your brain and spinal cord must fire to make those muscles contract. If you swing the racket a second time, the same muscles in your arm will contract again. But the firing pattern of the underlying neurons will probably be different. This phenomenon, in which different patterns of neural activity generate the same outcome, is called neural redundancy.

Neural redundancy allows a set of neurons to perform multiple tasks at once. For example, the same neurons may drive an arm movement while simultaneously planning the next activity. But does performing a given task constrain how often different patterns of neural activity can be produced? If so, this would limit whether other tasks could be carried out at the same time. To address this, Hennig et al. trained macaque monkeys to use a brain-computer interface (BCI). This is a device that reads out electrical brain activity and converts it into signals that can be used to control another device. The key advantage of a BCI is that the redundant activity patterns are precisely known. The monkeys learned to use their brain activity, via the BCI, to move a cursor on a computer screen in different directions.

The results revealed that monkeys could only produce a limited number of different patterns of brain activity for a given BCI cursor movement. This suggests that the ability of a group of neurons to multitask is restricted. For example, if the same set of neurons is involved in both planning and performing movements, then an animal’s ability to plan a future movement will depend on the one it is currently performing.

BCIs can help patients who have suffered stroke or paralysis. They enable patients to use their brain activity to control a computer or even robotic limbs. Understanding how the brain controls BCIs will help us improve their performance and deepen our knowledge of how the brain plans and performs movements. This might include designing BCIs that allow users to multitask more effectively.

Learning by neural reassociation

Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of Reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.

Assessing vibrotactile feedback strategies by controlling a cursor with unstable dynamics

Brain computer interface (BCI) control predominately uses visual feedback. Real arm movements, however, are controlled under a diversity of feedback mechanisms. The lack of additional BCI feedback modalities forces users to maintain visual contact while performing tasks. Such stringent requirements result in poor BCI control during tasks that inherently lack visual feedback, such as grasping, or when visual attention is diverted. Using a modified version of the Critical Tracking Task which we call the Critical Stability Task (CST), we tested the ability of 9 human subjects to control an unstable system using either free arm movements or pinch force. The subjects were provided either visual feedback, 'proportional' vibrotactile feedback, or 'on-off' vibrotactile feedback about the state of the unstable system. We increased the difficulty of the control task by making the virtual system more unstable. We judged the effectiveness of a particular form of feedback as the maximal instability the system could reach before the subject lost control of it. We found three main results. First, subjects can use solely vibrotactile feedback to control an unstable system, although control was better using visual feedback. Second, 'proportional' vibrotactile feedback provided slightly better control than 'on-off' vibrotactile feedback. Third, there was large intra-subject variability in terms of the most effective input and feedback methods. This highlights the need to tailor the input and feedback methods to the subject when a high degree of control is desired. Our new task can provide a complement to traditional center-out paradigms to help boost the real-world relevance of BCI research in the lab.