Temporal firing patterns of Purkinje cells in the cerebellar ventral paraflocculus during ocular following responses in monkeys II. Complex spikes

Complex spikes perturb movements and reveal the sensorimotor map of Purkinje cells

Computations that are performed by the cerebellar cortex are transmitted via simple spikes of Purkinje cells (P-cells) to downstream structures, but because P-cells are many synapses away from muscles, we do not know the relationship between modulation of simple spikes and control of behavior. Here, we recorded the spiking activities of hundreds of P-cells in the oculomotor vermis of marmosets during saccadic eye movements and found that following the presentation of a visual stimulus, the olivary input to a P-cell coarsely described the direction and amplitude of the visual stimulus as well as the upcoming movement. Occasionally, the complex spike occurred just before saccade onset, suppressing the P-cell's simple spikes and disrupting its output during that saccade. Remarkably, this brief suppression of simple spikes altered the saccade's trajectory by pulling the eyes toward the part of the visual space that was preferentially encoded by the olivary input to that P-cell. Thus, there is an alignment between the sensory space encoded by the complex spikes and the behavior conveyed by the simple spikes: a reduction in simple spikes is a signal to bias the ongoing movement toward the part of the sensory space preferentially encoded by the olivary input to that P-cell.

Cerebellar climbing fibers multiplex movement and reward signals during a voluntary movement task in mice

Cerebellar climbing fibers convey sensorimotor information and their errors, which are used for motor control and learning. Furthermore, they represent reward-related information. Despite such functional diversity of climbing fiber signals, it is still unclear whether each climbing fiber conveys the information of single or multiple modalities and how the climbing fibers conveying different information are distributed over the cerebellar cortex. Here we perform two-photon calcium imaging from cerebellar Purkinje cells in mice engaged in a voluntary forelimb lever-pull task and demonstrate that climbing fiber responses in 68% of Purkinje cells can be explained by the combination of multiple behavioral variables such as lever movement, licking, and reward delivery. Neighboring Purkinje cells exhibit similar climbing fiber response properties, form functional clusters, and share noise fluctuations of responses. Taken together, individual climbing fibers convey behavioral information on multiplex variables and are spatially organized into the functional modules of the cerebellar cortex.

From internal models toward metacognitive AI

In several papers published in Biological Cybernetics in the 1980s and 1990s, Kawato and colleagues proposed computational models explaining how internal models are acquired in the cerebellum. These models were later supported by neurophysiological experiments using monkeys and neuroimaging experiments involving humans. These early studies influenced neuroscience from basic, sensory-motor control to higher cognitive functions. One of the most perplexing enigmas related to internal models is to understand the neural mechanisms that enable animals to learn large-dimensional problems with so few trials. Consciousness and metacognition-the ability to monitor one's own thoughts, may be part of the solution to this enigma. Based on literature reviews of the past 20 years, here we propose a computational neuroscience model of metacognition. The model comprises a modular hierarchical reinforcement-learning architecture of parallel and layered, generative-inverse model pairs. In the prefrontal cortex, a distributed executive network called the "cognitive reality monitoring network" (CRMN) orchestrates conscious involvement of generative-inverse model pairs in perception and action. Based on mismatches between computations by generative and inverse models, as well as reward prediction errors, CRMN computes a "responsibility signal" that gates selection and learning of pairs in perception, action, and reinforcement learning. A high responsibility signal is given to the pairs that best capture the external world, that are competent in movements (small mismatch), and that are capable of reinforcement learning (small reward-prediction error). CRMN selects pairs with higher responsibility signals as objects of metacognition, and consciousness is determined by the entropy of responsibility signals across all pairs. This model could lead to new-generation AI, which exhibits metacognition, consciousness, dimension reduction, selection of modules and corresponding representations, and learning from small samples. It may also lead to the development of a new scientific paradigm that enables the causal study of consciousness by combining CRMN and decoded neurofeedback.

Cerebellar complex spikes multiplex complementary behavioral information

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves 2 types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC’s climbing fiber input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks, one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibers? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that, in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. Hence PCs receive a multiplexed climbing fiber input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves both high-frequency simple spikes and low-frequency complex spikes; the function of the latter, determined by a PC’s climbing fibre input, remains controversial. This study shows that PCs receive a multiplexed climbing fibre input that merges complementary streams of information relevant for behaviour.

Visuomotor control of intermittent circular tracking movements with visually guided orbits in 3D VR environment

The analysis of visually guided tracking movements is important to the understanding of imitation exercises and movements carried out using the human visuomotor control system. In this study, we analyzed the characteristics of visuomotor control in the intermittent performance of circular tracking movements by applying a system that can differentiate between the conditions of invisible and visible orbits and visible and invisible target phases implemented in a 3D VR space. By applying visuomotor control based on velocity control, our study participants were able to track objects with visible orbits with a precision of approximately 1.25 times greater than they could track objects with invisible orbits. We confirmed that position information is an important parameter related to intermittent motion at low speeds (below 0.5 Hz) and that tracked target velocity information could be obtained more precisely than position information at speeds above 0.5 Hz. Our results revealed that the feedforward (FF) control corresponding to velocity was delayed under the visible-orbit condition at speeds over 0.5 Hz, suggesting that, in carrying out imitation exercises and movements, the use of visually presented 3D guides can interfere with exercise learning and, therefore, that the effects of their use should be carefully considered.

Seeing motion of controlled object improves grip timing in adults with autism spectrum condition: evidence for use of inverse dynamics in motor control

Previous studies (Haswell et al. in Nat Neurosci 12:970-972, 2009; Marko et al. in Brain J Neurol 138:784-797, 2015) reported that people with autism rely less on vision for learning to reach in a force field. This suggested a possibility that they have difficulties in extracting force information from visual motion signals, a process called inverse dynamics computation. Our recent study (Takamuku et al. in J Int Soc Autism Res 11:1062-1075, 2018) examined the ability of inverse computation with two perceptual tasks and found similar performances in typical and autistic adults. However, this tested the computation only in the context of sensory perception while it was possible that the suspected disability is specific to the motor domain. Here, in order to address the concern, we tested the use of inverse dynamics computation in the context of motor control by measuring changes in grip timing caused by seeing/not seeing a controlled object. The motion of the object was informative of its inertial force and typical participants improved their grip timing based on the visual feedback. Our interest was on whether the autism participants show the same improvement. While some autism participants showed atypical hand slowing when seeing the controlled object, we found no evidence of abnormalities in the inverse computation in our grip timing task or in a replication of the perceptual task. This suggests that the ability of inverse dynamics computation is preserved not only for sensory perception but also for motor control in adults with autism.

