A role for the cerebellum in the control of limb movement velocity

Accepting, rejecting or modifying the many different theories of the cerebellum's role in the control of movement requires an understanding of the signals encoded in the discharge of cerebellar neurons and how those signals are transformed by the cerebellar circuitry. Particularly challenging is understanding the sensory and motor signals carried by the two types of action potentials generated by cerebellar Purkinje cells, the simple spikes and complex spikes. Advances have been made in understanding this signal processing in the context of voluntary arm movements. Recent evidence suggests that mossy fiber afferents to the cerebellar cortex are a source of kinematic signals, providing information about movement direction and speed. In turn, the simple spike discharge of Purkinje cells integrates this mossy fiber information to generate a movement velocity signal. Complex spikes may signal errors in movement velocity. It is proposed that the cerebellum uses the signals carried by the simple and complex spike discharges to control movement velocity for both step and tracking arm movements.     

小脑控制不同类型眼动的神经编码机制：从小脑皮层到小脑核团

近年来许多研究发现,小脑作为运动控制的主要脑区,除参与运动控制外也与孤独症、精神分裂症、奖励相关的认知功能和社会行为有关,因此小脑相关研究越来越受到重视。研究小脑参与运动学习和运动控制的神经机制是神经科学中最重要的课题之一。眼睛运动的肌肉协调和生物运动特征比其他类型的运动更简单,这使眼动成为研究小脑在运动控制中作用的理想模型。作为收集外界信息的主要方式之一,视觉对日常生活至关重要。为确保清晰视觉,3种主要类型的眼动（眼跳、平滑追随眼动（SPEM）和注视）需受小脑的精确控制,以确保静止或移动的物体保持在视小凹的中心。异常眼动可导致视力障碍,并可作为诊断各种疾病的临床指标。因此,眼动控制研究具有重要的医学和生物学意义。虽然对小脑皮层和顶核在调节眼动中的作用有基本了解,但眼动动力学编码的确切神经机制,尤其是小脑顶核控制追随眼动和注视的神经机制仍不清楚。本综述总结了目前小脑在运动和认知等方面的主要研究问题与小脑相关研究的潜在应用价值,以及近年来有关小脑控制眼动的相关文献,并深入探讨了利用单细胞记录和线性回归模型分析小脑皮层和顶核同一神经元同时参与控制不同类型的眼动,而不同类型眼动的不同动力学参数编码原则不同。此外,基于检测微眼跳的研究结果,我们讨论了小脑顶核参与控制视觉注视的可能神经机制。最后,讨论了最近技术进步给小脑研究带来的新机遇,为今后与小脑相关的研究和脑控义肢的优化控制（例如通过单独改善运动参数优化义肢控制）提供了新思路。     

Linear systems analysis of the relationship between firing of deep cerebellar neurons and the classically conditioned nictitating membrane response in rabbits

The correlation of the activity of neurons in the interposed and dentate nuclei of the cerebellum with conditioned movements of the nictitating membrane was investigated using linear systems analysis. The activity of single deep cerebellar nuclear cells was assumed to be the input to a linear system that produced nictitating membrane movement. Data were initially analyzed with a causal model to assess the degree to which past neural activity predicted the conditioned response. 55 of 165 cells had correlation coefficients of 0.50 or greater between the model's moment-to-moment output and the actual output, with two interpositus cells having correlation coefficients of greater than 0.90. Double-sided impulse responses indicated that afference from the face and efference copy probably affect deep cerebellar neural activity. Nonlinearities were also found in the relationship between neuronal activity and conditioned movement. It was concluded that cerebellar deep nuclear firing is highly correlated with future nictitating membrane movements but that the firing-movement relationship contains noncausal and nonlinear components.     

