Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control

How muscle synergies fail to solve the muscle redundancy problem during human reaching

The production of movement involves integrating biomechanical, neural, and environmental factors. The biomechanics is complex enough that neural sensorimotor circuits must embed its dynamics for efficient and robust control. However, a problem of redundancy exists, i.e., the problem of choosing among multiple muscles and combinations of joint angles that are possible for a given desired hand position or motion. This problem may be resolved by reducing the dimensionality of the space of motor commands by the central nervous system, i.e., through muscle synergies or motor primitives. Other studies have obtained muscle synergies using decomposition methods. However, we posit that it is not sufficient to show the existence of a low dimensional space, one needs to demonstrate the utility of the obtained synergies in controlling movement. Here we defined a muscle synergy as a single control signal producing specific force direction. We then tested a hypothesis that such muscle synergies exist using dimensionality reduction method. Our approach takes advantage of the close relationship between the temporal profiles of muscle activity observed with electromyography and the joint moments they produce during reaching derived from motion capture. We recorded electromyography of 12 muscles and the kinematics of both arms in 14 right-handed participants performing reaching movements in multiple directions from different starting positions. We used principal component analysis to evaluate the contribution of individual muscles to supporting the arm against gravity and producing propulsive forces. Results show that the joint torques in specific directions (flexion or extension) required to move toward a target were not produced by consistent muscle groups in most conditions as would be expected from the muscle synergy definition outlined above. We further show that both agonistic and antagonistic muscles coactivate in flexible muscle groups but to a different extent between the dominant and non-dominant arms. Our main findings indicate that the nervous system solves the problem of choosing which muscles to activate and when by taking into account limb dynamics rather than reducing the dimensionality through muscle synergies. Furthermore, our data supports the idea of two neural controllers that target different muscle groups in the arm and hand for gross postural and fine goal-directed control of reaching.

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

Rapid Online Corrections for Proprioceptive and Visual Perturbations Recruit Similar Circuits in Primary Motor Cortex

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioral goal. The primary motor cortex (M1) is a key region involved in processing feedback for rapid motor corrections, yet we know little about how M1 circuits are recruited by different sources of sensory feedback to make rapid corrections. We trained two male monkeys (Macaca mulatta) to make goal-directed reaches and on random trials introduced different sensory errors by either jumping the visual location of the goal (goal jump), jumping the visual location of the hand (cursor jump), or applying a mechanical load to displace the hand (proprioceptive feedback). Sensory perturbations evoked a broad response in M1 with ∼73% of neurons (n = 257) responding to at least one of the sensory perturbations. Feedback responses were also similar as response ranges between the goal and cursor jumps were highly correlated (range of r = [0.91, 0.97]) as were the response ranges between the mechanical loads and the visual perturbations (range of r = [0.68, 0.86]). Lastly, we identified the neural subspace each perturbation response resided in and found a strong overlap between the two visual perturbations (range of overlap index, 0.73-0.89) and between the mechanical loads and visual perturbations (range of overlap index, 0.36-0.47) indicating each perturbation evoked similar structure of activity at the population level. Collectively, our results indicate rapid responses to errors from different sensory sources target similar overlapping circuits in M1.

A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems

Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community.

A cerebellar-based solution to the nondeterministic time delay problem in robotic control

[Figure: see text].

On Robot Compliance: A Cerebellar Control Approach

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

Direct and indirect effects of cathodal cerebellar TDCS on visuomotor adaptation of hand and arm movements

Adaptation of movements involving the proximal and distal upper-limb can be differentially facilitated by anodal transcranial direct current stimulation (TDCS) over the cerebellum and primary motor cortex (M1). Here, we build on this evidence by demonstrating that cathodal TDCS impairs motor adaptation with a differentiation of the proximal and distal upper-limbs, relative to the site of stimulation. Healthy young adults received M1 or cerebellar cathodal TDCS while making fast 'shooting' movements towards targets under 60° rotated visual feedback conditions, using either whole-arm reaching or fine hand and finger movements. As predicted, we found that cathodal cerebellar TDCS resulted in impairment of adaptation of movements with the whole arm compared to M1 and sham groups, which proved significantly different during late adaptation. However, cathodal cerebellar TDCS also significantly enhanced adaptation of hand movements, which may reflect changes in the excitability of the pathway between the cerebellum and M1. We found no evidence for change of adaptation rates using arm or finger movements following cathodal TDCS directly over M1. These results are further evidence to support movement specific effects of TDCS, and highlight how the connectivity and functional organisation of the cerebellum and M1 must be considered when designing TDCS-based therapies.