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

We previously proposed, on theoretical grounds, that the cerebellum must regulate the dimensionality of its neuronal activity during motor learning and control to cope with the low firing frequency of inferior olive neurons, which form one of two major inputs to the cerebellar cortex. Such dimensionality regulation is possible via modulation of electrical coupling through the gap junctions between inferior olive neurons by inhibitory GABAergic synapses. In addition, we previously showed in simulations that intermediate coupling strengths induce chaotic firing of inferior olive neurons and increase their information carrying capacity. However, there is no in vivo experimental data supporting these two theoretical predictions. Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions: carbenoxolone (gap junctions blocker), control, and picrotoxin (GABA-A receptor antagonist). To examine the effect of electrical coupling on dimensionality and chaotic dynamics, we first determined the physiological range of effective coupling strengths between inferior olive neurons in the three conditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian model averaging approach. We found that effective coupling co-varied with synchrony and was inversely related to the dimensionality of inferior olive firing dynamics, as measured via a principal component analysis of the spike trains in each condition. Furthermore, for both the model and the data, we found an inverted U-shaped relationship between coupling strengths and complexity entropy, a measure of chaos for spiking neural data. These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning and control of high dimensional motor systems by the cerebellum.

Computational theory suggests that the cerebellum must decrease the dimensionality of its neuronal activity to learn and control high dimensional motor systems effectively, while being constrained by the low firing frequency of inferior olive neurons, one of the two major source of input signals to the cerebellum. We previously proposed that the cerebellum adaptively controls the dimensionality of inferior olive firing by adjusting the level of synchrony and that such control is made possible by modulating the electrical coupling strength between inferior olive neurons. Here, we developed a novel method that uses a biophysical model of the inferior olive to accurately estimate the effective coupling strengths between inferior olive neurons from in vivo recordings of spike activity in three different coupling conditions. We found that high coupling strengths induce synchronous firing and decrease the dimensionality of inferior olive firing dynamics. In contrast, intermediate coupling strengths lead to chaotic firing and increase the dimensionality of the firing dynamics. Thus, electrical coupling is a feasible mechanism to control dimensionality and chaotic firing of inferior olive neurons. In sum, our results provide insights into possible mechanisms underlying cerebellar function and, in general, a biologically plausible framework to control the dimensionality of neural coding.

50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

3D human arm reaching movement planning with principal patterns in successive phases

There are observations indicating that the central nervous system (CNS) decomposes a movement into several successive sub-movements as an effective strategy to control the motor task. In this study, we propose an algorithm in which, Arm Reaching Movement (ARM) in 3D space is decomposed into several successive phases using zero joint angle jerk features of the arm kinematic data. The presented decomposition algorithm for 3D motions is, in fact, an improved and generalized version of the decomposition method proposed earlier by Emadi and Bahrami in 2012 for 2D movements. They assumed that the motion is coordinated by minimum jerk characteristics in joint angles space in each phase. However, at the first glance, it seems that in 3D ARM joint angles are not coordinated based on the minimum jerk features. Therefore, we defined a resultant variable in the joint space and showed that one can use its jerk properties together with those of the elbow joint in movement decomposition. We showed that phase borders determined with the proposed algorithm in 3D ARM, are defined with jerk characteristics of ARM's performance variable. We observed the same results in the Sit-to-Stand (STS) movement, too. Thus, based on our results, we suggested that any 3D motion can be decomposed into several phases, such that in each phase a set of principal patterns (PPs) extracted by Principal Component Analysis (PCA) method are linearly recruited to regenerate angle trajectories of each joint. Our results also suggest that the CNS, as the primary policy, may simplify the control of the ARMs by reducing the dimension of the control space. This dimension reduction might be accomplished by decomposing the movement into successive phases in which the movement satisfies the minimum joint angle jerk constraint. Then, in each phase, a set of PPs are recruited in the joint space to regenerate angle trajectory of each joint. Then, the dimension of the control space will be the number of the recruitment coefficients.

Cerebellar climbing fibers encode expected reward size

Climbing fiber inputs to the cerebellum encode error signals that instruct learning. Recently, evidence has accumulated to suggest that the cerebellum is also involved in the processing of reward. To study how rewarding events are encoded, we recorded the activity of climbing fibers when monkeys were engaged in an eye movement task. At the beginning of each trial, the monkeys were cued to the size of the reward that would be delivered upon successful completion of the trial. Climbing fiber activity increased when the monkeys were presented with a cue indicating a large reward, but not a small reward. Reward size did not modulate activity at reward delivery or during eye movements. Comparison between climbing fiber and simple spike activity indicated different interactions for coding of movement and reward. These results indicate that climbing fibers encode the expected reward size and suggest a general role of the cerebellum in associative learning beyond error correction.

Physiological and Morphological Principles Underpinning Recruitment of the Cerebellar Reserve

Background:

In order to optimize outcomes of novel therapies for cerebellar ataxias (CAs), it is desirable to start these therapies while declined functions are restorable: i.e. while the so-called cere-bellar reserve remains.

Objective:

In this mini-review, we tried to define and discuss the cerebellar reserve from physiological and morphological points of view.

Method: The cerebellar neuron circuitry is designed to generate spatiotemporally organized outputs, re-gardless of the region. Therefore, the cerebellar reserve may be defined as a mechanism to restore its proper input-output organization of the cerebellar neuron circuitry, when it is damaged. Then, the follow-ing four components are essential for recruitment of the cerebellar reserve: operational local neuron cir-cuitry; proper combination of mossy fiber inputs to be integrated; climbing fiber inputs to instruct favor-able reorganization of the integration; deep cerebellar nuclei to generate reorganized outputs.

Results:

We discussed three topics related to these resources, 1) principles of generating organized cere-bellar outputs, 2) redundant mossy fiber inputs to the cerebellum, 3) plasticity of the cerebellar neuron circuitry.

Conclusion:

To make most of the cerebellar reserve, it is desirable to start any intervention as early as possible when the cerebellar cell loss is minimal or even negligible. Therefore, an ideal future therapy for degenerative cerebellar diseases should start before consuming the cerebellar reserve at all. In the meantime, our real challenge is to establish a reliable method to identify the decrease in the cerebellar re-serve as early as possible.

Cerebellum, Predictions and Errors

Making predictions and validating the predictions against actual sensory information is thought to be one of the most fundamental functions of the nervous system. A growing body of evidence shows that the neural mechanisms controlling behavior, both in motor and non-motor domains, rely on prediction errors, the discrepancy between predicted and actual information. The cerebellum has been viewed as a key component of the motor system providing predictions about upcoming movements and receiving feedback about motor errors. Consequentially, studies of cerebellar function have focused on the motor domain with less consideration for the wider context in which movements are generated. However, motor learning experiments show that cognition makes important contributions to motor adaptation that involves the cerebellum. One of the more successful theoretical frameworks for understanding motor control and cerebellar function is the forward internal model which states that the cerebellum predicts the sensory consequences of the motor commands and is involved in computing sensory prediction errors by comparing the predictions to the sensory feedback. The forward internal model was applied and tested mainly for effector movements, raising the question whether cerebellar encoding of behavior reflects task performance measures associated with cognitive involvement. Electrophysiological studies based on pseudo-random tracking in monkeys show that the discharge of Purkinje cell, the sole output neurons of the cerebellar cortex, encodes predictive and feedback signals not only of the effector kinematics but also of task performance. The implications are that the cerebellum implements both effector and task performance forward models and the latter are consistent with the cognitive contributions observed during motor learning. The implications of these findings include insights into recent psychophysical observations on moving with reduced feedback and motor learning. The findings also support the cerebellum's place in hierarchical generative models that work in concert to refine predictions about behavior and the world. Therefore, cerebellar representations bridge motor and non-motor domains and provide a better understanding of cerebellar function within the functional architecture of the brain.

