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The brain has a remarkable ability to learn and adjust behavior. For instance, the brain can adjust muscle activation to cope with changes in the environment. However, the neuronal mechanisms behind this adaptation are not clear. To address this fundamental question, this study examines the neuronal basis of long-term sensorimotor learning by recording neuronal activity in the primary motor cortex of monkeys during a long-term adaptation to a force-field perturbation. For 5 consecutive days, the same perturbation was applied to the monkey's hand when reaching to a single target, whereas movements to all other targets were not perturbed. The gradual improvement in performance over these 5 days was correlated to the evolvement in the population neuronal signal, with two timescales of changes in single-cell activity. Specifically, one subgroup of cells showed a relatively fast increase in activity, whereas the other showed a gradual, slower decrease. These adapted patterns of neuronal activity did not involve changes in directional tuning of single cells, suggesting that adaptation was the result of adjustments of the required motor plan by a population of neurons rather than changes in single-cell properties. Furthermore, generalization was mostly expressed in the direction of the required compensatory force during adaptation. Altogether, the neuronal activity and its generalization accord with the adapted motor plan.

Previous studies support the notion that sensorimotor learning involves multiple processes. We investigated the neuronal basis of these processes by recording single-unit activity in motor cortex of non-human primates (Macaca fascicularis), during adaptation to force-field perturbations. Perturbed trials (reaching to one direction) were practiced along with unperturbed trials (to other directions). The number of perturbed trials relative to the unperturbed ones was either low or high, in two separate practice schedules. Unsurprisingly, practice under high-rate resulted in faster learning with more pronounced generalization, as compared to the low-rate practice. However, generalization and retention of behavioral and neuronal effects following practice in high-rate were less stable; namely, the faster learning was forgotten faster. We examined two subgroups of cells and showed that, during learning, the changes in firing-rate in one subgroup depended on the number of practiced trials, but not on time. In contrast, changes in the second subgroup depended on time and practice; the changes in firing-rate, following the same number of perturbed trials, were larger under high-rate than low-rate learning. After learning, the neuronal changes gradually decayed. In the first subgroup, the decay pace did not depend on the practice rate, whereas in the second subgroup, the decay pace was greater following high-rate practice. This group shows neuronal representation that mirrors the behavioral performance, evolving faster but also decaying faster at learning under high-rate, as compared to low-rate. The results suggest that the stability of a new learned skill and its neuronal representation are affected by the acquisition schedule.

Sensory-motor learning is commonly considered as a mapping process, whereby sensory information is transformed into the motor commands that drive actions. However, this directional mapping, from inputs to outputs, is part of a loop; sensory stimuli cause actions and vice versa. Here, we explore whether actions affect the understanding of the sensory input that they cause. Using a visuo-motor task in humans, we demonstrate two types of learning-related behavioral effects. Stimulus-dependent effects reflect stimulus-response learning, while action-dependent effects reflect a distinct learning component, allowing the brain to predict the forthcoming sensory outcome of actions. Together, the stimulus-dependent and the action-dependent learning components allow the brain to construct a complete internal representation of the sensory-motor loop.

Learning motor skills entails adaptation of neural computations that can generate or modify associations between sensations and actions. Indeed, humans can use different strategies when adapting to dynamic loads depending on available sensory feedback. Here, we examined how neural activity in motor cortex was modified when monkeys made arm reaches to a visual target and locally adapted to curl force field with or without visual trajectory feedback. We found that firing rates of a large subpopulation of cells were consistently modulated depending on the distance of their preferred direction from the learned movement direction. The newly acquired activity followed a cosine-like function, with maximal increase in directions that opposed the perturbing force and decrease in opposite directions. As a result, the combined neuronal activity generated an adapted population vector. The results suggest that this could be achieved without changing the tuning properties of the cells. This population directional signal was however altered in the absence of visual feedback; while the cosine pattern of modulation was maintained, the population distributions of modulated cells differed across feedback consistent with the different trajectory shapes. Finally, we predicted generalization patterns of force-field learning based on the cosine-like modulation. These conformed to reported features of generalization in humans, suggesting that the generalization function was related to the observed rate modulations in the motor cortex. Overall, the findings suggest that the new combined activation of neuronal ensembles could underlie the change in the internal model of movement dynamics in a way that depends on available sensory feedback and chosen strategy.

Neurons in all brain areas exhibit variability in their spiking activity. Although part of this variability can be considered as noise that is detrimental to information processing, recent findings indicate that variability can also be beneficial. In particular, it was suggested that variability in the motor system allows for exploration of possible motor states and therefore can facilitate learning and adaptation to new environments. Here, we provide evidence to support this idea by analyzing the variability of neurons in the primary motor cortex (M1) and in the supplementary motor area (SMA-proper) of monkeys adapting to new rotational visuomotor tasks. We found that trial-to-trial variability increased during learning and exhibited four main characteristics: (1) modulation occurred preferentially during a delay period when the target of movement was already known, but before movement onset; (2) variability returned to its initial levels toward the end of learning; (3) the increase in variability was more apparent in cells with preferred movement directions close to those experienced during learning; and (4) the increase in variability emerged at early phases of learning in the SMA, whereas in M1 behavior reached plateau levels of performance. These results are highly consistent with previous findings that showed similar trends in variability across a population of neurons. Together, the results strengthen the idea that single-cell variability can be much more than mere noise and may be an integral part of the underlying mechanism of sensorimotor learning.

