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During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability-plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning--preasymptotic and postasymptotic--because once the error drops to a level comparable to that of the noise-induced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed--memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently.

Neurons in the primary motor cortex (M1) have been shown to have persistent, memory-like activity following adaptation to altered movement dynamics. However, the techniques used to study these memory traces limited recordings to only single sessions lasting no more than a few hours. Here, chronically implanted microelectrode arrays were used to study the long-term neuronal responses to repeated experience with perturbing, velocity-dependent force fields. Force-field-related neuronal activity within each session was similar to that found previously. That is, the directional tuning curves of the M1 neurons shifted in a manner appropriate to compensate for the forces. Next, the across-session behavior was examined. Long-term learning was evident in the performance improvements across multiple force-field sessions. Correlated with this change, the neuronal population had smaller within-session spike rate changes as experience with the force field increased. The smaller within-session changes were a result of persistent across-session shifts in directional tuning. The results extend the observation of memory traces of newly learned dynamics and provide further evidence for the role of M1 in early motor memory formation.

Professional athletes involved in sports that require the execution of fine motor skills must practice for a considerable length of time before competing in an event. Why is such practice necessary? Is it merely to warm-up the muscles, tendons, and ligaments, or does the athlete's sensorimotor network need to be constantly recalibrated? In this article, the authors present a point of view in which the human sensorimotor system is characterized by: (a) a high noise level and (b) a high learning rate at the synaptic level (which, because of the noise, does not equate to a high learning rate at the behavioral level). They argue that many heuristics of human skill learning, including the need for a prolonged period of warm-up in experts, follow from these assumptions.

In grasping, the CNS controls a particularly large number of degrees of freedom. We tested the idea that this control is facilitated by the presence of muscle synergies. According to the strong version of this concept, these synergies are invariant, hard-wired patterns of activation across muscles. Synergies may serve as modules that linearly sum, each with specific amplitude and timing coefficients, to generate a large array of muscle patterns. We tested two predictions of the synergy model. A small number of synergies should (1) account for a large fraction of variation in muscle activity, and (2) be modulated in their recruitment by task variables, even in novel behavioral contexts. We also examined whether the synergies would (3) have broadly similar structures across animals. We recorded from 15 to 19 electrodes implanted in forelimb muscles of two rhesus macaques as they grasped and transported 25 objects of variable shape and size. We show that three synergies accounted for 81% of the electromyographic data variation in each monkey. Each synergy was modulated in its recruitment strength and/or timing by object shape and/or size. Even when synergies were extracted from a small subset of object shape and size conditions and then used to reconstruct the entire dataset, we observed highly similar synergies and patterns of modulation. The synergies were well conserved between monkeys, with two of the synergies exceeding chance structural similarity, and the third being recruited, in both animals, in proportion to the size of the object handled.

Sensorimotor adaptation and sequence learning have often been treated as distinct forms of motor learning. But frequently the motor system must acquire both types of experience simultaneously. Here, we investigated the interaction of these two forms of motor learning by having subjects adapt to predictable forces imposed by a robotic manipulandum while simultaneously reaching to an implicit sequence of targets. We show that adaptation to novel dynamics and learning of a sequence of movements can occur simultaneously and without significant interference or facilitation. When both conditions were presented simultaneously to subjects, their trajectory error and reaction time decreased to the same extent as those of subjects who experienced the force field or sequence independently.

Although multiple lines of evidence implicate the primary motor cortex (M1) in motor learning, the precise role of M1 in the adaptation to novel movement dynamics and in the subsequent consolidation of a memory of those dynamics remains unclear. Here we used repetitive transcranial magnetic stimulation (rTMS) to dissociate the contribution of M1 to these distinct aspects of motor learning. Subjects performed reaching movements in velocity-dependent force fields over three epochs: a null-field baseline epoch, a clockwise-field learning epoch (15 min after the baseline epoch), and a clockwise-field retest epoch (24 h after the learning epoch). Half of the subjects received 15 min of 1 Hz rTMS to M1 between the baseline and learning epochs. Subjects given rTMS performed identically to control subjects during the learning epoch. However, control subjects performed with significantly less error than rTMS subjects in the retest epoch on the following day. These results suggest that M1 is not critical to the network supporting motor adaptation per se but that, within this network, M1 may be important for initiating the development of long-term motor memories.