Complex Spike Wars: a New Hope

The climbing fiber-Purkinje cell circuit is one of the most powerful and highly conserved in the central nervous system. Climbing fibers exert a powerful excitatory action that results in a complex spike in Purkinje cells and normal functioning of the cerebellum depends on the integrity of climbing fiber-Purkinje cell synapse. Over the last 50 years, multiple hypotheses have been put forward on the role of the climbing fibers and complex spikes in cerebellar information processing and motor control. Central to these theories is the nature of the interaction between the low-frequency complex spike discharge and the high-frequency simple spike firing of Purkinje cells. This review examines the major hypotheses surrounding the action of the climbing fiber-Purkinje cell projection, discussing both supporting and conflicting findings. The review describes newer findings establishing that climbing fibers and complex spikes provide predictive signals about movement parameters and that climbing fiber input controls the encoding of behavioral information in the simple spike firing of Purkinje cells. Finally, we propose the dynamic encoding hypothesis for complex spike function that strives to integrate established and newer findings.

Purkinje Cell Representations of Behavior: Diary of a Busy Neuron

Fundamental for understanding cerebellar function is determining the representations in Purkinje cell activity, the sole output of the cerebellar cortex. Up to the present, the most accurate descriptions of the information encoded by Purkinje cells were obtained in the context of motor behavior and reveal a high degree of heterogeneity of kinematic and performance error signals encoded. The most productive framework for organizing Purkinje cell firing representations is provided by the forward internal model hypothesis. Direct tests of this hypothesis show that individual Purkinje cells encode two different forward models simultaneously, one for effector kinematics and one for task performance. Newer results demonstrate that the timing of simple spike encoding of motor parameters spans an extend interval of up to ±2 seconds. Furthermore, complex spike discharge is not limited to signaling errors, can be predictive, and dynamically controls the information in the simple spike firing to meet the demands of upcoming behavior. These rich, diverse, and changing representations highlight the integrative aspects of cerebellar function and offer the opportunity to generalize the cerebellar computational framework over both motor and non-motor domains.

Tandem internal models execute motor learning in the cerebellum

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition.

Modulation of sensory prediction error in Purkinje cells during visual feedback manipulations

It is hypothesized that the cerebellum implements a forward internal model that transforms motor commands into predictions about upcoming movements. The predictions are compared with sensory feedback to generate sensory prediction errors critical to controlling movements. The simple spike firing of cerebellar Purkinje cells both lead and lag movement consistent with representations of motor predictions and sensory feedback. This study tests whether this leading and lagging modulation provides the prediction and sensory feedback necessary to compute sensory prediction errors. Two manipulations of the visual feedback are used in rhesus monkeys performing pseudo-random tracking. Consistent with a forward model, delaying the visual feedback demonstrates that the leading simple spike modulation with position error is time-locked to the hand movement. Reducing the feedback shows that the lagged modulation is directly driven by visual inputs. Therefore, Purkinje cell discharge carries both the motor predictions and sensory feedback required of a forward internal model.

Computational Theory Underlying Acute Vestibulo-ocular Reflex Motor Learning with Cerebellar Long-Term Depression and Long-Term Potentiation

The vestibulo-ocular reflex (VOR) can be viewed as an adaptive control system that maintains compensatory eye movements during head motion. As the cerebellar flocculus is intimately involved in this adaptive motor control of the VOR, the VOR has been a popular model system for investigating cerebellar motor learning. Long-term depression (LTD) and long-term potentiation (LTP) at the parallel fiber-Purkinje cell synapses are considered to play major roles in cerebellar motor learning. A recent study using mutant mice demonstrated cerebellar motor learning with hampered LTD; the study concluded that the parallel fiber-Purkinje cell LTD is not essential. More recently, multiple forms of plasticity have been found in the cerebellum, and they are believed to contribute to cerebellar motor learning. However, it is still unclear how synaptic plasticity modifies the signal processing that underlies motor learning in the flocculus. A computational simulation suggested that the plasticity present in mossy fiber-granule cell synapses improves VOR-related sensory-motor information transferred into granule cells, whereas the plasticity in the molecular layer stores this information as a memory under guidance from climbing fiber teaching signals. Thus, motor learning and memory are thought to be induced mainly by LTD and LTP at parallel fiber-Purkinje cell synapses and by rebound potentiation at molecular interneuron-Purkinje cell synapses among the multiple forms of plasticity in the cerebellum. In this study, we focused on the LTD and LTP at parallel fiber-Purkinje cell synapses. Based on our simulation, we propose that acute VOR motor learning accomplishes by simultaneous enhancement of eye movement signals via LTP and suppression of vestibular signals via LTD to increase VOR gain (gain-up learning). To decrease VOR gain (gain-down learning), these two signals are modified in the opposite directions; namely, LTD suppresses eye movement signals, whereas LTP enhances vestibular signals.

Climbing fibers predict movement kinematics and performance errors

Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey (Macaca mulatta). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to "events," either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control.NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each parameter. In contrast with the view that CSs carry feedback signals, the CSs are predominantly predictive of upcoming position errors and kinematics. Therefore, climbing fibers carry multiple and predictive signals for online motor control.

Neural activity in the dorsal medial superior temporal area of monkeys represents retinal error during adaptive motor learning

To adapt to variable environments, humans regulate their behavior by modulating gains in sensory-to-motor processing. In this study, we measured a simple eye movement, the ocular following response (OFR), in monkeys to study the neuronal basis of adaptive motor learning in the visuomotor processing stream. The medial superior temporal (MST) area of the cerebral cortex is a critical site for contextual gain modulation of the OFR. However, the role of MST neurons in adaptive gain modulation of the OFR remains unknown. We adopted a velocity step-down sequence paradigm that was designed to promote adaptive gain modulation of the OFR to investigate the role of the dorsal MST (MSTd) in adaptive motor learning. In the initial learning stage, we observed a reduction in the OFR but no significant change in the “open-loop” responses for the majority of the MSTd neurons. However, in the late learning stage, some MSTd neurons exhibited significantly enhanced “closed-loop” responses in association with increases in retinal error velocity. These results indicate that the MSTd area primarily encodes visual motion, suggesting that MSTd neurons function upstream of the motor learning site to provide sensory signals to the downstream structures involved in adaptive motor learning.

