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

Concurrent vestibular activation and postural training recalibrate somatosensory, vestibular and gaze stabilization processes

Postural instability is a common symptom of vestibular dysfunction that impacts a person's day-to-day activities. Vestibular rehabilitation is effective in decreasing dizziness, visual symptoms and improving postural control through several mechanisms including sensory reweighting of the vestibular, visual and somatosensory systems. As part of the sensory reweighting mechanisms, vestibular activation exercises with headshaking influence vestibular-ocular reflex (VOR). However, combining challenging vestibular and postural tasks to facilitate more effective rehabilitation outcomes is under-utilized. Understanding how and why this may work is unknown. The aim of the study was to assess sensory reweighting of postural control processing and VOR after concurrent vestibular activation and weight shift training (WST) in healthy young adults. Forty-two participants (18-35years) were randomly assigned into four groups: No training/control (CTL), a novel visual feedback WST coupled with a concurrent, rhythmic active horizontal or vertical headshake activity (HHS and VHS), or the same WST with no headshake (NHS). Training was performed for five days. All groups performed baseline- and post-assessments using the video head impulse test, sensory organization test, force platform rotations and electro-oculography. Significantly decreased horizontal eye movement variability in the HHS group compared to the other groups suggests improved gaze stabilization (p = .024). Significantly decreased horizontal VOR gain (p = .040) and somatosensory downweighting (p = .050) were found in the combined headshake groups (HHS and VHS) compared to the other two groups (NHS and CTL). The training also showed a significantly faster automatic postural response (p = .003) with improved flexibility (p = .010) in the headshake groups. The concurrent training influences oculomotor function and suggests improved gaze stabilization through vestibular recalibration due to adaptation and possibly habituation. The novel protocol could be modified into progressive functional activities that would incorporate gaze stabilization exercises. The findings may have implications for future development of vestibular rehabilitation protocols.

Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control

The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and synaptic plasticity mechanisms may allow us to analyze cerebellar information processing in the real world by applying it to adaptive control of actual machines. Several artificial cerebellums have previously been constructed, but they require high-performance hardware to run in real-time for real-world machine control. Presently, we implemented an artificial cerebellum with the size of 104 spiking neuron models on a field-programmable gate array (FPGA) which is compact, lightweight, portable, and low-power-consumption. In the implementation three novel techniques are employed: (1) 16-bit fixed-point operation and randomized rounding, (2) fully connected spike information transmission, and (3) alternative memory that uses pseudo-random number generators. We demonstrate that the FPGA artificial cerebellum runs in real-time, and its component neuron models behave as those in the corresponding artificial cerebellum configured on a personal computer in Python. We applied the FPGA artificial cerebellum to the adaptive control of a machine in the real world and demonstrated that the artificial cerebellum is capable of adaptively reducing control error after sudden load changes. This is the first implementation and demonstration of a spiking artificial cerebellum on an FPGA applicable to real-world adaptive control. The FPGA artificial cerebellum may provide neuroscientific insights into cerebellar information processing in adaptive motor control and may be applied to various neuro-devices to augment and extend human motor control capabilities.

Effects of proprioception and core stability training on gait parameters of deaf adolescents: a randomized controlled trial