Previous studies have suggested that the motor system may simplify control by combining a small number of muscle synergies represented as activation profiles across a set of muscles. The role of sensory feedback in the activation and organization of synergies has remained an open question. Here, we assess to what extent the motor system relies on centrally organized synergies activated by spinal and/or supraspinal commands to generate motor outputs by analyzing electromyographic (EMG) signals collected from 13 hindlimb muscles of the bullfrog during swimming and jumping, before and after deafferentation. We first established that, for both behaviors, the intact and deafferented data sets possess low and similar dimensionalities. Subsequently, we used a novel reformulation of the non-negative matrix factorization algorithm to simultaneously search for synergies shared by, and synergies specific to, the intact and deafferented data sets. Most muscle synergies were identified as shared synergies, suggesting that EMGs of locomotor behaviors are generated primarily by centrally organized synergies. Both the amplitude and temporal patterns of the activation coefficients of most shared synergies, however, were altered by deafferentation, suggesting that sensory inflow modulates activation of those centrally organized synergies. For most synergies, effects of deafferentation on the activation coefficients were not consistent across frogs, indicating substantial interanimal variability of feedback actions. We speculate that sensory feedback might adapt recruitment of muscle synergies to behavioral constraints, and the few synergies specific to the intact or deafferented states might represent afferent-specific modules or feedback reorganization of spinal neuronal networks.

The mechanical stability properties of hindlimb-hindlimb wiping movements of the spinalized frog were examined. One hindlimb, the wiping limb, was implanted with 12 electromyographic (EMG) electrodes and attached to a robot that both recorded its trajectory and applied brief force perturbations. Cutaneous electrical stimulation was applied to the other hindlimb, the target limb, to evoke the hindlimb-hindlimb wiping reflex. Kinematic and EMG data from both unperturbed trials and trials in which a phasic perturbation was applied were collected from each spinalized frog. In the perturbed behaviors, we found that the initially large displacement attributable to the perturbation was compensated such that the final position was statistically indistinguishable from the unperturbed final position in all of the frogs, thus indicating the dynamic stability of these movements. This stability was robust to the range of perturbation amplitudes and nominal kinematic variation observed in this study. In addition, we investigated the extent to which intrinsic viscoelastic properties of the limb and proprioceptive feedback play a role in stabilizing the movements. No significant changes were seen in the EMGs after the perturbation. Furthermore, deafferentation of the wiping limb did not significantly affect the stability of the wiping reflex. Thus, we found that the intrinsic viscoelastic properties of the hindlimb conferred robust stability properties to the hindlimb-hindlimb wiping behavior. This stability mechanism may simplify the control required by the frog spinal motor systems to produce successful movements in an unpredictable and varying environment.

To execute visually guided reaching movements, the central nervous system (CNS) must transform a desired hand trajectory (kinematics) into appropriate muscle-related commands (dynamics). It has been suggested that the CNS might face this challenging computation by using internal forward models for the dynamics. Previous work in humans found that new internal models can be acquired through experience. In a series of studies in monkeys, we investigated how neurons in the motor areas of the frontal lobe reflect the movement dynamics and how their activity changes when monkeys learn a new internal model. Here we describe the results for the supplementary motor area (SMA-proper, or SMA). In the experiments, monkeys executed visually guided reaching movements and adapted to an external perturbing force field. The experimental design allowed dissociating the neuronal activity related to movement dynamics from that related to movement kinematics. It also allowed dissociating the changes related to motor learning from the activity related to motor performance (kinematics and dynamics). We show that neurons in SMA reflect the movement dynamics individually and as a population, and that their activity undergoes a variety of plastic changes when monkeys adapt to a new dynamic environment.