Evaluation of Teaching Signals for Motor Control in the Cerebellum during Real-World Robot Application

Motor learning in the cerebellum is believed to entail plastic changes at synapses between parallel fibers and Purkinje cells, induced by the teaching signal conveyed in the climbing fiber (CF) input. Despite the abundant research on the cerebellum, the nature of this signal is still a matter of debate. Two types of movement error information have been proposed to be plausible teaching signals: sensory error (SE) and motor command error (ME); however, their plausibility has not been tested in the real world. Here, we conducted a comparison of different types of CF teaching signals in real-world engineering applications by using a realistic neuronal network model of the cerebellum. We employed a direct current motor (simple task) and a two-wheeled balancing robot (difficult task). We demonstrate that SE, ME or a linear combination of the two is sufficient to yield comparable performance in a simple task. When the task is more difficult, although SE slightly outperformed ME, these types of error information are all able to adequately control the robot. We categorize granular cells according to their inputs and the error signal revealing that different granule cells are preferably engaged for SE, ME or their combination. Thus, unlike previous theoretical and simulation studies that support either SE or ME, it is demonstrated for the first time in a real-world engineering application that both SE and ME are adequate as the CF teaching signal in a realistic computational cerebellar model, even when the control task is as difficult as stabilizing a two-wheeled balancing robot.

The Errors of Our Ways: Understanding Error Representations in Cerebellar-Dependent Motor Learning

The cerebellum is essential for error-driven motor learning and is strongly implicated in detecting and correcting for motor errors. Therefore, elucidating how motor errors are represented in the cerebellum is essential in understanding cerebellar function, in general, and its role in motor learning, in particular. This review examines how motor errors are encoded in the cerebellar cortex in the context of a forward internal model that generates predictions about the upcoming movement and drives learning and adaptation. In this framework, sensory prediction errors, defined as the discrepancy between the predicted consequences of motor commands and the sensory feedback, are crucial for both on-line movement control and motor learning. While many studies support the dominant view that motor errors are encoded in the complex spike discharge of Purkinje cells, others have failed to relate complex spike activity with errors. Given these limitations, we review recent findings in the monkey showing that complex spike modulation is not necessarily required for motor learning or for simple spike adaptation. Also, new results demonstrate that the simple spike discharge provides continuous error signals that both lead and lag the actual movements in time, suggesting errors are encoded as both an internal prediction of motor commands and the actual sensory feedback. These dual error representations have opposing effects on simple spike discharge, consistent with the signals needed to generate sensory prediction errors used to update a forward internal model.

Chaotic Resonance in Coupled Inferior Olive Neurons with the Llinás Approach Neuron Model

It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaotic irregular firing in the IO neuron assembly; such chaotic resonance (CR) was replicated in their computer demonstration of a Hodgkin-Huxley (HH)-type compartment model. In this study, we examined the response of CR to a periodic signal in the IO neuron assembly comprising the Llinás approach IO neuron model. This system involves empirically observed dynamics of the IO membrane potential and is simpler than the HH-type compartment model. We then clarified its dependence on electrical coupling strength, input signal strength, and frequency. Furthermore, we compared the physiological validity for IO neurons such as low firing rate and sustaining subthreshold oscillation between CR and conventional stochastic resonance (SR) and examined the consistency with asynchronous firings indicated by the previous model-based studies in the cerebellar learning process. In addition, the signal response of CR and SR was investigated in a large neuron assembly. As the result, we confirmed that CR was consistent with the above IO neuron's characteristics, but it was not as easy for SR.

Modulation of error-sensitivity during a prism adaptation task in people with cerebellar degeneration

Cerebellar damage can profoundly impair human motor adaptation. For example, if reaching movements are perturbed abruptly, cerebellar damage impairs the ability to learn from the perturbation-induced errors. Interestingly, if the perturbation is imposed gradually over many trials, people with cerebellar damage may exhibit improved adaptation. However, this result is controversial, since the differential effects of gradual vs. abrupt protocols have not been observed in all studies. To examine this question, we recruited patients with pure cerebellar ataxia due to cerebellar cortical atrophy (n = 13) and asked them to reach to a target while viewing the scene through wedge prisms. The prisms were computer controlled, making it possible to impose the full perturbation abruptly in one trial, or build up the perturbation gradually over many trials. To control visual feedback, we employed shutter glasses that removed visual feedback during the reach, allowing us to measure trial-by-trial learning from error (termed error-sensitivity), and trial-by-trial decay of motor memory (termed forgetting). We found that the patients benefited significantly from the gradual protocol, improving their performance with respect to the abrupt protocol by exhibiting smaller errors during the exposure block, and producing larger aftereffects during the postexposure block. Trial-by-trial analysis suggested that this improvement was due to increased error-sensitivity in the gradual protocol. Therefore, cerebellar patients exhibited an improved ability to learn from error if they experienced those errors gradually. This improvement coincided with increased error-sensitivity and was present in both groups of subjects, suggesting that control of error-sensitivity may be spared despite cerebellar damage.

How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error and distal learning problems. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.

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.

Releasing dentate nucleus cells from Purkinje cell inhibition generates output from the cerebrocerebellum

The cerebellum generates its vast amount of output to the cerebral cortex through the dentate nucleus (DN) that is essential for precise limb movements in primates. Nuclear cells in DN generate burst activity prior to limb movement, and inactivation of DN results in cerebellar ataxia. The question is how DN cells become active under intensive inhibitory drive from Purkinje cells (PCs). There are two excitatory inputs to DN, mossy fiber and climbing fiber collaterals, but neither of them appears to have sufficient strength for generation of burst activity in DN. Therefore, we can assume two possible mechanisms: post-inhibitory rebound excitation and disinhibition. If rebound excitation works, phasic excitation of PCs and a concomitant inhibition of DN cells should precede the excitation of DN cells. On the other hand, if disinhibition plays a primary role, phasic suppression of PCs and activation of DN cells should be observed at the same timing. To examine these two hypotheses, we compared the activity patterns of PCs in the cerebrocerebellum and DN cells during step-tracking wrist movements in three Japanese monkeys. As a result, we found that the majority of wrist-movement-related PCs were suppressed prior to movement onset and the majority of wrist-movement-related DN cells showed concurrent burst activity without prior suppression. In a minority of PCs and DN cells, movement-related increases and decreases in activity, respectively, developed later. These activity patterns suggest that the initial burst activity in DN cells is generated by reduced inhibition from PCs, i.e., by disinhibition. Our results indicate that suppression of PCs, which has been considered secondary to facilitation, plays the primary role in generating outputs from DN. Our findings provide a new perspective on the mechanisms used by PCs to influence limb motor control and on the plastic changes that underlie motor learning in the cerebrocerebellum.