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning

 Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike-occurrence time after stimulus onset must capture at least the first, second and third moments of the temporal distribution of error signals. Received: 28 January 2000 / Accepted in revised form: 2 August 2000     

Recurrent cerebellar architecture solves the motor-error problem

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estimate unobservable motor error from its observed sensory consequences. We have proposed elsewhere a recurrent decorrelation control architecture in which Marr-Albus models learn without requiring motor error. Here, we prove convergence for this architecture and demonstrate important advantages for the modular control of systems with multiple degrees of freedom. These results are illustrated by modelling adaptive plant compensation for the three-dimensional vestibular ocular reflex. This provides a functional role for recurrent cerebellar connectivity, which may be a generic anatomical feature of projections between regions of cerebral and cerebellar cortex.     

Analysis of Chaotic Resonance in Izhikevich Neuron Model

In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.     

Information maximization for exploring neural coding in hippocampus and lateral septum

A central problem in neural coding is to understand what are the features of the stimulus that are encoded by the neural activity. Assuming that neuronal coding is optimized for information transmission, we can use mutual information maximization for extracting the relevant features encoded in certain activity patterns. We show that this algorithm can be successfully applied to the study of different encoding strategies for location and direction of movement in hippocampal and lateral septal cells. Using this approach, we find that in lateral septum, a significant amount of information about location can be encoded in patterns that are not place-fields.     

Optimum signal in a simple neuronal model with signal-dependent noise

How does the information about a signal in neural threshold crossings depend on the noise acting upon it? Two models are explored, a binary McCulloch and Pitts (threshold exceedance) model and a model of waiting time to exceedance--a discrete-time version of interspike intervals. If noise grows linearly with the signal, we find the best identification of the signal in terms of the Fisher information is for signals that do not reach the threshold in the absence of noise. Identification attains the same precision under weak and strong signals, but the coding range decreases at both extremes of signal level. We compare the results obtained for Fisher information with those using related first and second moment measures. The maximum obtainable information is plotted as a function of the ratio of noise to signal.     

Functional magnetic resonance signal changes in neural structures to baroreceptor reflex activation.

The sequence of neural responses to exogenous arterial pressure manipulation remains unclear, especially for extramedullary sites. We used functional magnetic resonance imaging procedures to visualize neural responses during pressor (phenylephrine) and depressor (sodium nitroprusside) challenges in seven isoflurane-anesthetized adult cats. Depressor challenges produced signal-intensity declines in multiple cardiovascular-related sites in the medulla, including the nucleus tractus solitarius, and caudal and rostral ventrolateral medulla. Signal decreases also emerged in the cerebellar vermis, inferior olive, dorsolateral pons, and right insula. Rostral sites, such as the amygdala and hypothalamus, increased signal intensity as arterial pressure declined. In contrast, arterial pressure elevation elicited smaller signal increases in medullary regions, the dorsolateral pons, and the right insula and signal declines in regions of the hypothalamus, with no change in deep cerebellar areas. Responses to both pressor and depressor challenges were typically lateralized. In a subset of animals, barodenervation resulted in rises and falls of blood pressure that were comparable to these resulting from the pharmacological challenges but different regional neural responses, indicating that the regional signal intensity responses did not derive from global perfusion effects but from baroreceptor mediation of central mechanisms. The findings demonstrate widespread lateralized distribution of neural sites responsive to blood pressure manipulation. The distribution and time course of neural responses follow patterns associated with early and late compensatory reactions.     

Bridging the brain to the world: a perspective on neural interface systems

Neural interface (NI) systems hold the potential to return lost functions to persons with paralysis. Impressive progress has been made, including evaluation of neural control signals, sensor testing in humans, signal decoding advances, and proof-of-concept validation. Most importantly, the field has demonstrated that persons with paralysis can use prototype systems for spelling, "point and click," and robot control. Human and animal NI research is advancing knowledge about neural information processing and plasticity in healthy, diseased, and injured nervous systems. This emerging field promises a range of neurotechnologies able to return communication, independence, and control to people with movement limitations.     