The use of sensorimotor adaptation and learning paradigms in psychophyical and electrophysiological experiments can help to shed light on two fundamental questions. First, what are the computations that control sensorimotor behavior and, second, what are the neuronal mechanisms and representations underlying newly learned sensorimotor skills? We describe experiments that combined behavioral and electrophysioloigcal techniques and discuss implication of the results to three main questions: How do neuronal primitives of representation affect performance and learning? Do pre-motor and primary motor cortices form a hierarchy of computation, with different roles during learning and motor performance? How do these different cortical areas and the representations of movement change during the different stages of learning and memory formation?

The neural basis of the internal models used in sensorimotor transformations is beginning to be uncovered. Sensorimotor learning involves the modification of such models. Different stages of sensory-motor processing have been explored with a continuum of experimental tasks, from learning arbitrary associations of sensory cues to movements, to adapting to altered kinematic and dynamic environments. Several groups have been studying changes in neuronal activity in cortical and subcortical areas that may be related to the acquisition and consolidation processes. We discuss the progress and challenges in understanding how these learning-related neural changes are involved in the modification of internal models, and offer future directions.

It is widely accepted that learning first involves generating new memories and then consolidating them into long-term memory. Thus learning is generally viewed as a single continuous process with two sequential stages; acquisition and consolidation. Here, we tested an alternative hypothesis proposing that acquisition and consolidation take place, at least partly, in parallel. Human subjects learned two visuomotor tasks. One task required moving a cursor under visuomotor rotation and the other required arbitrary association of colour to direction of movement. Subjects learned the two tasks in sequence, and were tested for acquisition of the second immediately after learning the first, and for retention of the first on the following day. The results show that learning one task led to proactive interference to acquisition of the second. However, this interference was not accompanied by retroactive interference to consolidation of the first task, indicating that acquisition and consolidation can be uncoupled.

The kinematics of straight reaching movements can be specified vectorially by the direction of the movement and its extent. To explore the representation in the brain of these two properties, psychophysical studies have examined learning of visuomotor transformations of either rotation or gain and their generalization. However, the neuronal substrates of such complex learning are only beginning to be addressed. As an initial step in ensuring the validity of such investigations, it must be shown that monkeys indeed learn and generalize visuomotor transformations in the same manner as humans. Here, we analyze trajectories and velocities of movements as monkeys adapt to either rotational or gain transformations. We used rotations with different signs and magnitudes, and gains with different signs, and analyzed transfer of learning to untrained movements. The results show that monkeys can adapt to both types of transformation with a time course that resembles human learning. Analysis of the aftereffects reveals that rotation is learned locally and generalizes poorly to untrained directions, whereas gain is learned more globally and can be transferred to other amplitudes. The results lend additional support to the hypothesis that reaching movements are learned locally but can be easily rescaled to other magnitudes by scaling the peak velocity. The findings also indicate that reaching movements in monkeys are planned and executed very similarly to those in humans. This validates the underlying presumption that neuronal recordings in primates can help elucidate the mechanisms of motor learning in particular and motor planning in general.

Human studies show that the learning of a new sensorimotor mapping that requires adaptation to directional errors is local and generalizes poorly to untrained directions. We trained monkeys to learn new visuomotor rotations for only one target in space and recorded neuronal activity in the primary motor cortex before, during and after learning. Similar to humans, the monkeys showed poor transfer of learning to other directions, as observed by behavioral aftereffects for untrained directions. To test for internal representations underlying these changes, we compared two features of neuronal activity before and after learning: changes in firing rates and changes in information content. Specific elevations of firing rate were only observed in a subpopulation of cells in the motor cortex with directional properties corresponding to the locally learned rotation; namely cells only showed plasticity if their preferred direction was near the training one. We applied measures from information theory to probe for learning-related changes in the neuronal code. Single cells conveyed more information about the direction of movement and this specific improvement in encoding was correlated with an increase in the slope of the neurons' tuning curve. Further, the improved information after learning enabled a more accurate reconstruction of movement direction from neuronal populations. Our findings suggest a neural mechanism for the confined generalization of a newly acquired internal model by showing a tight relationship between the locality of learning and the properties of neurons. They also provide direct evidence for improvement in the neural code as a result of learning.

Many recent studies describe learning-related changes in sensory and motor areas, but few have directly probed for improvement in neuronal coding after learning. We used information theory to analyze single-cell activity from the primary motor cortex of monkeys, before and after learning a local rotational visuomotor task. We show that after learning, neurons in the primary motor cortex conveyed more information about the direction of movement and did so with relation to their directional sensitivity. Similar to recent findings in sensory systems, this specific improvement in encoding is correlated with an increase in the slope of the neurons' tuning curve. We further demonstrate that the improved information after learning enables a more accurate reconstruction of movement direction from neuronal populations. Our results suggest that similar mechanisms govern learning in sensory and motor areas and provide further evidence for a tight relationship between the locality of learning and the properties of neurons; namely, cells only show plasticity if their preferred direction is near the training one. The results also suggest that simple learning tasks can enhance the performance of brain-machine interfaces., Competing Interests: The authors have declared that no conflicts of interest exist.

In humans, learning to produce correct visually guided movements to adapt to new sensorimotor conditions requires the formation of an internal model that represents the new transformation between visual input and the required motor command. When the new environment requires adaptation to directional errors, learning generalizes poorly to untrained locations and directions, indicating that such learning is local. Here we replicated these behavioral findings in rhesus monkeys using a visuomotor rotation task and simultaneously recorded neuronal activity. Specific changes in activity were observed only in a subpopulation of cells in the motor cortex with directional properties corresponding to the locally learned rotation. These changes adhered to the dynamics of behavior during learning and persisted between learning and relearning of the same rotation. These findings suggest a neural mechanism for the locality of newly acquired sensorimotor tasks and provide electrophysiological evidence for their retention in working memory.