The current study aimed to explore the effects of proprioception versus core stability training over 8 weeks on the gait parameters of deaf adolescents. A total of 20 deaf adolescents were randomized into two groups: one group receiving proprioception training (PT, n = 10), another group receiving core stability training (CST, n = 10), and eleven typically developing adolescents assigned into the control group (CON; n = 11). Gait was recorded by two digital cameras; then, using the Kinovea software, the parameters of gait included: gait velocity, cadence, stride length, stride time, stance time, and swing time were calculated in terms of percentages of the walking cycle. After 8 weeks of PT, no significant differences were observed for all gait parameters between PT and control groups (p > 0.05). Also, after 8 weeks of CST, no significant differences were observed in gait velocity and cadence between the CST and control groups (p > 0.05). However, after 8 weeks of CST, stride length (p = 0.02) was higher in the control group; Stride time (p = 0.03), stance time (p = 0.04) and swing time (p = 0.04) were higher in the CST group. Moreover, after 8 weeks of PT, values showed significant improvements in all gait parameters (p = 0.001). Also, after 8 weeks of CST, values showed significant improvements in gait velocity and cadence (p = 0.001), but no significant differences were observed in other gait parameters (p > 0.05). The findings of this study indicated that PT improved all gait parameters, whereas CST improved gait velocity and cadence. The results of the present study also demonstrated that PT had a greater effect on gait parameters of deaf adolescents compared with CST. It seems that PT induces more training effects than CTS for enhancing gait parameters of deaf adolescents.Trial registration: Clinical trial registry number: IRCT20170312033029N2. URL: https://en.irct.ir/trial/25584 .

Internal models of self-motion: neural computations by the vestibular cerebellum

The vestibular cerebellum plays an essential role in maintaining our balance and ensuring perceptual stability during activities of daily living. Here I examine three key regions of the vestibular cerebellum: the floccular lobe, anterior vermis (lobules I-V), and nodulus and ventral uvula (lobules X-IX of the posterior vermis). These cerebellar regions encode vestibular information and combine it with extravestibular signals to create internal models of eye, head, and body movements, as well as their spatial orientation with respect to gravity. To account for changes in the external environment and/or biomechanics during self-motion, the neural mechanisms underlying these computations are continually updated to ensure accurate motor behavior. To date, studies on the vestibular cerebellum have predominately focused on passive vestibular stimulation, whereas in actuality most stimulation is the result of voluntary movement. Accordingly, I also consider recent research exploring these computations during active self-motion and emerging evidence establishing the cerebellum's role in building predictive models of self-generated movement.

A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction

Spiking neural networks were introduced to understand spatiotemporal information processing in neurons and have found their application in pattern encoding, data discrimination, and classification. Bioinspired network architectures are considered for event-driven tasks, and scientists have looked at different theories based on the architecture and functioning. Motor tasks, for example, have networks inspired by cerebellar architecture where the granular layer recodes sparse representations of the mossy fiber (MF) inputs and has more roles in motor learning. Using abstractions from cerebellar connections and learning rules of deep learning network (DLN), patterns were discriminated within datasets, and the same algorithm was used for trajectory optimization. In the current work, a cerebellum-inspired spiking neural network with dynamics of cerebellar neurons and learning mechanisms attributed to the granular layer, Purkinje cell (PC) layer, and cerebellar nuclei interconnected by excitatory and inhibitory synapses was implemented. The model’s pattern discrimination capability was tested for two tasks on standard machine learning (ML) datasets and on following a trajectory of a low-cost sensor-free robotic articulator. Tuned for supervised learning, the pattern classification capability of the cerebellum-inspired network algorithm has produced more generalized models than data-specific precision models on smaller training datasets. The model showed an accuracy of 72%, which was comparable to standard ML algorithms, such as MLP (78%), Dl4jMlpClassifier (64%), RBFNetwork (71.4%), and libSVM-linear (85.7%). The cerebellar model increased the network’s capability and decreased storage, augmenting faster computations. Additionally, the network model could also implicitly reconstruct the trajectory of a 6-degree of freedom (DOF) robotic arm with a low error rate by reconstructing the kinematic parameters. The variability between the actual and predicted trajectory points was noted to be ± 3 cm (while moving to a position in a cuboid space of 25 × 30 × 40 cm). Although a few known learning rules were implemented among known types of plasticity in the cerebellum, the network model showed a generalized processing capability for a range of signals, modulating the data through the interconnected neural populations. In addition to potential use on sensor-free or feed-forward based controllers for robotic arms and as a generalized pattern classification algorithm, this model adds implications to motor learning theory.