Targeted tDCS selectively improves motor adaptation with the proximal and distal upper limb

BACKGROUND

The cerebellum and primary motor cortex (M1) are crucial to coordinated and accurate movements of the upper limbs. There is also appreciable evidence that these two structures exert somewhat divergent influences upon proximal versus distal upper limb control. Here, we aimed to differentially regulate the contribution of the cerebellum and M1 to proximal and distal effectors during motor adaptation, with transcranial direct current stimulation (tDCS). For this, we employed tasks that promote similar motor demands, but isolate whole arm from hand/finger movements, in order to functionally segregate the hierarchy of upper limb control.

METHODS

Both young and older adults took part in a visuomotor rotation task; where they adapted to a 60° visuomotor rotation using either a hand-held joystick (requiring finger/hand movements) or a 2D robotic manipulandum (requiring whole-arm reaching movements), while M1, cerebellar or sham tDCS was applied.

RESULTS

We found that cerebellar stimulation improved adaptation performance when arm movements were required to complete the task, while in contrast stimulation of M1 enhanced adaptation during hand and finger movements only. This double-dissociation was replicated in an independent group of older adults, demonstrating that the behaviour remains intact in ageing.

CONCLUSIONS

These results suggest that stimulation of distinct motor areas can selectively improve motor adaptation in the proximal and distal upper limb. This also highlights new ways in which tDCS might be best applied to achieve reliable rehabilitation of upper limb motor deficits.

Effects of the amount of practice and time interval between practice sessions on the retention of internal models

The amount of practice and time interval between practice sessions are important factors that influence motor learning efficiency. Here, we aimed to reveal the relationship between the retention and consolidation of a new internal model, and the amount of practice and time interval between practice sessions. We employed a visuomotor rotation tracking task to test the hypotheses that (1) a new internal model consolidates owing to extensive practice after reaching a task performance plateau and (2) a longer time interval between practice sessions makes it difficult to activate a new internal model. The participants were assigned to one of the four groups that differed in terms of the amount of practice and the time interval between practice sessions. They performed a tracking task in which they experienced 120° clockwise visuomotor rotation and were required to track a moving target on a computer display using a mouse cursor. To evaluate the retention and consolidation of a new internal model, we calculated the aftereffects and savings as measures of motor learning. To the best our knowledge, this is the first study to manipulate both the amount of practice and the time interval between practice sessions simultaneously in one experiment using a visuomotor tracking task. Our results support the previously reported idea that extensive practice is necessary for the consolidation of a new internal model.

Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation

Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation.

Cerebellar Purkinje cells regulate accurate eye movement coordination. However, it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic, high frequency bursting, and post-complex spike pauses. We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation. A biophysical model of Purkinje cell is at the core of a spiking network model, which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites. We show that Purkinje spike burst-pause dynamics are critical for (1) gating the vestibular-motor response association during VOR acquisition; (2) mediating the LTD/LTP balance for VOR consolidation; (3) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping.

A computational scheme for internal models not requiring precise system parameters

Utilization by humans of a precise and adaptable internal model of the dynamics of the body in generating movements is a well-supported concept. The prevailing opinion is that such an internal model ceaselessly develops through long-term repetition and accumulation in the central nervous system (CNS). However, a long-term learning process would not be absolutely necessary for the formation of internal models. It is possible to estimate the dynamics of the system by using a motor command and its resulting output, instead of constructing a model of the dynamics with precise parameters. In this study, a computational model is proposed that uses a motor command and its corresponding output to estimate the dynamics of the system and it is examined whether the proposed model is capable of describing a series of empirical movements. The proposed model was found to be capable of describing humans' fast movements which require compensation for system dynamics as well as sensory delays. In addition, the proposed model shows equifinality under inertial perturbations as seen in several experimental studies. This satisfactory reproducibility of the proposed computation raises the possibility that humans make a movement by estimating the system dynamics with a copy of motor command and sensory output on a momentary basis, without the need to identify precise system parameters.

A muscle-activity-dependent gain between motor cortex and EMG

Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions. However, a model with a gain parameter that increased with the target force remained accurate across forces and dynamical loads. Surprisingly, this model showed that a greater proportion of EMG changes were explained by the nonlinear gain than the linear mapping from M1. In addition to its theoretical implications, the strength of this nonlinearity has important implications for brain-computer interfaces (BCIs). If BCI decoders are to be used to control movement dynamics (including interaction forces) directly, they will need to be nonlinear and include training data from broad data sets to function effectively across tasks. Our study reinforces the need to investigate neural control of movement across a wide range of conditions to understand its basic characteristics as well as translational implications. NEW & NOTEWORTHY We explored the motor cortex-to-electromyogram (EMG) mapping across a wide range of forces and loading conditions, which we found to be highly nonlinear. A greater proportion of EMG was explained by a nonlinear gain than a linear mapping. This nonlinearity allows motor cortex to control the wide range of forces encountered in the real world. These results unify earlier observations and inform the next-generation brain-computer interfaces that will control movement dynamics and interaction forces.

