Adaptive filters and internal models: multilevel description of cerebellar function

Roles for cerebellum and subsumption architecture in central pattern generation

Within vertebrates, central pattern generators drive rhythmical behaviours, such as locomotion and ventilation. Their pattern generation is also influenced by sensory input and various forms of neuromodulation. These capabilities arose early in vertebrate evolution, preceding the evolution of the cerebellum in jawed vertebrates. This later evolution of the cerebellum is suggestive of subsumption architecture that adds functionality to a pre-existing network. From a central-pattern-generator perspective, what additional functionality might the cerebellum provide? The suggestion is that the adaptive filter capabilities of the cerebellum may be able to use error learning to appropriately repurpose pattern output. Examples may include head and eye stabilization during locomotion, song learning, and context-dependent alternation between learnt motor-control sequences.

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

A differential Hebbian framework for biologically-plausible motor control

In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive them. This selection happens through a family of differential Hebbian learning rules that, through interaction with the environment, can learn to control systems where the error responds monotonically to the control signal. We next show that in a more general case, neural reinforcement learning can be coupled with a feedback controller to reduce errors that arise non-monotonically from the control signal. The use of feedback control can reduce the complexity of the reinforcement learning problem, because only a desired value must be learned, with the controller handling the details of how it is reached. This makes the function to be learned simpler, potentially allowing learning of more complex actions. We use simple examples to illustrate our approach, and discuss how it could be extended to hierarchical architectures.

Sensory, somatomotor and internal mentation networks emerge dynamically in the resting brain with internal mentation predominating in older age

Age-related changes in the brain are associated with a decline in functional flexibility. Intrinsic functional flexibility is evident in the brain's dynamic ability to switch between alternative spatiotemporal states during resting state. However, the relationship between brain connectivity states, associated psychological functions during resting state, and the changes in normal aging remain poorly understood. In this study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP; N = 812) and the UK Biobank (UKB; N = 6,716). Using signed community clustering to identify distinct states of dynamic functional connectivity, and text-mining of a large existing literature for functional annotation of each state, our findings from the HCP dataset indicated that the resting brain spontaneously transitions between three functionally specialized states: sensory, somatomotor, and internal mentation networks. The occurrence, transition-rate, and persistence-time parameters for each state were correlated with behavioural scores using canonical correlation analysis. We estimated the same brain states and parameters in the UKB dataset, subdivided into three distinct age ranges: 50-55, 56-67, and 68-78 years. We found that the internal mentation network was more frequently expressed in people aged 71 and older, whereas people younger than 55 more frequently expressed sensory and somatomotor networks. Furthermore, analysis of the functional entropy - a measure of uncertainty of functional connectivity - also supported this finding across the three age ranges. Our study demonstrates that dynamic functional connectivity analysis can expose the time-varying patterns of transition between functionally specialized brain states, which are strongly tied to increasing age.

A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks

While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behavior. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behavior observed in human reaching experiments.

Multiple behavior-specific cancellation signals contribute to suppressing predictable sensory reafference in a cerebellum-like structure

Movement induces sensory stimulation of an animal's own sensory receptors, termed reafference. With a few exceptions, notably vestibular and proprioception, this reafference is unwanted sensory noise and must be selectively filtered in order to detect relevant external sensory signals. In the cerebellum-like electrosensory nucleus of elasmobranch fish, an adaptive filter preserves novel signals by generating cancellation signals that suppress predictable reafference. A parallel fiber network supplies the principal Purkinje-like neurons (called ascending efferent neurons, AENs) with behavior-associated internal reference signals, including motor corollary discharge and sensory feedback, from which predictive cancellation signals are formed. How distinct behavior-specific cancellation signals interact within AENs when multiple behaviors co-occur and produce complex, changing patterns of reafference is unknown. Here, we show that when multiple streams of internal reference signals are available, cancellation signals form that are specific to parallel fiber inputs temporally correlated with, and therefore predictive of, sensory reafference. A single AEN has the capacity to form more than one cancellation signal, and AENs form multiple cancellation signals simultaneously and modify them independently during co-occurring behaviors. Cancellation signals update incrementally during continuous behaviors, as well as episodic bouts of behavior that last minutes to hours. Finally, individual AENs, independently of their neighbors, form unique AEN-specific cancellation signals that depend on the particular sensory reafferent input it receives. Together, these results demonstrate the capacity of the adaptive filter to utilize multiple cancellation signals to suppress dynamic patterns of reafference arising from complex co-occurring and intermittently performed behaviors.