It is widely acknowledged that movements are planned at the level of the kinematics. However, the central nervous system must ultimately transform kinematic plans into dynamics-related commands. How, when, and where the kinematics-to-dynamics (KD) transformation is processed represent fundamental and unanswered questions. We recorded from the supplementary motor area (SMA) of two monkeys as they executed visually instructed reaching movements. We specifically analyzed a delay period following the instruction but prior to the go signal (motor planning). During the delay, a group of neurons in the SMA progressively came to reflect the dynamics rather than the desired kinematics of the upcoming movement. This finding suggests that some neurons in the SMA participate in the KD transformation.

The primary motor cortex (M1) is known to control motor performance. Recent findings have also implicated M1 in motor learning, as neurons in this area show learning-related plasticity. In the present study, we analyzed the neuronal activity recorded in M1 in a force field adaptation task. Our goal was to investigate the neuronal reorganization across behavioral epochs (before, during, and after adaptation). Here we report two main findings. First, memory cells were present in two classes. With respect to the changes of preferred direction (Pd), these two classes complemented each other after readaptation. Second, for the entire neuronal population, the shift of Pd matched the shift observed for muscles. These results provide a framework whereby the activity of distinct neuronal subpopulations combines to subserve both functions of motor performance and motor learning.

We investigated how human subjects adapt to forces perturbing the motion of their ams. We found that this kind of learning is based on the capacity of the central nervous system (CNS) to predict and therefore to cancel externally applied perturbing forces. Our experimental results indicate: (i) that the ability of the CNS to compensate for the perturbing forces is restricted to those spatial locations where the perturbations have been experienced by the moving arm. The subjects also are able to compensate for forces experienced at neighboring workspace locations. However, adaptation decays smoothly and quickly with distance from the locations where disturbances had been sensed by the moving limb. (ii) Our experiments also how that the CNS builds an internal model of the external perturbing forces in intrinsic (muscles and / or joints) coordinates.

Single-unit recordings from the pontine reticular formation in four monkeys have shown the presence of two classes of short-lead burst neurons firing during coordinated eye-head movements. The activity of one class showed a correlation with the size of saccadic movements performed during head movements; the other showed a correlation between firing pattern and the combined eye-head movement. Anatomical reconstructions of the recording sites point to an intermingling of the two cell types.

1. Spinal frogs are known to make coordinated and successful wiping movements to almost all places on the body and legs. Such wiping movements involve a sensorimotor transformation. Information from both the spatial locations of stimuli on the skin and the body configuration of the frog is transformed into a set of motor commands that generate body movements adequate to successfully remove the irritant. The spinal cord itself therefore has a limited capacity for sensorimotor transformations. 2. We examined the kinematics of wiping motions in both spinal and intact leopard frogs and bullfrogs. This data was used to assess the flexibility, precision, and strategy of the kinematic sensorimotor transformations used during wiping. The movements involved the use of redundant degrees of freedom in the limbs. Thus many possible movements or solutions could generate successful wiping. This redundancy allows motor-equivalent movements to be used by the frog. 3. Movements were examined in two dimensions by the use of VHS shuttered-video recording and in three dimensions with the use of a WATSMART system of infrared diodes and cameras. The kinematic analysis was applied to those motions in which the limbs did not interact with kinematic constraints, such as the surface of the substrate or body. These unconstrained motions are directly related to motor commands and thus more easily interpreted. 4. Wiping movements to the back were retained in essentially the same form in both spinal and intact frogs. In both cases wiping had four phases with a fifth occasionally present. The phases included flexion, placing, aiming, and whisking, with occasional extension and multiply repeated wipes. However, the aiming phase was often very brief or absent in this data, and flexion was sometimes omitted in multiple wipes. We found that the placing posture was adjusted in a simple way in response to variations in the location of the target stimulus. The rostrocaudal position of the foot tip was strongly and linearly related to the rostrocaudal stimulus location. 5. During the placing posture, joint angles as well as the limb tip in back wipes had linear relationships to the stimulus' rostrocaudal coordinate. The limb configuration used by the frog allowed a strategy of linear (and potentially independent) postural adjustment of joint angle to stimulus position to generate almost linear endpoint adjustments in the placing phase of wiping. This solution to the ill-posed problem of choosing a joint angle for the placing posture in back-wiping may be computationally simple.(ABSTRACT TRUNCATED AT 400 WORDS)