Comparison of Control Algorithms Using a Generalized Model for a Human with an Exoskeleton

This article presents a pictorial representation of a generalized model for a human interacting with an exoskeleton or a force-augmenting device. This model is used for comparing four different control schemes, which are: 1) Kazerooni's algorithm, 2) BLEEX’s algorithm, 3) technique inspired by fictitious gain, and 4) Force control with velocity and position feedback. The hardware and software requirements for the presented control algorithms are also discussed.

A system-level mathematical model of Basal Ganglia motor-circuit for kinematic planning of arm movements

In this paper, a novel system-level mathematical model of the Basal Ganglia (BG) for kinematic planning, is proposed. An arm composed of several segments presents a geometric redundancy. Thus, selecting one trajectory among an infinite number of possible ones requires overcoming redundancy, according to some kinds of optimization. Solving this optimization is assumed to be the function of BG in planning. In the proposed model, first, a mathematical solution of kinematic planning is proposed for movements of a redundant arm in a plane, based on minimizing energy consumption. Next, the function of each part in the model is interpreted as a possible role of a nucleus of BG. Since the kinematic variables are considered as vectors, the proposed model is presented based on the vector calculus. This vector model predicts different neuronal populations in BG which is in accordance with some recent experimental studies. According to the proposed model, the function of the direct pathway is to calculate the necessary rotation of each joint, and the function of the indirect pathway is to control each joint rotation considering the movement of the other joints. In the proposed model, the local feedback loop between Subthalamic Nucleus and Globus Pallidus externus is interpreted as a local memory to store the previous amounts of movements of the other joints, which are utilized by the indirect pathway. In this model, activities of dopaminergic neurons would encode, at short-term, the error between the desired and actual positions of the end-effector. The short-term modulating effect of dopamine on Striatum is also modeled as cross product. The model is simulated to generate the commands of a redundant manipulator. The performance of the model is studied for different reaching movements between 8 points in a plane. Finally, some symptoms of Parkinson's disease such as bradykinesia and akinesia are simulated by modifying the model parameters, inspired by the dopamine depletion.

Analysis of the pen pressure and grip force signal during basic drawing tasks: The timing and speed changes impact drawing characteristics

Writing is a complex fine and trained motor skill, involving complex biomechanical and cognitive processes. In this paper, we propose the study of writing kinetics using three angles: the pen-tip normal force, the total grip force signal and eventually writing quality assessment. In order to collect writing kinetics data, we designed a sensor collecting these characteristics simultaneously. Ten healthy right-handed adults were recruited and were asked to perform four tasks: first, they were instructed to draw circles at a speed they considered comfortable; they then were instructed to draw circles at a speed they regarded as fast; afterwards, they repeated the comfortable task compelled to follow the rhythm of a metronome; and eventually they performed the fast task under the same timing constraints. Statistical differences between the tasks were computed, and while pen-tip normal force and total grip force signal were not impacted by the changes introduced in each task, writing quality features were affected by both the speed changes and timing constraint changes. This verifies the already-studied speed-accuracy trade-off and suggest the existence of a timing constraints-accuracy trade-off.

Dysfunctions of the basal ganglia-cerebellar-thalamo-cortical system produce motor tics in Tourette syndrome

Motor tics are a cardinal feature of Tourette syndrome and are traditionally associated with an excess of striatal dopamine in the basal ganglia. Recent evidence increasingly supports a more articulated view where cerebellum and cortex, working closely in concert with basal ganglia, are also involved in tic production. Building on such evidence, this article proposes a computational model of the basal ganglia-cerebellar-thalamo-cortical system to study how motor tics are generated in Tourette syndrome. In particular, the model: (i) reproduces the main results of recent experiments about the involvement of the basal ganglia-cerebellar-thalamo-cortical system in tic generation; (ii) suggests an explanation of the system-level mechanisms underlying motor tic production: in this respect, the model predicts that the interplay between dopaminergic signal and cortical activity contributes to triggering the tic event and that the recently discovered basal ganglia-cerebellar anatomical pathway may support the involvement of the cerebellum in tic production; (iii) furnishes predictions on the amount of tics generated when striatal dopamine increases and when the cortex is externally stimulated. These predictions could be important in identifying new brain target areas for future therapies. Finally, the model represents the first computational attempt to study the role of the recently discovered basal ganglia-cerebellar anatomical links. Studying this non-cortex-mediated basal ganglia-cerebellar interaction could radically change our perspective about how these areas interact with each other and with the cortex. Overall, the model also shows the utility of casting Tourette syndrome within a system-level perspective rather than viewing it as related to the dysfunction of a single brain area.