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing

The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.

Cerebellar patients have intact feedback control that can be leveraged to improve reaching

It is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here, we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.

A Patterned Architecture of Monoaminergic Afferents in the Cerebellar Cortex: Noradrenergic and Serotonergic Fibre Distributions within Lobules and Parasagittal Zones

The geometry of the glutamatergic mossy-parallel fibre and climbing fibre inputs to cerebellar cortical Purkinje cells has powerfully influenced thinking about cerebellar functions. The compartmentation of the cerebellum into parasagittal zones, identifiable in olivo-cortico-nuclear projections, and the trajectories of the parallel fibres, transverse to these zones and following the long axes of the cortical folia, are particularly important. Two monoaminergic afferent systems, the serotonergic and noradrenergic, are major inputs to the cerebellar cortex but their architecture and relationship with the cortical geometry are poorly understood. Immunohistochemistry for the serotonin transporter (SERT) and for the noradrenaline transporter (NET) revealed strong anisotropy of these afferent fibres in the molecular layer of rat cerebellar cortex. Individual serotonergic fibres travel predominantly medial-lateral, along the long axes of the cortical folia, similar to parallel fibres and Zebrin II immunohistochemistry revealed that they can influence multiple zones. In contrast, individual noradrenergic fibres run predominantly parasagittally with rostral-caudal extents significantly longer than their medial-lateral deviations. Their local area of influence has similarities in form and size to those of identified microzones. Within the molecular layer, the orthogonal trajectories of these two afferent systems suggest different information processing. An individual serotonergic fibre must influence all zones and microzones within its medial-lateral trajectory. In contrast, noradrenergic fibres can influence smaller cortical territories, potentially as limited as a microzone. Evidence is emerging that these monoaminergic systems may not supply a global signal to all of their targets and their potential for cerebellar cortical functions is discussed.

Problems in Audiovisual Filtering for Children with Special Educational Needs

There is pervasive evidence that problems in sensory processing occur across a range of developmental disorders, but their aetiology and clinical significance remain unclear. The present study investigated the relation between sensory processing and literacy skills in children with and without a background of special educational needs (SEN). Twenty-six children aged between 7 and 12 years old, from both regular classes and SEN programmes, participated. Following baseline tests of literacy, fine motor skills and naming speed, two sets of instruments were administered: the carer-assessed Child Sensory Profile-2 and a novel Audiovisual Animal Stroop (AVAS) test. The SEN group showed significantly higher ratings on three Child Sensory Profile-2 quadrants, together with body position ratings. The SEN participants also showed a specific deficit when required to ignore an accompanying incongruent auditory stimulus on the AVAS. Interestingly, AVAS performance correlated significantly with literacy scores and with the sensory profile scores. It is proposed that the children with SEN showed a specific deficit in "filtering out" irrelevant auditory input. The results highlight the importance of including analysis of sensory processes within theoretical and applied approaches to developmental differences and suggest promising new approaches to the understanding, assessment, and support of children with SEN.

Tutorial Review of Bio-Inspired Approaches to Robotic Manipulation for Space Debris Salvage

We present a comprehensive tutorial review that explores the application of bio-inspired approaches to robot control systems for grappling and manipulating a wide range of space debris targets. Current robot manipulator control systems exploit limited techniques which can be supplemented by additional bio-inspired methods to provide a robust suite of robot manipulation technologies. In doing so, we review bio-inspired control methods because this will be the key to enabling such capabilities. In particular, force feedback control may be supplemented with predictive forward models and software emulation of viscoelastic preflexive joint behaviour. This models human manipulation capabilities as implemented by the cerebellum and muscles/joints respectively. In effect, we are proposing a three-level control strategy based on biomimetic forward models for predictive estimation, traditional feedback control and biomimetic muscle-like preflexes. We place emphasis on bio-inspired forward modelling suggesting that all roads lead to this solution for robust and adaptive manipulator control. This promises robust and adaptive manipulation for complex tasks in salvaging space debris.