Environmental Enrichment Improves Vestibular Oculomotor Learning in Mice

We assessed the behavioral effects of environmental enrichment on contrast sensitivity, reflexive eye movements and on oculomotor learning in mice that were housed in an enriched environment for a period of 3 weeks. Research has shown that a larger cage and a more complex environment have positive effects on the welfare of laboratory mice and other animals held in captivity. It has also been shown that environmental enrichment affects various behavior and neuroanatomical and molecular characteristics. We found a clear effect on oculomotor learning. Animals that were housed in an enriched environment learned significantly faster than controls that were housed under standard conditions. In line with existing literature, the enriched group also outperformed the controls in behavioral tests for explorative behavior. Meanwhile, both visual and reflexive oculomotor performance in response to visual and vestibular stimuli was unaffected. This points toward an underlying mechanism that is specific for motor learning, rather than overall motor performance.

Effect of diverse recoding of granule cells on optokinetic response in a cerebellar ring network with synaptic plasticity

We consider a cerebellar ring network for the optokinetic response (OKR), and investigate the effect of diverse recoding of granule (GR) cells on OKR by varying the connection probability p_c from Golgi to GR cells. For an optimal value of p_c^∗(=0.06), individual GR cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of "effective" depression (i.e., strong/weak LTD) at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree L_g, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates at about the 300th cycle. By varying p_c from p_c^∗, we find that a plot of saturated learning gain degree L_g^∗ versus p_c forms a bell-shaped curve with a peak at p_c^∗ (where the diversity degree in spiking patterns of GR cells is also maximum). Consequently, the more diverse in recoding of GR cells, the more effective in motor learning for the OKR adaptation.

Roles of the Cerebellar Interneurons on Vestibulo-Ocular Reflex Motor Learning: A Computational Study

The vestibule-ocular reflex (VOR) has been one of the most popular model systems to investigate the role of the cerebellum in adaptive motor control. The cerebellum receives sensory and motor information via mossy and climbing fibers, and solely outputs motor-related activities from Purkinje cell. The cerebellar flocculus also contains rich inhibitory interneurons known as Golgi, basket, and stellate cells. While most of the previous studies on VOR motor learning have focused on responses of Purkinje cells, little attention has been paid to the roles of cerebellar inhibitory interneurons due to difficulty in identifying and recording them in behaving animals. Thus, the contribution of those interneurons on cerebellar motor learning is still a mystery. Herein, we have constructed a computational model of the VOR that explicitly implements the anatomically realistic floccular neuronal network structure so that the activities of each inhibitory interneuron can be evaluated. From the knocked out simulation of Golgi or basket/stellate cells, we confirmed that those inhibitory interneurons play a different contribution to the acquisition of new VOR performance.

Ocular Reflex Adaptation as an Experimental Model of Cerebellar Learning -- In Memory of Masao Ito -

Masao Ito proposed a cerebellar learning hypothesis with Marr and Albus in the early 1970s. He suggested that cerebellar flocculus (FL) Purkinje cells (PCs), which directly inhibit the vestibular nuclear neurons driving extraocular muscle motor neurons, adaptively control the horizontal vestibulo-ocular reflex (HVOR) through the modification of mossy and parallel fiber-mediated vestibular responsiveness by visual climbing fiber (CF) inputs. Later, it was suggested that the same FL PCs adaptively control the horizontal optokinetic response (HOKR) in the same manner through the modification of optokinetic responsiveness in rodents and rabbits. In 1982, Ito and his colleagues discovered the plasticity of long-term depression (LTD) at parallel fiber (PF)-PC synapses after conjunctive stimulation of mossy or parallel fibers with CFs. Long-term potentiation (LTP) at PF-PC synapses by weak PF stimulation alone was found later. Many lines of experimental evidence have supported their hypothesis using various experimental methods and materials for the past 50 years by many research groups. Although several controversial findings were presented regarding their hypothesis, the reasons underlying many of them were clarified. Today, their hypothesis is considered as a fundamental mechanism of cerebellar learning. Furthermore, it was found that the memory of adaptation is transferred from the FL to vestibular nuclei for consolidation by repetition of adaptation through the plasticity of vestibular nuclear neurons. In this article, after overviewing their cerebellar learning hypothesis, I discuss possible roles of LTD and LTP in gain-up and gain-down HVOR/HOKR adaptations and refer to the expansion of their hypothesis to cognitive functions.