Coding of stimulus strength via analog calcium signals in Purkinje cell dendrites of awake mice

The climbing fiber input to Purkinje cells acts as a teaching signal by triggering a massive influx of dendritic calcium that marks the occurrence of instructive stimuli during cerebellar learning. Here, we challenge the view that these calcium spikes are all-or-none and only signal whether the instructive stimulus has occurred, without providing parametric information about its features. We imaged ensembles of Purkinje cell dendrites in awake mice and measured their calcium responses to periocular airpuffs that serve as instructive stimuli during cerebellar-dependent eyeblink conditioning. Information about airpuff duration and pressure was encoded probabilistically across repeated trials, and in two additional signals in single trials: the synchrony of calcium spikes in the Purkinje cell population, and the amplitude of the calcium spikes, which was modulated by a non-climbing fiber pathway. These results indicate that calcium-based teaching signals in Purkinje cells contain analog information that encodes the strength of instructive stimuli trial-by-trial.

DOI:http://dx.doi.org/10.7554/eLife.03663.001

A region of the brain known as the cerebellum plays a key role in learning how to anticipate an event. For example, if you know that a puff of air is going to be directed at your eye, it's a good idea to close it in advance. However, how much you need to close it depends on how strong that puff of air is. A very strong puff might require closing the eye completely to protect it. In contrast, it is probably better to only partially close the eye if you know a lighter puff of air is coming, so that you can still see.

Extensive research has focused on how neurons in and around the cerebellum work together to achieve this goal. When an event—such as a puff of air—occurs, signals are sent to large neurons in the cerebellum, called Purkinje cells, by ‘climbing fibers’. However, climbing fibers were thought to be able to respond in only two ways: either they fire in a single burst to signal that an event has occurred, or they don't fire. It was therefore unclear how the finer details of the event (for example, the strength of the puff of air) are transmitted to the cerebellum.

Najafi et al. imaged the level of calcium in the cerebellum of mice, as this indicates how active the neurons are. When a puff of air was directed at the eyes of the mice, Najafi et al. saw that the size of the response of the Purkinje cells corresponded with how big the puff of air was. Najafi et al. show that the size of this response, which is based mostly on input from the climbing fibers, is also influenced by input from an additional unknown source.

These findings show that Purkinje cells of the cerebellum receive detailed information about the nature of an event, such as a puff of air. What remains to be seen is whether the cerebellum uses this information to learn the correct response, that is how hard to blink to avoid the expected puff.

DOI:http://dx.doi.org/10.7554/eLife.03663.002

The cerebellum for jocks and nerds alike

Historically the cerebellum has been implicated in the control of movement. However, the cerebellum's role in non-motor functions, including cognitive and emotional processes, has also received increasing attention. Starting from the premise that the uniform architecture of the cerebellum underlies a common mode of information processing, this review examines recent electrophysiological findings on the motor signals encoded in the cerebellar cortex and then relates these signals to observations in the non-motor domain. Simple spike firing of individual Purkinje cells encodes performance errors, both predicting upcoming errors as well as providing feedback about those errors. Further, this dual temporal encoding of prediction and feedback involves a change in the sign of the simple spike modulation. Therefore, Purkinje cell simple spike firing both predicts and responds to feedback about a specific parameter, consistent with computing sensory prediction errors in which the predictions about the consequences of a motor command are compared with the feedback resulting from the motor command execution. These new findings are in contrast with the historical view that complex spikes encode errors. Evaluation of the kinematic coding in the simple spike discharge shows the same dual temporal encoding, suggesting this is a common mode of signal processing in the cerebellar cortex. Decoding analyses show the considerable accuracy of the predictions provided by Purkinje cells across a range of times. Further, individual Purkinje cells encode linearly and independently a multitude of signals, both kinematic and performance errors. Therefore, the cerebellar cortex's capacity to make associations across different sensory, motor and non-motor signals is large. The results from studying how Purkinje cells encode movement signals suggest that the cerebellar cortex circuitry can support associative learning, sequencing, working memory, and forward internal models in non-motor domains.

Nonvisual complex spike signals in the rabbit cerebellar flocculus

In addition to the well-known signals of retinal image slip, floccular complex spikes (CSs) also convey nonvisual signals. We recorded eye movement and CS activity from Purkinje cells in awake rabbits sinusoidally oscillated in the dark on a vestibular turntable. The stimulus frequency ranged from 0.2 to 1.2 Hz, and the velocity amplitude ranged from 6.3 to 50°/s. The average CS modulation was evaluated at each combination of stimulus frequency and amplitude. More than 75% of the Purkinje cells carried nonvisual CS signals. The amplitude of this modulation remained relatively constant over the entire stimulus range. The phase response of the CS modulation in the dark was opposite to that during the vestibulo-ocular reflex (VOR) in the light. With increased frequency, the phase response systematically shifted from being aligned with contraversive head velocity toward peak contralateral head position. At fixed frequency, the phase response was dependent on peak head velocity, indicating a system nonlinearity. The nonvisual CS modulation apparently reflects a competition between eye movement and vestibular signals, resulting in an eye movement error signal inferred from nonvisual sources. The combination of this error signal with the retinal slip signal in the inferior olive results in a net error signal reporting the discrepancy between the actual visually measured eye movement error and the inferred eye movement error derived from measures of the internal state. The presence of two error signals requires that the role of CSs in models of the floccular control of VOR adaption be expanded beyond retinal slip.

Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns

The inferior olive (IO) is a neural network belonging to the olivo-cerebellar system whose neurons are coupled with electrical synapses and display subthreshold oscillations and spiking activity. The IO is frequently proposed as the generator of timing signals to the cerebellum. Electrophysiological and imaging recordings show that the IO network generates complex spatio-temporal patterns. The generation and modulation of coherent spiking activity in the IO is one key issue in cerebellar research. In this work, we build a large scale IO network model of electrically coupled conductance-based neurons to study the emerging spatio-temporal patterns of its transient neuronal activity. Our modeling reproduces and helps to understand important phenomena observed in IO in vitro and in vivo experiments, and draws new predictions regarding the computational properties of this network and the associated cerebellar circuits. The main factors studied governing the collective dynamics of the IO network were: the degree of electrical coupling, the extent of the electrotonic connections, the presence of stimuli or regions with different excitability levels and the modulatory effect of an inhibitory loop (IL). The spatio-temporal patterns were analyzed using a discrete wavelet transform to provide a quantitative characterization. Our results show that the electrotonic coupling produces quasi-synchronized subthreshold oscillations over a wide dynamical range. The synchronized oscillatory activity plays the role of a timer for a coordinated representation of spiking rhythms with different frequencies. The encoding and coexistence of several coordinated rhythms is related to the different clusterization and coherence of transient spatio-temporal patterns in the network, where the spiking activity is commensurate with the quasi-synchronized subthreshold oscillations. In the presence of stimuli, different rhythms are encoded in the spiking activity of the IO neurons that nevertheless remains constrained to a commensurate value of the subthreshold frequency. The stimuli induced spatio-temporal patterns can reverberate for long periods, which contributes to the computational properties of the IO. We also show that the presence of regions with different excitability levels creates sinks and sources of coordinated activity which shape the propagation of spike wave fronts. These results can be generalized beyond IO studies, as the control of wave pattern propagation is a highly relevant problem in the context of normal and pathological states in neural systems (e.g., related to tremor, migraine, epilepsy) where the study of the modulation of activity sinks and sources can have a potential large impact.