World Statistics Drive Learning of Cerebellar Internal Models in Adaptive Feedback Control: A Case Study Using the Optokinetic Reflex

The cerebellum is widely implicated in having an important role in adaptive motor control. Many of the computational studies on cerebellar motor control to date have focused on the associated architecture and learning algorithms in an effort to further understand cerebellar function. In this paper we switch focus to the signals driving cerebellar adaptation that arise through different motor behavior. To do this, we investigate computationally the contribution of the cerebellum to the optokinetic reflex (OKR), a visual feedback control scheme for image stabilization. We develop a computational model of the adaptation of the cerebellar response to the world velocity signals that excite the OKR (where world velocity signals are used to emulate head velocity signals when studying the OKR in head-fixed experimental laboratory conditions). The results show that the filter learnt by the cerebellar model is highly dependent on the power spectrum of the colored noise world velocity excitation signal. Thus, the key finding here is that the cerebellar filter is determined by the statistics of the OKR excitation signal.

A Neuroanatomically Grounded Optimal Control Model of the Compensatory Eye Movement System in Mice

We present a working model of the compensatory eye movement system in mice. We challenge the model with a data set of eye movements in mice (n =34) recorded in 4 different sinusoidal stimulus conditions with 36 different combinations of frequency (0.1-3.2 Hz) and amplitude (0.5-8°) in each condition. The conditions included vestibular stimulation in the dark (vestibular-ocular reflex, VOR), optokinetic stimulation (optokinetic reflex, OKR), and two combined visual/vestibular conditions (the visual-vestibular ocular reflex, vVOR, and visual suppression of the VOR, sVOR). The model successfully reproduced the eye movements in all conditions, except for minor failures to predict phase when gain was very low. Most importantly, it could explain the interaction of VOR and OKR when the two reflexes are activated simultaneously during vVOR stimulation. In addition to our own data, we also reproduced the behavior of the compensatory eye movement system found in the existing literature. These include its response to sum-of-sines stimuli, its response after lesions of the nucleus prepositus hypoglossi or the flocculus, characteristics of VOR adaptation, and characteristics of drift in the dark. Our model is based on ideas of state prediction and forward modeling that have been widely used in the study of motor control. However, it represents one of the first quantitative efforts to simulate the full range of behaviors of a specific system. The model has two separate processing loops, one for vestibular stimulation and one for visual stimulation. Importantly, state prediction in the visual processing loop depends on a forward model of residual retinal slip after vestibular processing. In addition, we hypothesize that adaptation in the system is primarily adaptation of this model. In other words, VOR adaptation happens primarily in the OKR loop.

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

The cerebellum, which is responsible for motor control and learning, has been suggested to act as a Smith predictor for compensation of time-delays by means of internal forward models. However, insights about how forward model predictions are integrated in the Smith predictor have not yet been unveiled. To fill this gap, a novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed. The goal is to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes. The outcome of the proposed architecture with other two control schemes that do not include the Smith control strategy or the cerebellar-like corrections are compared. The results obtained on four sets of experiments confirm that the cerebellum-like circuit provides more effective corrections when only the Smith strategy is adopted and that minor tuning in the parameters, fast adaptation and reproducible configuration are enabled.

Cerebellar Prediction of the Dynamic Sensory Consequences of Gravity

As we go about our everyday activities, our brain computes accurate estimates of both our motion relative to the world and our orientation relative to gravity. However, how the brain then accounts for gravity as we actively move and interact with our environment is not yet known. Here, we provide evidence that, although during passive movements, individual cerebellar output neurons encode representations of head motion and orientation relative to gravity, these gravity-driven responses are cancelled when head movement is a consequence of voluntary generated movement. In contrast, the gravity-driven responses of primary otolith and semicircular canal afferents remain intact during both active and passive self-motion, indicating the attenuated responses of central neurons are not inherited from afferent inputs. Taken together, our results are consistent with the view that the cerebellum builds a dynamic prediction (e.g., internal model) of the sensory consequences of gravity during active self-motion, which in turn enables the preferential encoding of unexpected motion to ensure postural and perceptual stability.