The cerebellar nodulus/uvula integrates otolith signals for the translational vestibulo-ocular reflex

BACKGROUND: The otolith-driven translational vestibulo-ocular reflex (tVOR) generates compensatory eye movements to linear head accelerations. Studies in humans indicate that the cerebellum plays a critical role in the neural control of the tVOR, but little is known about mechanisms of this control or the functions of specific cerebellar structures. Here, we chose to investigate the contribution of the nodulus and uvula, which have been shown by prior studies to be involved in the processing of otolith signals in other contexts. METHODOLOGY/PRINCIPAL FINDINGS: We recorded eye movements in two rhesus monkeys during steps of linear motion along the interaural axis before and after surgical lesions of the cerebellar uvula and nodulus. The lesions strikingly reduced eye velocity during constant-velocity motion but had only a small effect on the response to initial head acceleration. We fit eye velocity to a linear combination of head acceleration and velocity and to a dynamic mathematical model of the tVOR that incorporated a specific integrator of head acceleration. Based on parameter optimization, the lesion decreased the gain of the pathway containing this new integrator by 62%. The component of eye velocity that depended directly on head acceleration changed little (gain decrease of 13%). In a final set of simulations, we compared our data to the predictions of previous models of the tVOR, none of which could account for our experimental findings. CONCLUSIONS/ SIGNIFICANCE: Our results provide new and important information regarding the neural control of the tVOR. Specifically, they point to a key role for the cerebellar nodulus and uvula in the mathematical integration of afferent linear head acceleration signals. This function is likely to be critical not only for the tVOR but also for the otolith-mediated reflexes that control posture and balance.     

The role of the cerebellum in modulating voluntary limb movement commands

We recorded the activity of cerebellar Purkinje cells (PCs), primary motor cortical (M1) neurons, and limb EMG signals while monkeys executed a sequential reaching and button pressing task. PC simple spike discharge generally correlated well with the activity of one or more forelimb muscles. Surprisingly, given the inhibitory projection of PCs, only about one quarter of the correlations were negative. The largest group of neurons burst during movement and were positively correlated with EMG signals, while another significant group burst and were negatively correlated. Among the PCs that paused during movement most were negatively correlated with EMG. The strength of these various correlations was somewhat weaker, on average, than equivalent correlations between M1 neurons and EMG signals. On the other hand, there were no significant differences in the timing of the onset of movement related discharge among these groups of PCs, or between the PCs and M1 neurons. PC discharge was modulated largely in phase, or directly out of phase, with muscle activity. The nearly synchronous activation of PCs and muscles yielded positive correlations, despite the fact that the synaptic effect of the PC discharge is inhibitory. The apparent function of this inhibition is to restrain activity in the limb premotor network, shaping it into a spatiotemporal pattern that is appropriate for controlling the many muscles that participate in this task. The observed timing suggests that the cerebellar cortex learns to modulate PC discharge predictively. Through the cerebellar nucleus, this PC signal is combined with an underlying cerebral cortical signal. In this manner the cerebellum refines the descending command as compared with the relatively crude version generated when the cerebellum is damaged.     

Signal processing in cerebellar Purkinje cells

Mechanisms and functional implications of signal processing in cerebellar Purkinje cells have been the subject of recent extensive investigations. Complex patterns of their planar dendritic arbor are analysed with computer-aided reconstructions and also topological analyses. Local computation may occur in Purkinje cell dendrites, but its extent is not clear at present. Synaptic transmission and electrical and ionic activity of Purkinje cell membrane have been revealed in detail, and related biochemical processes are being uncovered. A special type of synaptic plasticity is present in Purkinje cell dendrites; long-term depression (LTD) occurs in parallel fiber-Purkinje cell transmission when the parallel fibers are activated with a climbing fiber innervating that Purkinje cell. Evidence indicates that synaptic plasticity in Purkinje cells is due to sustained desensitization of Purkinje dendritic receptors to glutamate, which is a putative neurotransmitter of parallel fibers, and that conjunctive activation of a climbing fiber and parallel fibers leads to desensitization through enhanced intradendritic calcium concentration. A microzone of the cerebellar cortex is connected to an extracerebellar neural system through the inhibitory projection of Purkinje cells to a cerebellar or vestibular nuclear cell group. Climbing fiber afferents convey signals representing control errors in the performance of a neural system, and evoke complex spikes in Purkinje cells of the microzone connected to the neural system. Complex spikes would modify the performance of the microzone by producing LTD in parallel fiber-Purkinje cell synapses, and consequently would improve the overall performance of the neural system. The primary function of the cerebellum thus appears to be endowing adaptability to numerous neural control systems in the brain and spinal cord through error-triggered reorganization of the cerebellar cortical circuitry.     