Tourette syndrome is a neuropsychiatric disorder characterized by vocal and motor tics. Tics represent a cardinal symptom traditionally associated with a dysfunction of the basal ganglia leading to an excess of the dopamine neurotransmitter. This view gives a restricted clinical picture and limits therapeutic approaches because it ignores the influence of altered interactions between the basal ganglia and other brain areas. In this respect, recent evidence supports a more articulated framework where cerebellum and cortex are also involved in tic production. Building on these data, we propose a computational model of the basal ganglia-cerebellar-thalamo-cortical network to investigate the specific mechanisms underlying motor tic production. The model reproduces the results of recent experiments and suggests an explanation of the system-level processes underlying tic production. Moreover, it furnishes predictions related to the amount of tics generated when there are dysfunctions in the basal ganglia-cerebellar-thalamo-cortical circuits. These predictions could be important in identifying new brain target areas for future therapies based on a system-level view of Tourette syndrome.

Nonlinear mixed-effects model reveals a distinction between learning and performance in intensive reach training post-stroke

BACKGROUND

We recently showed that individuals with chronic stroke who completed two sessions of intensive unassisted arm reach training exhibited improvements in movement times up to one month post-training. Here, we study whether changes in movement times during training can predict long-term changes.

METHODS

Sixteen participants with chronic stroke and ten non-disabled age-matched participants performed two sessions of reach training with 600 movements per session. Movement time data during training were fitted to a nonlinear mixed-effects model consisting of a decreasing exponential term to model improvements of performance due to learning and an increasing linear term to model worsening of performance due to activity-dependent fatigability and/or other factors unrelated to learning.

RESULTS

For non-disabled age-matched participants, movement times gradually decreased overall during training and overall changes in movement times during training predicted long-term changes. In contrast, for participants post-stroke, movement times often worsened near the end of training. As a result, overall changes in movement times during training did not predict long-term changes in movement times in the stroke group. However, improvements in movement times due to training, as estimated by the exponential term of the model, predicted long-term changes in movement times.

CONCLUSION

Participants post-stroke showed a distinction between learning and performance in unassisted intensive arm reach training. Despite worsening of performance in later trials, extended training was beneficial for long-term gains.

Cerebellar Control of Balance and Locomotion

The cerebellum is important for movement control and plays a particularly crucial role in balance and locomotion. As such, one of the most characteristic signs of cerebellar damage is walking ataxia. It is not known how the cerebellum normally contributes to walking, although recent work suggests that it plays a role in the generation of appropriate patterns of limb movements, dynamic regulation of balance, and adaptation of posture and locomotion through practice. The purpose of this review is to examine mechanisms of cerebellar control of balance and locomotion, emphasizing studies of humans and other animals. Implications for rehabilitation are also considered.

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

Meta-analytic connectivity and behavioral parcellation of the human cerebellum

The cerebellum historically has been thought to mediate motor and sensory signals between the body and cerebral cortex, yet cerebellar lesions are also associated with altered cognitive behavioral performance. Neuroimaging evidence indicates that the cerebellum contributes to a wide range of cognitive, perceptual, and motor functions. Here, we used the BrainMap database to investigate whole-brainco-activation patterns between cerebellar structures and regions of the cerebral cortex, as well as associations with behavioral tasks. Hierarchical clustering was performed to meta-analytically identify cerebellar structures with similar cortical co-activation, and independently, with similar correlations to specific behavioral tasks. Strong correspondences were observed in these separate but parallel analyses of meta-analytic connectivity and behavioral metadata. We recovered differential zones of cerebellar co-activation that are reflected across the literature. Furthermore, the behaviors and tasks associated with the different cerebellar zones provide insight into the specialized function of the cerebellum, relating to high-order cognition, emotion, perception, interoception, and action. Taken together, these task-basedmeta-analytic results implicate distinct zones of the cerebellum as critically involved in the monitoring and mediation of psychological responses to internal and external stimuli.

Deep networks for motor control functions

The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a non-linear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control.