Sensorimotor maps can be dynamically calibrated using an adaptive-filter model of the cerebellum

Substantial experimental evidence suggests the cerebellum is involved in calibrating sensorimotor maps. Consistent with this involvement is the well-known, but little understood, massive cerebellar projection to maps in the superior colliculus. Map calibration would be a significant new role for the cerebellum given the ubiquity of map representations in the brain, but how it could perform such a task is unclear. Here we investigated a dynamic method for map calibration, based on electrophysiological recordings from the superior colliculus, that used a standard adaptive-filter cerebellar model. The method proved effective for complex distortions of both unimodal and bimodal maps, and also for predictive map-based tracking of moving targets. These results provide the first computational evidence for a novel role for the cerebellum in dynamic sensorimotor map calibration, of potential importance for coordinate alignment during ongoing motor control, and for map calibration in future biomimetic systems. This computational evidence also provides testable experimental predictions concerning the role of the connections between cerebellum and superior colliculus in previously observed dynamic coordinate transformations.

The human brain contains a structure known as the cerebellum, which contains a vast number of neurons–around 80% of the total ~90 billion. We believe the cerebellum is involved in learning motor skills, and so is vitally important for accurately controlling the movements of our body, amongst other things. However, like most regions of the brain, we still do not fully understand the role of the cerebellum and evidence for new roles is appearing all the time. One such new role is in the calibration of sensorimotor maps in the brain that link our sensory perception to motor function, such as when a visual stimulus causes a redirect of our gaze. We investigated this problem by connecting a mathematical model of the cerebellar cortical microcircuit to simulated sensory maps in the superior colliculus that are used to control orienting movements. We found the error signal generated by inaccurate orienting movements could be used to accurately calibrate sensorimotor maps, and to allow predictive tracking of moving targets. This finding points to a potentially widespread role for the cerebellum in calibrating the sensorimotor maps that are ubiquitous in the brain and could prove useful in controlling the movements of multi-joint robots.

Computational Principles of Supervised Learning in the Cerebellum

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input-output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Vestibular processing during natural self-motion: implications for perception and action

How the brain computes accurate estimates of our self-motion relative to the world and our orientation relative to gravity in order to ensure accurate perception and motor control is a fundamental neuroscientific question. Recent experiments have revealed that the vestibular system encodes this information during everyday activities using pathway-specific neural representations. Furthermore, new findings have established that vestibular signals are selectively combined with extravestibular information at the earliest stages of central vestibular processing in a manner that depends on the current behavioural goal. These findings have important implications for our understanding of the brain mechanisms that ensure accurate perception and behaviour during everyday activities and for our understanding of disorders of vestibular processing.

The Macaque Cerebellar Flocculus Outputs a Forward Model of Eye Movement

The central nervous system (CNS) achieves fine motor control by generating predictions of the consequences of the motor command, often called forward models of the movement. These predictions are used centrally to detect not-self generated sensations, to modify ongoing movements, and to induce motor learning. However, finding a neuronal correlate of forward models has proven difficult. In the oculomotor system, we can identify neuronal correlates of forward models vs. neuronal correlates of motor commands by examining neuronal responses during smooth pursuit at eccentric eye positions. During pursuit, torsional eye movement information is not present in the motor command, but it is generated by the mechanic of the orbit. Importantly, the directionality and approximate magnitude of torsional eye movement follow the half angle rule. We use this rule to investigate the role of the cerebellar flocculus complex (FL, flocculus and ventral paraflocculus) in the generation of forward models of the eye. We found that mossy fibers (input elements to the FL) did not change their response to pursuit with eccentricity. Thus, they do not carry torsional eye movement information. However, vertical Purkinje cells (PCs; output elements of the FL) showed a preference for counter-clockwise (CCW) eye velocity [corresponding to extorsion (outward rotation) of the ipsilateral eye]. We hypothesize that FL computes an estimate of torsional eye movement since torsion is present in PCs but not in mossy fibers. Overall, our results add to those of other laboratories in supporting the existence in the CNS of a predictive signal constructed from motor command information.

Motor context dominates output from purkinje cell functional regions during reflexive visuomotor behaviours

The cerebellum integrates sensory stimuli and motor actions to enable smooth coordination and motor learning. Here we harness the innate behavioral repertoire of the larval zebrafish to characterize the spatiotemporal dynamics of feature coding across the entire Purkinje cell population during visual stimuli and the reflexive behaviors that they elicit. Population imaging reveals three spatially-clustered regions of Purkinje cell activity along the rostrocaudal axis. Complementary single-cell electrophysiological recordings assign these Purkinje cells to one of three functional phenotypes that encode a specific visual, and not motor, signal via complex spikes. In contrast, simple spike output of most Purkinje cells is strongly driven by motor-related tail and eye signals. Interactions between complex and simple spikes show heterogeneous modulation patterns across different Purkinje cells, which become temporally restricted during swimming episodes. Our findings reveal how sensorimotor information is encoded by individual Purkinje cells and organized into behavioral modules across the entire cerebellum.