Error detection and representation in the olivo-cerebellar system

Complex spikes generated in a cerebellar Purkinje cell via a climbing fiber have been assumed to encode errors in the performance of neuronal circuits involving Purkinje cells. To reexamine this notion in this review, I analyzed structures of motor control systems involving the cerebellum. A dichotomy was found between the two types of error: sensory and motor errors play roles in the feedforward and feedback control conditions, respectively. To substantiate this dichotomy, here in this article I reviewed recent data on neuronal connections and signal contents of climbing fibers in the vestibuloocular reflex (VOR), optokinetic eye movement response, saccade, hand reaching, cursor tracking, as well as some other cases of motor control. In our studies, various sources of sensory and motor errors were located in the neuronal pathways leading to the inferior olive. We noted that during the course of evolution, control system structures involving the cerebellum changed rather radically from the prototype seen in the flocculonodular lobe and vermis to that applicable to the cerebellar hemisphere. Nevertheless, the dichotomy between sensory and motor errors is maintained.

Predictive and feedback performance errors are signaled in the simple spike discharge of individual Purkinje cells

The cerebellum has been implicated in processing motor errors required for on-line control of movement and motor learning. The dominant view is that Purkinje cell complex spike discharge signals motor errors. This study investigated whether errors are encoded in the simple spike discharge of Purkinje cells in monkeys trained to manually track a pseudorandomly moving target. Four task error signals were evaluated based on cursor movement relative to target movement. Linear regression analyses based on firing residuals ensured that the modulation with a specific error parameter was independent of the other error parameters and kinematics. The results demonstrate that simple spike firing in lobules IV-VI is significantly correlated with position, distance, and directional errors. Independent of the error signals, the same Purkinje cells encode kinematics. The strongest error modulation occurs at feedback timing. However, in 72% of cells at least one of the R(2) temporal profiles resulting from regressing firing with individual errors exhibit two peak R(2) values. For these bimodal profiles, the first peak is at a negative τ (lead) and a second peak at a positive τ (lag), implying that Purkinje cells encode both prediction and feedback about an error. For the majority of the bimodal profiles, the signs of the regression coefficients or preferred directions reverse at the times of the peaks. The sign reversal results in opposing simple spike modulation for the predictive and feedback components. Dual error representations may provide the signals needed to generate sensory prediction errors used to update a forward internal model.

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.

Microlesions of the inferior olive reduce vestibular modulation of Purkinje cell complex and simple spikes in mouse cerebellum

Cerebellar Purkinje cells have two distinct action potentials: complex spikes (CSs) are evoked by single climbing fibers that originate from the contralateral inferior olive. Simple spikes (SSs) are often ascribed to mossy fiber-granule cell-parallel fiber inputs to Purkinje cells. Although generally accepted, this view lacks experimental support. Vestibular stimulation independently activates primary afferent mossy fibers and tertiary afferent climbing fibers that project to the uvula-nodulus (folia 8-10). CSs and SSs normally discharge antiphasically during sinusoidal roll-tilt. When CSs increase, SSs decrease. We tested the relative independence of these pathways in mice by making electrolytic microlesions of the two inferior olivary nuclei from which vestibular climbing fibers originate; the β-nucleus and dorsomedial cell column. This reduced vestibular climbing fiber signaling to the contralateral folia 8-10, while leaving intact vestibular primary and secondary afferent mossy fibers. We recorded from Purkinje cells and interneurons in folia 8-10, identified by juxtacellular labeling with Neurobiotin. Microlesions of the inferior olive increased the spontaneous discharge of SSs in contralateral folia 8-10, but blocked their modulation during vestibular stimulation. The vestibularly evoked discharge of excitatory cerebellar interneurons (granule cells and unipolar brush cells) was not modified by olivary microlesions. The modulated discharge of stellate cells, but not Golgi cells, was reduced by olivary microlesions. We conclude that vestibular modulation of CSs and SSs depends on intact climbing fibers. The absence of vestibularly modulated SSs following olivary microlesions reflects the loss of climbing fiber-evoked stellate cell discharge.

Relationship between complex and simple spike activity in macaque caudal vermis during three-dimensional vestibular stimulation

Lobules 10 and 9 in the caudal posterior vermis [also known as nodulus and uvula (NU)] are thought important for spatial orientation and balance. Here, we characterize complex spike (CS) and simple spike (SS) activity in response to three-dimensional vestibular stimulation. The strongest modulation was seen during translation (CS: 12.8 +/- 1.5, SS: 287.0 +/- 23.2 spikes/s/G, 0.5 Hz). Preferred directions tended to cluster along the cardinal axes (lateral, fore-aft, vertical) for CSs and along the semicircular canal axes for SSs. Most notably, the preferred directions for CS/SS pairs arising from the same Purkinje cells were rarely aligned. During 0.5 Hz pitch/roll tilt, only about a third of CSs had significant modulation. Thus, most CSs correlated best with inertial rather than net linear acceleration. By comparison, all SSs were selective for translation and ignored changes in spatial orientation relative to gravity. Like SSs, tilt modulation of CSs increased at lower frequencies. CSs and SSs had similar response dynamics, responding to linear velocity during translation and angular position during tilt. The most salient finding is that CSs did not always modulate out-of-phase with SSs. The CS/SS phase difference varied broadly among Purkinje cells, yet for each cell it was precisely matched for the otolith-driven and canal-driven components of the response. These findings illustrate a spatiotemporal mismatch between CS/SS pairs and provide the first comprehensive description of the macaque NU, an important step toward understanding how CSs and SSs interact during complex movements and spatial disorientation.

Simulating the shaping of the fastigial deep nuclear saccade command by cerebellar Purkinje cells

Early lesion and physiological studies established the key contributions of the cerebellar cortex and fastigial deep nuclei in maintaining the accuracy of saccades. Recent evidence has demonstrated that fastigial oculomotor region cells (FORCs) provide commands that are critical both for driving and braking saccades. Modeling studies have largely ignored the mechanisms by which the FORC activity patterns, and those of the Purkinje cells (PCs) that inhibit them, are produced by the mossy fiber (MF) inputs common to both. We have created a hybrid network of integrate-and-fire and summation units to model the circuitry between PCs, FORCs, and MFs that can account for all observed PC and FORC activity patterns. The model demonstrates that a crucial component of FORC activity may be due to the rebound depolarization intrinsic to FORC neurons that, like the MF-driven activity of FORCs, is also shaped by PC inhibition and disinhibition. The model further demonstrates that the shaping of the FORC saccade command by PCs can be adaptively modified through plausible learning rules based on cerebellar long-term depression (LTD) and long-term potentiation (LTP), which are guided by climbing fiber (CF) input to PCs that realistically indicates only the direction (but not the magnitude) of saccade error. These modeling results provide new insights into the adaptive control by the cerebellum of the deep nuclear saccade command.