A cutaneous positioning system

Our previous work revealed that torso cutaneous information contributes to the internal representation of the torso and plays a role in postural control. Hence, the aims of this study were to assess whether posture could be manipulated by patterns of vibrotactile stimulation and to determine whether resulting modified postures were associated with specific and consistent spatial attitudes. Ten healthy young adults stood in normal and Romberg stances with six vibrating actuators positioned on the torso in contact with the skin over the anatomical locations corresponding to left and right external oblique, internal oblique and erector spinae muscles at the L4/L5 vertebrae level. A 250-Hz tactile vibration was applied for 5 s either at a single location or consecutively at each location in clockwise or counterclockwise sequences. Kinematic analysis of the body segments indicated that postural responses observed in response to single and sequential stimulation patterns were similar, while the center of pressure remained unaltered in any situations. Moreover, torso inclinations followed rectilinear-like path segments chartered by stimuli loci during sequential stimulations. Comparison of torso attitudes with previous results obtained with co-vibration patterns of the same duration showed that torso inclination amplitudes are equivalent for single (one location) and co-vibration (pairs of locations) patterns inducing the same directional effect. Hence, torso cutaneous information exhibits kinesthetic properties, appears to provide a map of upper body spatial configuration, and could assume the role of an internal positioning system for the upper body.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Testing models of cerebellar ataxia via dynamic simulation

Patients with damage to the cerebellum make reaching movements that are uncoordinated or “ataxic.” One prevailing hypothesis is that the cerebellum functions as an internal model for planning movements, and that damage to the cerebellum results in movements that do not properly account for arm dynamics. An exoskeleton robot was used to record multi-joint reaching movements. Subsequently, joint-torque trajectories were calculated and a gradient descent algorithm found optimal, patient-specific perturbations to actual limb dynamics predicted to reduce directional reaching errors by an average of 41%, elucidating a promising form of robotic intervention and adding support to the internal model hypothesis.

Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation

The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.

The organization of plasticity in the cerebellar cortex: from synapses to control

The cerebellum is thought to play a critical role in procedural learning, but the relationship between this function and the underlying cellular and synaptic mechanisms remains largely speculative. At present, at least nine forms of long-term synaptic and nonsynaptic plasticity (some of which are bidirectional) have been reported in the cerebellar cortex and deep cerebellar nuclei. These include long-term potentiation (LTP) and long-term depression at the mossy fiber-granule cell synapse, at the synapses formed by parallel fibers, climbing fibers, and molecular layer interneurons on Purkinje cells, and at the synapses formed by mossy fibers and Purkinje cells on deep cerebellar nuclear cells, as well as LTP of intrinsic excitability in granule cells, Purkinje cells, and deep cerebellar nuclear cells. It is suggested that the complex properties of cerebellar learning would emerge from the distribution of plasticity in the network and from its dynamic remodeling during the different phases of learning. Intrinsic and extrinsic factors may hold the key to explain how the different forms of plasticity cooperate to select specific transmission channels and to regulate the signal-to-noise ratio through the cerebellar cortex. These factors include regulation of neuronal excitation by local inhibitory networks, engagement of specific molecular mechanisms by spike bursts and theta-frequency oscillations, and gating by external neuromodulators. Therefore, a new and more complex view of cerebellar plasticity is emerging with respect to that predicted by the original "Motor Learning Theory," opening issues that will require experimental and computational testing.

Automatic online control of motor adjustments in reaching and grasping

Following the princeps investigations of Marc Jeannerod on action-perception, specifically, goal-directed movement, this review article addresses visual and non-visual processes involved in guiding the hand in reaching or grasping tasks. The contributions of different sources of correction of ongoing movements are considered; these include visual feedback of the hand, as well as the often-neglected but important spatial updating and sharpening of goal localization following gaze-saccade orientation. The existence of an automatic online process guiding limb trajectory toward its goal is highlighted by a series of princeps experiments of goal-directed pointing movements. We then review psychophysical, electrophysiological, neuroimaging and clinical studies that have explored the properties of these automatic corrective mechanisms and their neural bases, and established their generality. Finally, the functional significance of automatic corrective mechanisms-referred to as motor flexibility-and their potential use in rehabilitation are discussed.

Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, 1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario.