Control of a muscle-like soft actuator via a bioinspired approach

Soft actuators have played an indispensable role in generating compliant motions of soft robots. Among the various soft actuators explored for soft robotic applications, dielectric elastomer actuators (DEAs) have caught the eye with their intriguing attributes similar to biological muscles. However, the control challenge of DEAs due to their strong nonlinear behaviors has hindered the development of DEA-based soft robots. To overcome the control challenge, this paper proposes a bioinspired control approach of DEAs. A three-dimensional muscle-like DEA, capable of large forces and giant deformation, is fabricated and adopted as the control platform. To facilitate the controller design, the dynamic model of the DEA is developed through experimental analysis, which takes electromechanical coupling, viscoelastic effects and dynamics uncertainties into consideration. Motivated by the proprioception of the biological muscles, the self-sensing capability of the actuator is explored and exhibits good accuracy. Thus the self-sensing of the actuator is utilized to provide the sensory feedback in the control loop without the need of additional external sensors. Inspired from the role of the cerebellum in motor learning, a cerebellum model articulation nonlinear controller is proposed to compensate the dynamics uncertainties and to provide motion correction. Finally, the effectiveness of the proposed control approach is verified by both the simulation and the experiments.

Reconstructing the Neanderthal brain using computational anatomy

The present study attempted to reconstruct 3D brain shape of Neanderthals and early Homo sapiens based on computational neuroanatomy. We found that early Homo sapiens had relatively larger cerebellar hemispheres but a smaller occipital region in the cerebrum than Neanderthals long before the time that Neanderthals disappeared. Further, using behavioural and structural imaging data of living humans, the abilities such as cognitive flexibility, attention, the language processing, episodic and working memory capacity were positively correlated with size-adjusted cerebellar volume. As the cerebellar hemispheres are structured as a large array of uniform neural modules, a larger cerebellum may possess a larger capacity for cognitive information processing. Such a neuroanatomical difference in the cerebellum may have caused important differences in cognitive and social abilities between the two species and might have contributed to the replacement of Neanderthals by early Homo sapiens.

Reconstructing the Neanderthal brain using computational anatomy

The present study attempted to reconstruct 3D brain shape of Neanderthals and early Homo sapiens based on computational neuroanatomy. We found that early Homo sapiens had relatively larger cerebellar hemispheres but a smaller occipital region in the cerebrum than Neanderthals long before the time that Neanderthals disappeared. Further, using behavioural and structural imaging data of living humans, the abilities such as cognitive flexibility, attention, the language processing, episodic and working memory capacity were positively correlated with size-adjusted cerebellar volume. As the cerebellar hemispheres are structured as a large array of uniform neural modules, a larger cerebellum may possess a larger capacity for cognitive information processing. Such a neuroanatomical difference in the cerebellum may have caused important differences in cognitive and social abilities between the two species and might have contributed to the replacement of Neanderthals by early Homo sapiens.

The mystery of the cerebellum: clues from experimental and clinical observations

The cerebellum has a striking homogeneous cytoarchitecture and participates in both motor and non-motor domains. Indeed, a wealth of evidence from neuroanatomical, electrophysiological, neuroimaging and clinical studies has substantially modified our traditional view on the cerebellum as a sole calibrator of sensorimotor functions. Despite the major advances of the last four decades of cerebellar research, outstanding questions remain regarding the mechanisms and functions of the cerebellar circuitry. We discuss major clues from both experimental and clinical studies, with a focus on rodent models in fear behaviour, on the role of the cerebellum in motor control, on cerebellar contributions to timing and our appraisal of the pathogenesis of cerebellar tremor. The cerebellum occupies a central position to optimize behaviour, motor control, timing procedures and to prevent body oscillations. More than ever, the cerebellum is now considered as a major actor on the scene of disorders affecting the CNS, extending from motor disorders to cognitive and affective disorders. However, the respective roles of the mossy fibres, the climbing fibres, cerebellar cortex and cerebellar nuclei remains unknown or partially known at best in most cases. Research is now moving towards a better definition of the roles of cerebellar modules and microzones. This will impact on the management of cerebellar disorders.