The multiple roles of Purkinje cells in sensori-motor calibration: to predict, teach and command

Neurophysiological recordings in the cerebellar cortex of awake-behaving animals are revolutionizing the way we think about the role of Purkinje cells in sensori-motor calibration. Early theorists suggested that if a movement became miscalibrated, Purkinje cell output would be changed to adjust the motor command and restore good performance. The finding that Purkinje cell activity changed in many sensori-motor calibration tasks was taken as strong support for this hypothesis. Based on more recent data, however, it has been suggested that changes in Purkinje cell activity do not contribute to the motor command directly; instead, they are used either as a teaching signal, or to predict the altered kinematics of the movement after calibration has taken place. I will argue that these roles are not mutually exclusive, and that Purkinje cells may contribute to command generation, teaching, and prediction at different times during sensori-motor calibration.     

Learning to predict the future: the cerebellum adapts feedforward movement control

The role of the cerebellum in motor control and learning has been largely inferred from the effects of cerebellar damage. Recent work shows that cerebellar damage produces greater impairment of movements that require predictive as opposed to reactive control. This dissociation is consistent across many different types of movement. Predictive control is crucial for fast and ballistic movements, but impaired prediction can also affect slow movements, because of increased reliance on time-delayed feedback signals. The new findings are compatible with theories of cerebellar function, but still do not resolve whether the cerebellum operates by predicting the optimal motor commands or future sensory states. Prediction mechanisms must be learned and maintained through comparisons between predicted and observed outcomes. New results show that not all such error information is equivalent in driving cerebellar learning.     

The roof plate regulates cerebellar cell-type specification and proliferation

During embryogenesis, the isthmic organizer, a well-described signaling center at the junction of the mid-hindbrain, establishes the cerebellar territory along the anterior/posterior axis of the neural tube. Mechanisms specifying distinct populations within the early cerebellar anlage are less defined. Using a newly developed gene expression map of the early cerebellar anlage, we demonstrate that secreted signals from the rhombomere 1 roof plate are both necessary and sufficient for specification of the adjacent cerebellar rhombic lip and its derivative fates. Surprisingly, we show that the roof plate is not absolutely required for initial specification of more distal cerebellar cell fates, but rather regulates progenitor proliferation and cell position within the cerebellar anlage. Thus, in addition to the isthmus, the roof plate represents an important signaling center controlling multiple aspects of cerebellar patterning.     

A computational mechanism for unified gain and timing control in the cerebellum

Precise gain and timing control is the goal of cerebellar motor learning. Because the basic neural circuitry of the cerebellum is homogeneous throughout the cerebellar cortex, a single computational mechanism may be used for simultaneous gain and timing control. Although many computational models of the cerebellum have been proposed for either gain or timing control, few models have aimed to unify them. In this paper, we hypothesize that gain and timing control can be unified by learning of the complete waveform of the desired movement profile instructed by climbing fiber signals. To justify our hypothesis, we adopted a large-scale spiking network model of the cerebellum, which was originally developed for cerebellar timing mechanisms to explain the experimental data of Pavlovian delay eyeblink conditioning, to the gain adaptation of optokinetic response (OKR) eye movements. By conducting large-scale computer simulations, we could reproduce some features of OKR adaptation, such as the learning-related change of simple spike firing of model Purkinje cells and vestibular nuclear neurons, simulated gain increase, and frequency-dependent gain increase. These results suggest that the cerebellum may use a single computational mechanism to control gain and timing simultaneously.     