Visual error signals from the pretectal nucleus of the optic tract guide motor learning for smooth pursuit

Smooth pursuit (SP) eye movements are used to maintain the image of a moving object on or near the fovea. Visual motion signals aid in driving SP and are necessary for its adaptation. The sources of visual error signals that support SP adaptation are incompletely understood but could involve neurons in cortical and brain stem areas with direction selective visual motion responses. Here we focus on the pretectal nucleus of the optic tract (NOT), which encodes retinal error information during SP. The aim of this study was to characterize the role of the NOT in SP adaptation. SP adaptation is typically produced using a double step of velocity ramp (double-step paradigm), where target speed either increases or decreases 100 ms after the beginning of a trial. In our study, we delivered a brief (200 ms) train of microelectrical stimulation (ES) in the left NOT to introduce directional error signals at the point in time where a second target speed would appear in a double-step paradigm. The target was extinguished coincidentally with the onset of the ES train. Initial eye acceleration (1st 100 ms) showed significant increases after 100 trials, which included left NOT stimulation during ongoing pursuit in an ipsiversive (leftward) direction. In contrast, initial eye acceleration showed significant decreases after repeated left NOT stimulation during contraversive (rightward) SP. Control studies performed using the same periodicity of NOT stimulation as in the preceding text but without accompanying SP did not induce changes in eye acceleration. In contrast, ES of the NOT paired with active SP produced gradual changes in eye acceleration similar to that observed in double-step paradigm. Therefore our findings support the suggestion that the NOT is an important source of visual error information for guiding motor learning during horizontal SP.

Using input minimization to train a cerebellar model to simulate regulation of smooth pursuit

Cerebellar learning appears to be driven by motor error, but whether or not error signals are provided by climbing fibers (CFs) remains a matter of controversy. Here we show that a model of the cerebellum can be trained to simulate the regulation of smooth pursuit eye movements by minimizing its inputs from parallel fibers (PFs), which carry various signals including error and efference copy. The CF spikes act as "learn now" signals. The model can be trained to simulate the regulation of smooth pursuit of visual objects following circular or complex trajectories and provides insight into how Purkinje cells might encode pursuit parameters. In minimizing both error and efference copy, the model demonstrates how cerebellar learning through PF input minimization (InMin) can make movements more accurate and more efficient. An experimental test is derived that would distinguish InMin from other models of cerebellar learning which assume that CFs carry error signals.

Cerebellum predicts the future motor state

Feed forward control and estimates of the future state of the motor system are critical for fast and coordinated movements. One framework for generating these predictive signals is based on the central nervous system implementing internal models. Internal models provide for representations of the input-output properties of the motor apparatus or their inverses. It has been widely hypothesized that the cerebellum acquires and stores internal models of the motor apparatus. The results of psychophysical, functional imaging and transcranial magnetic stimulation studies in normal subjects support this hypothesis. Also, the deficits in patients with cerebellar dysfunction can be attributed to a failure of predictive feed forward control and/or to accurately estimate the consequences of motor commands. Furthermore, the computation performed by the cerebellar-like electrosensory lobes in several groups of fishes is to predict the sensory consequences of motor commands. However, only a few electrophysiological investigations have directly tested whether neurons in the cerebellar cortex have the requisite signals compatible with either an inverse or forward internal model. Our studies in the monkey performing manual pursuit tracking demonstrate that the simple spike discharge of Purkinje cells does not have the dynamics-related signals required to be the output of an inverse dynamics model. However, Purkinje cell firing has several of the characteristics of a forward internal model of the arm. A synthesis of the evidence suggests that the cerebellum is involved in integrating the current state of the motor system with internally generated motor commands to predict the future state.

Pausing purkinje cells in the cerebellum of the awake cat

A recent controversy has emerged concerning the existence of long pauses, presumably reflecting bistability of membrane potential, in the cerebellar Purkinje cells (PC) of awake animals. It is generally agreed that in the anesthetized animals and in vitro, these cells switch between two stable membrane potential states: a depolarized state (the ‘up-state’) characterized by continuous firing of simple spikes (SS) and a hyperpolarized state (the ‘down-state’) characterized by long pauses in the SS activity. To address the existence of long pauses in the neural activity of cerebellar PCs in the awake and behaving animal we used extracellular recordings in cats and find that approximately half of the recorded PCs exhibit such long pauses in the SS activity and transition between activity – periods with uninterrupted SS lasting an average of 1300 ms – and pauses up to several seconds. We called these cells pausing Purkinje cells (PPC) and they can easily be distinguished from continuous firing Purkinje cells. In most PPCs, state transitions in both directions were often associated (25% of state transitions) with complex spikes (CSs). This is consistent with intracellular findings of CS-driven state transitions. In sum, we present proof for the existence of long pauses in the PC SS activity that probably reflect underlying bistability, provide the first in-depth analysis of these pauses and show for the first time that transitions in and out of these pauses are related to CS firing in the awake and behaving animal.

Adaptive processing in electrosensory systems: links to cerebellar plasticity and learning

The first central stage of electrosensory processing in fish takes place in structures with local circuitry that resembles the cerebellum. Cerebellum-like structures and the cerebellum itself share common patterns of gene expression and may also share developmental and evolutionary origins. Given these similarities it is natural to ask whether insights gleaned from the study of cerebellum-like structures might be useful for understanding aspects of cerebellar function and vice versa. Work from electrosensory systems has shown that cerebellum-like circuitry acts to generate learned predictions about the sensory consequences of the animals' own behavior through a process of associative plasticity at parallel fiber synapses. Subtraction of these predictions from the actual sensory input serves to highlight unexpected and hence behaviorally relevant features. Learning and prediction are also central to many current ideas regarding the function of the cerebellum itself. The present review draws comparisons between cerebellum-like structures and the cerebellum focusing on the properties and sites of synaptic plasticity in these structures and on connections between plasticity and learning. Examples are drawn mainly from the electrosensory lobe (ELL) of mormyrid fish and from extensive work characterizing the role of the cerebellum in Pavlovian eyelid conditioning and vestibulo-ocular reflex (VOR) modification. Parallels with other cerebellum-like structures, including the gymnotid ELL, the elasmobranch dorsal octavolateral nucleus (DON), and the mammalian dorsal cochlear nucleus (DCN) are also discussed.