Movement Rate Is Encoded and Influenced by Widespread, Coherent Activity of Cerebellar Molecular Layer Interneurons

Inhibition from molecular layer interneurons (MLIs) is thought to play an important role in cerebellar function by sharpening the precision of Purkinje cell spike output. Yet the coding features of MLIs during behavior are poorly understood. To study MLI activity, we used in vivo Ca²⁺ imaging in head-fixed mice during the performance of a rhythmic motor behavior, licking during water consumption. MLIs were robustly active during lick-related movement across a lobule-specific region of the cerebellum showing high temporal correspondence within their population. Average MLI Ca²⁺ activity strongly correlated with movement rate but not to the intentional, or unexpected, adjustment of lick position or to sensory feedback that varied with task condition. Chemogenetic suppression of MLI output reduced lick rate and altered tongue movements, indicating that activity of these interneurons not only encodes temporal aspects of movement kinematics but also influences motor outcome pointing to an integral role in online control of rhythmic behavior.SIGNIFICANCE STATEMENT The cerebellum helps fine-tune coordinated motor actions via signaling from projection neurons called Purkinje cells. Molecular layer interneurons (MLIs) provide powerful inhibition onto Purkinje cells, but little is understood about how this inhibitory circuit is engaged during behavior or what type of information is transmitted through these neurons. Our work establishes that MLIs in the lateral cerebellum are broadly activated during movement with calcium activity corresponding to movement rate. We also show that suppression of MLI output slows and disorganizes the precise movement pattern. Therefore, MLIs are an important circuit element in the cerebellum allowing for accurate motor control.

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.

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle

Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.

Quantitative Classification of Cerebellar Foliation in Cartilaginous Fishes (Class: Chondrichthyes) Using Three-Dimensional Shape Analysis and Its Implications for Evolutionary Biology

A true cerebellum appeared at the onset of the chondrichthyan (sharks, batoids, and chimaerids) radiation and is known to be essential for executing fast, accurate, and efficient movement. In addition to a high degree of variation in size, the corpus cerebellum in this group has a high degree of variation in convolution (or foliation) and symmetry, which ranges from a smooth cerebellar surface to deep, branched convexities and folds, although the functional significance of this trait is unclear. As variation in the degree of foliation similarly exists throughout vertebrate evolution, it becomes critical to understand this evolutionary process in a wide variety of species. However, current methods are either qualitative and lack numerical rigor or they are restricted to two dimensions. In this paper, a recently developed method for the characterization of shapes embedded within noisy, three-dimensional data called spherical wave decomposition (SWD) is applied to the problem of characterizing cerebellar foliation in cartilaginous fishes. The SWD method provides a quantitative characterization of shapes in terms of well-defined mathematical functions. An additional feature of the SWD method is the construction of a statistical criterion for the optimal fit, which represents the most parsimonious choice of parameters that fits to the data without overfitting to background noise. We propose that this optimal fit can replace a previously described qualitative visual foliation index (VFI) in cartilaginous fishes with a quantitative analog, i.e. the cerebellar foliation index (CFI). The capability of the SWD method is demonstrated in a series of volumetric images of brains from different chondrichthyan species that span the range of foliation gradings currently described for this group. The CFI is consistent with the qualitative grading provided by the VFI, delivers a robust measure of cerebellar foliation, and can provide a quantitative basis for brain shape characterization across taxa.

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

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.

Multiple timescales in the adaptation of the rotational VOR

Goal-directed movements, such as pointing and saccades, have been shown to share similar neural architectures, in spite of the different neuromuscular systems producing them. Such structure involve an inverse model of the actuator being controlled, which produces the commands innervating the muscles, and a forward model of the actuator, which predicts the sensory consequences of such commands and allows online movement corrections. Recent studies have shown that goal-directed movements also share similar motor-learning and motor-memory mechanisms, which are based on multiple timescales. The hypothesis that also the rotational vestibulo-ocular reflex (rVOR) may be based on a similar architecture has been presented recently. We hypothesize that multiple timescales are the brain's solution to the plasticity-stability dilemma, allowing adaptation to temporary and sudden changes while keeping stable motor-control abilities. If that were the case, then we would also expect the adaptation of reflex movements to follow the same principles. Thus we studied rVOR gain adaptation in eight healthy human subjects using a custom paradigm aimed at investigating the existence of spontaneous recovery, which we considered as the hallmark of multiple timescales in motor learning. Our experimental results show that spontaneous recovery occurred in six of eight subjects. Thus we developed a mathematical model of rVOR adaptation based on two hidden-states processes, which adapts the cerebellar-forward model of the ocular motor plant, and show that it accurately simulates our experimental data on rVOR gain adaptation, whereas a single timescale learning process fails to do so.