Cerebellar ataxia impairs modulation of arm stiffness during postural maintenance

Impedance control enables humans to effectively interact with their environment during postural and movement tasks, adjusting the mechanical behavior of their limbs to account for instability. Previous work has shown that people are able to selectively modulate the end-point stiffness of their arms, adjusting for varying directions of environmental disturbances. Behavioral studies also suggest that separate controllers are used for impedance modulation versus joint torque coordination. Here we tested whether people with cerebellar damage have deficits in impedance control. It is known that these individuals have poor motor coordination, which has typically been attributed to deficits in joint torque control. Subjects performed a static postural maintenance task with two different types of directional force perturbations. On average, patients with cerebellar ataxia modified stiffness differentially for the two perturbation conditions, although significantly less than age-matched control subjects. Thus cerebellar damage may impair the ability to modulate arm impedance. Surprisingly, the patients' intact ability to generally alter their limb stiffness during the postural task (albeit less than age-matched control subjects) improved their movement performance in a subsequent tracing task. The transfer of stiffness control from the static to the movement task may be a strategy that can be used by patients to compensate for their motor deficits.

Role of the olivo-cerebellar complex in motor learning and control

How is the cerebellum capable of efficient motor learning and control despite very low firing of the inferior olive (IO) inputs, which are postulated to carry errors needed for learning and contribute to on-line motor control? IO neurons form the largest electrically coupled network in the adult human brain. Here, we discuss how intermediate coupling strengths can lead to chaotic resonance and increase information transmission of the error signal despite the very low IO firing rate. This increased information transmission can then lead to more efficient learning than with weak or strong coupling. In addition, we argue that a dynamic modulation of IO electrical coupling via the Purkinje cell-deep cerebellar neurons – IO triangle could speed up learning and improve on-line control. Initially strong coupling would allow transmission of large errors to multiple functionally related Purkinje cells, resulting in fast but coarse learning as well as significant effects on deep cerebellar nucleus and on-line motor control. In the late phase of learning decreased coupling would allow desynchronized IO firing, allowing high-fidelity transmission of error, resulting in slower but fine learning, and little on-line motor control effects.

Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study

The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular-layer network. In response to mossy fiber (MF) bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at MF to granule cell synapses regulated the delay of the first spike and the weight at MF and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First-spike timing was regulated with millisecond precision and the number of spikes ranged from zero to three. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time-scale and allows the cerebellar granular layer to flexibly control burst transmission along the MF pathway.

Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics

We often make reaching movements having similar trajectories within very different mechanical environments, for example, with and without an added load in the hand. Under these varying conditions, our kinematic intentions must be transformed into muscle commands that move the limbs. Primary motor cortex (M1) has been implicated in the neural mechanism that mediates this adaptation to new movement dynamics, but our recent experiments suggest otherwise. We have recorded from electrode arrays that were chronically implanted in M1 as monkeys made reaching movements under two different dynamic conditions: the movements were opposed by either a clockwise or counterclockwise velocity-dependent force field acting at the hand. Under these conditions, the preferred direction (PD) of neural discharge for nearly all neurons rotated in the direction of the applied field, as did those of proximal limb electromyograms (EMGs), although the median neural rotation was significantly smaller than that of muscles. For a given neuron, the rotation angle was very consistent, even across multiple sessions. Within the limits of measurement uncertainty, both the neural and EMG changes occurred nearly instantaneously, reaching a steady state despite ongoing behavioral adaptation. Our results suggest that M1 is not directly involved in the adaptive changes that occurred within an experimental session. Rather, most M1 neurons are directly related to the dynamics of muscle activation that themselves reflect the external load. It appears as though gain modulation, the differential recruitment of M1 neurons by higher motor areas, can account for the load and behavioral adaptation-related changes in M1 discharge.

Cerebellar motor learning: are environment dynamics more important than error size?

Cerebellar damage impairs the control of complex dynamics during reaching movements. It also impairs learning of predictable dynamic perturbations through an error-based process. Prior work suggests that there are distinct neural mechanisms involved in error-based learning that depend on the size of error experienced. This is based, in part, on the observation that people with cerebellar degeneration may have an intact ability to learn from small errors. Here we studied the relative effect of specific dynamic perturbations and error size on motor learning of a reaching movement in patients with cerebellar damage. We also studied generalization of learning within different coordinate systems (hand vs. joint space). Contrary to our expectation, we found that error size did not alter cerebellar patients' ability to learn the force field. Instead, the direction of the force field affected patients' ability to learn, regardless of whether the force perturbations were introduced gradually (small error) or abruptly (large error). Patients performed best in fields that helped them compensate for movement dynamics associated with reaching. However, they showed much more limited generalization patterns than control subjects, indicating that patients rely on a different learning mechanism. We suggest that patients typically use a compensatory strategy to counteract movement dynamics. They may learn to relax this compensatory strategy when the external perturbation is favorable to counteracting their movement dynamics, and improve reaching performance. Altogether, these findings show that dynamics affect learning in cerebellar patients more than error size.