Ptf1a, a bHLH transcriptional gene, defines GABAergic neuronal fates in cerebellum

The molecular machinery governing glutamatergic-GABAergic neuronal subtype specification is unclear. Here we describe a cerebellar mutant, cerebelless, which lacks the entire cerebellar cortex in adults. The primary defect of the mutant brains was a specific inhibition of GABAergic neuron production from the cerebellar ventricular zone (VZ), resulting in secondary and complete loss of external germinal layer, pontine, and olivary nuclei during development. We identified the responsible gene, Ptf1a, whose expression was lost in the cerebellar VZ but was maintained in the pancreas in cerebelless. Lineage tracing revealed that two types of neural precursors exist in the cerebellar VZ: Ptf1a-expressing and -nonexpressing precursors, which generate GABAergic and glutamatergic neurons, respectively. Introduction of Ptf1a into glutamatergic neuron precursors in the dorsal telencephalon generated GABAergic neurons with representative morphological and migratory features. Our results suggest that Ptf1a is involved in driving neural precursors to differentiate into GABAergic neurons in the cerebellum.     

An internal model architecture for novelty detection: implications for cerebellar and collicular roles in sensory processing

The cerebellum is thought to implement internal models for sensory prediction, but details of the underlying circuitry are currently obscure. We therefore investigated a specific example of internal-model based sensory prediction, namely detection of whisker contacts during whisking. Inputs from the vibrissae in rats can be affected by signals generated by whisker movement, a phenomenon also observable in whisking robots. Robot novelty-detection can be improved by adaptive noise-cancellation, in which an adaptive filter learns a forward model of the whisker plant that allows the sensory effects of whisking to be predicted and thus subtracted from the noisy sensory input. However, the forward model only uses information from an efference copy of the whisking commands. Here we show that the addition of sensory information from the whiskers allows the adaptive filter to learn a more complex internal model that performs more robustly than the forward model, particularly when the whisking-induced interference has a periodic structure. We then propose a neural equivalent of the circuitry required for adaptive novelty-detection in the robot, in which the role of the adaptive filter is carried out by the cerebellum, with the comparison of its output (an estimate of the self-induced interference) and the original vibrissal signal occurring in the superior colliculus, a structure noted for its central role in novelty detection. This proposal makes a specific prediction concerning the whisker-related functions of a region in cerebellar cortical zone A(2) that in rats receives climbing fibre input from the superior colliculus (via the inferior olive). This region has not been observed in non-whisking animals such as cats and primates, and its functional role in vibrissal processing has hitherto remained mysterious. Further investigation of this system may throw light on how cerebellar-based internal models could be used in broader sensory, motor and cognitive contexts.     

A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

In this paper, a brain-computer interface (BCI) system for character recognition is proposed based on the P300 signal. A P300 speller is used to spell the word or character without any muscle movement. P300 detection is the first step to detect the character from the electroencephalogram (EEG) signal. The character is recognized from the detected P300 signal. In this paper, sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) based feature extraction methods are proposed for P300 detection. This work also proposes a fusion of deep-features with the temporal features for P300 detection. A SSAE technique extracts high-level information about input data. The combination of SSAE features with the temporal features provides abstract and temporal information about the signal. An ensemble of weighted artificial neural network (EWANN) is proposed for P300 detection to minimize the variation among different classifiers. To provide more importance to the good classifier for final classification, a higher weightage is assigned to the better performing classifier. These weights are calculated from the cross-validation test. The model is tested on two different publicly available datasets, and the proposed method provides better or comparable character recognition performance than the state-of-the-art methods.