How the cerebellum may monitor sensory information for spatial representation

The cerebellum has already been shown to participate in the navigation function. We propose here that this structure is involved in maintaining a sense of direction and location during self-motion by monitoring sensory information and interacting with navigation circuits to update the mental representation of space. To better understand the processing performed by the cerebellum in the navigation function, we have reviewed: the anatomical pathways that convey self-motion information to the cerebellum; the computational algorithm(s) thought to be performed by the cerebellum from these multi-source inputs; the cerebellar outputs directed toward navigation circuits and the influence of self-motion information on space-modulated cells receiving cerebellar outputs. This review highlights that the cerebellum is adequately wired to combine the diversity of sensory signals to be monitored during self-motion and fuel the navigation circuits. The direct anatomical projections of the cerebellum toward the head-direction cell system and the parietal cortex make those structures possible relays of the cerebellum influence on the hippocampal spatial map. We describe computational models of the cerebellar function showing that the cerebellum can filter out the components of the sensory signals that are predictable, and provides a novelty output. We finally speculate that this novelty output is taken into account by the navigation structures, which implement an update over time of position and stabilize perception during navigation.

Decorrelation learning in the cerebellum: computational analysis and experimental questions

Many cerebellar models use a form of synaptic plasticity that implements decorrelation learning. Parallel fibers carrying signals positively correlated with climbing-fiber input have their synapses weakened (long-term depression), whereas those carrying signals negatively correlated with climbing input have their synapses strengthened (long-term potentiation). Learning therefore ceases when all parallel-fiber signals have been decorrelated from climbing-fiber input. This is a computationally powerful rule for supervised learning and can be cast in a spike-timing dependent plasticity form for comparison with experimental evidence. Decorrelation learning is particularly well suited to sensory prediction, for example, in the reafference problem where external sensory signals are interfered with by reafferent signals from the organism's own movements, and the required circuit appears similar to the one found to mediate classical eye blink conditioning. However, for certain stimuli, avoidance is a much better option than simple prediction, and decorrelation learning can also be used to acquire appropriate avoidance movements. One example of a stimulus to be avoided is retinal slip that degrades visual processing, and decorrelation learning appears to play a role in the vestibulo-ocular reflex that stabilizes gaze in the face of unpredicted head movements. Decorrelation learning is thus suitable for both sensory prediction and motor control. It may also be well suited for generic spatial and temporal coordination, because of its ability to remove the unwanted side effects of movement. Finally, because it can be used with any kind of time-varying signal, the cerebellum could play a role in cognitive processing.

An adaptive filter model of cerebellar zone C3 as a basis for safe limb control?

The review asks how the adaptive filter model of the cerebellum might be relevant to experimental work on zone C3, one of the most extensively studied regions of cerebellar cortex. As far as features of the cerebellar microcircuit are concerned, the model appears to fit very well with electrophysiological discoveries concerning the importance of molecular layer interneurons and their plasticity, the significance of long-term potentiation and the striking number of silent parallel fibre synapses. Regarding external connectivity and functionality, a key feature of the adaptive filter model is its use of the decorrelation algorithm, which renders it uniquely suited to problems of sensory noise cancellation. However, this capacity can be extended to the avoidance of sensory interference, by appropriate movements of, for example, the eyes in the vestibulo-ocular reflex. Avoidance becomes particularly important when painful signals are involved, and as the climbing fibre input to zone C3 is extremely responsive to nociceptive stimuli, it is proposed that one function of this zone is the avoidance of pain by, for example, adjusting movements of the body to avoid self-harm. This hypothesis appears consistent with evidence from humans and animals concerning the role of the intermediate cerebellum in classically conditioned withdrawal reflexes, but further experiments focusing on conditioned avoidance are required to test the hypothesis more stringently. The proposed architecture may also be useful for automatic self-adjusting damage avoidance in robots, an important consideration for next generation 'soft' robots designed to interact with people.