Processing of multi-dimensional sensorimotor information in the spinal and cerebellar neuronal circuitry: a new hypothesis

Why are sensory signals and motor command signals combined in the neurons of origin of the spinocerebellar pathways and why are the granule cells that receive this input thresholded with respect to their spike output? In this paper, we synthesize a number of findings into a new hypothesis for how the spinocerebellar systems and the cerebellar cortex can interact to support coordination of our multi-segmented limbs and bodies. A central idea is that recombination of the signals available to the spinocerebellar neurons can be used to approximate a wide array of functions including the spatial and temporal dependencies between limb segments, i.e. information that is necessary in order to achieve coordination. We find that random recombination of sensory and motor signals is not a good strategy since, surprisingly, the number of granule cells severely limits the number of recombinations that can be represented within the cerebellum. Instead, we propose that the spinal circuitry provides useful recombinations, which can be described as linear projections through aspects of the multi-dimensional sensorimotor input space. Granule cells, potentially with the aid of differentiated thresholding from Golgi cells, enhance the utility of these projections by allowing the Purkinje cell to establish piecewise-linear approximations of non-linear functions. Our hypothesis provides a novel view on the function of the spinal circuitry and cerebellar granule layer, illustrating how the coordinating functions of the cerebellum can be crucially supported by the recombinations performed by the neurons of the spinocerebellar systems.

Does a basic deficit in force control underlie cerebellar ataxia?

Because damage to the cerebellum results in characteristic movement incoordination known as "ataxia," it has been hypothesized that it is involved in estimation of limb dynamics that occur during movement. However, cerebellar function may extend beyond movement to force control in general, with or without movement. Here we tested whether the cerebellum is involved in controlling force separate from estimating limb dynamics and whether ataxia could result from a deficit in force control. We studied patients with cerebellar ataxia controlling their arm force isometrically; in this condition arm dynamics are absent and there is no need for (or effect from an impairment in) estimations of limb dynamics. Subjects were required to control their force magnitude, direction, or both. Cerebellar patients were able to match force magnitude or direction similarly to control subjects. Furthermore, when controlling force magnitude, they intuitively chose directions (not specified) that required minimal effort at the joint level--this ability was also similar to control subjects. In contrast, cerebellar patients performed significantly worse than control subjects when asked to match both force magnitude and direction. This was surprising, since they did not exhibit significant impairment in doing either in isolation. These results show that cerebellum-dependent computations are not limited to estimations of body dynamics needed for active movement. Deficits occur even in isometric conditions, but apparently only when multiple degrees of freedom must be controlled simultaneously. Thus a fundamental cerebellar operation may be combining/coordinating degrees of freedom across many kinds of movements and behaviors.

Learning Kinematic Constraints in Laparoscopic Surgery

To better understand how kinematic variables impact learning in surgical training, we devised an interactive environment for simulated laparoscopic maneuvers, using either 1) mechanical constraints typical of a surgical "box-trainer" or 2) virtual constraints in which free hand movements control virtual tool motion. During training, the virtual tool responded to the absolute position in space (Position-Based) or the orientation (Orientation-Based) of a hand-held sensor. Volunteers were further assigned to different sequences of target distances (Near-Far-Near or Far-Near-Far). Training with the Orientation-Based constraint enabled much lower path error and shorter movement times during training, which suggests that tool motion that simply mirrors joint motion is easier to learn. When evaluated in physically constrained (physical box-trainer) conditions, each group exhibited improved performance from training. However, Position-Based training enabled greater reductions in movement error relative to Orientation-Based (mean difference: 14.0 percent; CI: 0.7, 28.6). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13:5 percent, CI: -19:5, -7:5). Training that focused on shallow tool insertion (near targets) might promote more efficient movement strategies by emphasizing the curvature of tool motion. In addition, our findings suggest that an understanding of absolute tool position is critical to coping with mechanical interactions between the tool and trocar.

What features of limb movements are encoded in the discharge of cerebellar neurons?

This review examines the signals encoded in the discharge of cerebellar neurons during voluntary arm and hand movements, assessing the state of our knowledge and the implications for hypotheses of cerebellar function. The evidence for the representation of forces, joint torques, or muscle activity in the discharge of cerebellar neurons is limited, questioning the validity of theories that the cerebellum directly encodes the motor command. In contrast, kinematic parameters such as position, direction, and velocity are widely and robustly encoded in the activity of cerebellar neurons. These findings favor hypotheses that the cerebellum plans or controls movements in a kinematic framework, such as the proposal that the cerebellum provides a forward internal model. Error signals are needed for on-line correction and motor learning, and several hypotheses postulate the need for their representations in the cerebellum. Error signals have been described mostly in the complex spike discharge of Purkinje cells, but no consensus has emerged on the exact information signaled by complex spikes during limb movements. Newer studies suggest that simple spike firing may also encode error signals. Finally, Purkinje cells located more posterior and laterally in the cerebellar cortex and dentate neurons encode nonmotor, task-related signals such as visual cues. These results suggest that cerebellar neurons provide a complement of information about motor behaviors. We assert that additional single unit studies are needed using rich movement paradigms, given the power of this approach to directly test specific hypotheses about cerebellar function.