Neural correlates of predictive and postdictive switching mechanisms for internal models

Switching of sensorimotor tasks may be classified into predictive switching based on contextual information and postdictive switching based on the error between sensorimotor feedback and predictions. We used functional neuroimaging to study the brain regions involved in each type of switching of internal models for visuomotor rotations (clockwise and counterclockwise rotations of visual feedback). The color of a cue presented before movement initiation corresponded to direction of rotation of the feedback in an instructed condition, but not in a noninstructed condition. Switching-related activity was identified as activity that transiently increased after the direction of rotation was changed. The switching-related activity in cue periods in the instructed condition, when a predictive switch is possible, was observed in the superior parietal lobule (SPL). However, the switching-related activity in feedback periods in the noninstructed condition, when prediction error is crucial for the postdictive switch, was observed in the inferior parietal lobule (IPL) and prefrontal cortex. The functional influence of the SPL on the lateral cerebellum, namely, a possible neural correlate for internal models, increased in the instructed condition, but the influence of the IPL on the cerebellum was increased in the noninstructed condition. We observed a rapid activity increase in the instructed condition and a gradual activity increase in the noninstructed condition mainly in the lateral occipito-temporal cortices (LOTC) and supplementary motor cortex (SMA). These results are consistent with separate mechanisms for predictive and postdictive switches and suggest that the LOTC and SMA receive output signals from appropriate internal models.

From 'understanding the brain by creating the brain' towards manipulative neuroscience

Ten years have passed since the Japanese 'Century of the Brain' was promoted, and its most notable objective, the unique 'creating the brain' approach, has led us to apply a humanoid robot as a neuroscience tool. Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by essentially the same principles. I postulate that this 'Understanding the Brain by Creating the Brain' approach is the only way to fully understand neural mechanisms in a rigorous sense. Several humanoid robots and their demonstrations are introduced. A theory of cerebellar internal models and a systems biology model of cerebellar synaptic plasticity is discussed. Both models are experimentally supported, but the latter is more easily verifiable while the former is still controversial. I argue that the major reason for this difference is that essential information can be experimentally manipulated in molecular and cellular neuroscience while it cannot be manipulated at the system level. I propose a new experimental paradigm, manipulative neuroscience, to overcome this difficulty and allow us to prove cause-and-effect relationships even at the system level.

Functions of interneurons in mouse cerebellum

The output signal of Purkinje cells is conveyed by the modulated discharge of simple spikes (SSs) often ascribed to mossy fiber-granule cell-parallel fiber inputs to Purkinje cell dendrites. Although generally accepted, this view lacks experimental support. We can address this view by controlling afferent signals that reach the cerebellum over climbing and mossy fiber pathways. Vestibular primary afferents constitute the largest mossy fiber projection to the uvula-nodulus. The discharge of vestibular primary afferent mossy fibers increases during ipsilateral roll tilt. The discharge of SSs decreases during ipsilateral roll tilt. Climbing fiber discharge [complex spikes (CSs)] increases during ipsilateral roll tilt. These observations suggest that the modulation of SSs during vestibular stimulation cannot be attributed directly to vestibular mossy fiber afferents. Rather we suggest that interneurons driven by vestibular climbing fibers may determine SS modulation. We recorded from cerebellar interneurons (granule, unipolar brush, Golgi, stellate, basket, and Lugaro cells) and Purkinje cells in the uvula-nodulus of anesthetized mice during vestibular stimulation. We identified all neuronal types by juxtacellular labeling with neurobiotin. Granule, unipolar brush, stellate, and basket cells discharge in phase with ipsilateral roll tilt and in phase with CSs. Golgi cells discharge out of phase with ipsilateral roll tilt and out of phase with CSs. The phases of stellate and basket cell discharge suggests that their activity could account for the antiphasic behavior of CSs and SSs. Because Golgi cells discharge in phase with SSs, Golgi cell activity cannot account for SS modulation. The sagittal array of Golgi cell axon terminals suggests that they contribute to the organization of discrete parasagittal vestibular zones.

Horizontal Smooth Pursuit Adaptation in Macaques After Muscimol Inactivation of the Dorsolateral Pontine Nucleus (DLPN)

The smooth pursuit (SP) system can adapt its response to developmental changes, injury, and behavioral context. Previous lesion and single-unit recording studies show that the macaque cerebellum plays a role in SP initiation, maintenance, and adaptation. The aim of this study was to determine the potential role of the DLPN in SP adaptation. The DLPN receives inputs from the cortical SP system and delivers eye and visual motion information to the dorsal/ventral paraflocculus and vermis of the cerebellum. We studied SP adaptation in two juvenile rhesus monkeys ( Macaca mulatta), using double steps of target speed that step- up (10–30°/s) or step-down (25–5°/s). We used microinjection of muscimol (≤2%; 0.15 μl) to reversibly inactivate the DLPN, unilaterally. After DLPN inactivation, initial ipsilesional SP acceleration (first 100 ms) was significantly reduced by 47–74% ( P < 0.01; unpaired t-test) of control values in the single-speed step-ramp paradigm. Similarly, ipsilesional steady-state SP velocity was also reduced by 59–78% ( P < 0.01; unpaired t-test) of control values. Contralesional SP was not impaired after DLPN inactivation. Control testing showed significant adaptive changes of initial SP eye acceleration after 100 trials in either step-up or step-down paradigms. After inactivation, during ipsilesional SP, adaptation was impaired in the step-up but not in the step-down paradigm. In contrast, during contralesional tracking, adaptive capability remained in the step-down but not in the step-up paradigm. Therefore SP adaptation could depend, in part, on direction sensitive eye/visual motion information provided by DLPN neurons to cerebellum.

Increased occurrence of climbing fiber inputs to the cerebellar flocculus in a mutant mouse is correlated with the timing delay of optokinetic response

The cerebellum plays an essential role in motor control, and its dysfunction may delay the onset of action and disrupt smooth and efficient movement. A Purkinje neuron (PN), the sole output cell type in the cerebellar cortex, receives two distinct types of excitatory synaptic inputs, numerous weak inputs from granule neurons (GNs) and occasional strong inputs from a climbing fiber (CF). The role of each input and the significance of low firing rate of CF have been studied. Here we show that the increased occurrence of CF inputs altered the firing pattern of a PN, which was correlated with timing of a reflex. We used the mutant mice deficient in the glutamate receptor delta2 subunit, a molecule related to ionotropic glutamate receptor specifically expressed at GN-PN synapses. The mutant mouse shows more frequent CF inputs and longer timing delay in optokinetic response (OKR), reflex eye movement that follows slow motion of a large visual field. A PN shows two types of action potentials: complex spikes (CS) induced by CF inputs; and simple spikes. They changed respective firing rates during sinusoidal optokinetic stimulation, and the timing of each firing rate modulation was similar in wild-type and mutant mice. However, increased occurrence of CS in the mutant altered the total firing pattern of a PN in the flocculus, which was correlated with the timing delay of OKR. These results support the functional merit of low firing rate of CF in motor control.