FROM SENSORS TO SPIKES: EVOLVING RECEPTIVE FIELDS TO ENHANCE SENSORIMOTOR INFORMATION IN A ROBOT-ARM

In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate sensor output per link (encoder) is commonly used to track the position and the velocity. Interfacing bio-inspired control systems with spiking neural networks emulating the cerebellum with conventional robots is not a straight forward task. Therefore, it is necessary to adapt this one-dimensional measure (encoder output) into a multidimensional space (inputs for a spiking neural network) to connect, for instance, the spiking cerebellar architecture; i.e. a translation from an analog space into a distributed population coding in terms of spikes. This paper analyzes how evolved receptive fields (optimized towards information transmission) can efficiently generate a sensorimotor representation that facilitates its discrimination from other "sensorimotor states". This can be seen as an abstraction of the Cuneate Nucleus (CN) functionality in a robot-arm scenario. We model the CN as a spiking neuron population coding in time according to the response of mechanoreceptors during a multi-joint movement in a robot joint space. An encoding scheme that takes into account the relative spiking time of the signals propagating from peripheral nerve fibers to second-order somatosensory neurons is proposed. Due to the enormous number of possible encodings, we have applied an evolutionary algorithm to evolve the sensory receptive field representation from random to optimized encoding. Following the nature-inspired analogy, evolved configurations have shown to outperform simple hand-tuned configurations and other homogenized configurations based on the solution provided by the optimization engine (evolutionary algorithm). We have used artificial evolutionary engines as the optimization tool to circumvent nonlinearity responses in receptive fields.

Learning with slight forgetting optimizes sensorimotor transformation in redundant motor systems

Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.

It is thought that the brain can optimize motor commands to produce efficient movements; however, it is unknown how this optimization process is implemented in the brain. Here we examine a biologically plausible hypothesis in which slight forgetting in the motor learning process plays an important role in the optimization process. Using a neural network model for motor learning, we initially theoretically demonstrated that motor learning with a slight forgetting factor consistently led the network to converge at an optimal state. In addition, by applying the forgetting scheme to a more sophisticated neural network model with realistic musculoskeletal data, we showed that the model could account for the reported stereotypical activity patterns of muscles and motor cortex neurons in various motor tasks. Our results support the hypothesis that slight forgetting, which is conventionally considered to diminish motor learning performance, plays a crucial role in the optimization process of the redundant motor system.

A modular neural model of motor synergies

Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments.

Neural circuits of the cerebellum: hypothesis for function

The rapid growth of cerebellar research is going to clarify several aspects of cellular and circuit physiology. However, the concepts about cerebellar mechanisms of function are still largely related to clinical observations and to models elaborated before the last discoveries appeared. In this paper, the major issues are revisited, suggesting that previous concepts can now be refined and modified. The cerebellum is fundamentally involved in timing and in controlling the ordered and precise execution of motor sequences. The fast reaction of the cerebellum to the inputs is sustained by specific cellular mechanisms ensuring precision on the millisecond scale. These include burst-burst reconversion in the granular layer and instantaneous frequency modulation on the 100-Hz band in Purkinje and deep cerebellar nuclei cells. Precisely timed signals can be used for perceptron operations in Purkinje cells and to establish appropriate correlations with climbing fiber signals inducing learning at parallel fiber synapses. In the granular layer, plasticity turns out to be instrumental to timing, providing a conceptual solution to the discrepancy between cerebellar learning and timing. The granular layer sub-circuit can be tuned by long-term synaptic plasticity and synaptic inhibition to delay the incoming signals over a 100-ms range. For longer sequences, large circuit sections can be entrained into coherent activity in 100-ms cycles. These dynamic aspects, which have not been accounted for by original theories, could in fact represent the essence of cerebellar functioning. It is suggested that the cerebellum can, in this way, operate the realignment of temporally incongruent signals, allowing their binding and pattern recognition in Purkinje cells. The demonstration of these principles, their behavioral relevance and their relationship with internal model theories represent a challenge for future cerebellar research.