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.

Realization of Direction Selective Motor Learning in the Artificial Cerebellum: Simulation on the Vestibuloocular Reflex Adaptation

The vestibule-ocular reflex (VOR) has been one of the most popular model systems to investigate the role of the cerebellum in adaptive motor control. VOR motor learning can be experimentally induced by continuous application of combination of head rotating stimulus and optokinetic stimulus. For instance, in phase application of those stimuli decreases VOR gain defined by eye velocity of VOR in the dark divided by head velocity, while out of phase of those increases VOR gain. It has been known that VOR gain is modifiable context-dependently. Namely, VOR gains for leftward and rightward head rotations can be respectively increased and decreased simultaneously. The cerebellar signal processing underlying the context dependent VOR motor learning, however, is not fully uncovered. In the present study, we simulated direction selective VOR motor learning, using the artificial cerebellar neuronal network model that we developed to understand the origin of the cerebellar motor learning.

Time Is Cerebellum

The cerebellum characteristically has the capacity to compensate for and restore lost functions. These compensatory/restorative properties are explained by an abundant synaptic plasticity and the convergence of multimodal central and peripheral signals. In addition, extra-cerebellar structures contribute also to the recovery after a cerebellar injury. Clinically, some patients show remarkable improvement of severe ataxic symptoms associated with trauma, stroke, metabolism, or immune-mediated cerebellar ataxia (IMCA, e.g., multiple sclerosis, paraneoplastic cerebellar degeneration, gluten ataxia, anti-GAD65 antibody-associated cerebellar ataxia). However, extension of a cerebellar lesion can impact upon the fourth ventricle or the brainstem, either by direct or indirect mechanisms, leading to serious complications. Moreover, cerebellar reserve itself is affected by advanced cell loss and, at some point of disease progression, deficits become irreversible. Such phase transition from a treatable/restorable state (the reserve is still sufficient) to an untreatable state (the reserve is severely affected) is a loss of therapeutic opportunity, highlighting the need for early treatment during the restorable stage. Based on the motto of "Time is Brain," a warning that stresses the importance of early therapeutic intervention in ischemic diseases, we propose "Time is Cerebellum" as a principle in the management of patients with cerebellar diseases, especially immune ataxias whose complexity often delay the therapeutic intervention. Indeed, this concept should not be restricted to ischemic cerebellar diseases. We argue that every effort should be made to reduce the diagnostic delay and to initiate early therapy to avoid the risk of transition from a treatable state to an irreversible condition and an associated accumulation of disability. The myriad of disorders affecting the cerebellum is a challenging factor that may contribute to irreversible disability if the window of therapeutic opportunity is missed.

Occurrence of long-term depression in the cerebellar flocculus during adaptation of optokinetic response

Long-term depression (LTD) at parallel fiber (PF) to Purkinje cell (PC) synapses has been considered as a main cellular mechanism for motor learning. However, the necessity of LTD for motor learning was challenged by demonstration of normal motor learning in the LTD-defective animals. Here, we addressed possible involvement of LTD in motor learning by examining whether LTD occurs during motor learning in the wild-type mice. As a model of motor learning, adaptation of optokinetic response (OKR) was used. OKR is a type of reflex eye movement to suppress blur of visual image during animal motion. OKR shows adaptive change during continuous optokinetic stimulation, which is regulated by the cerebellar flocculus. After OKR adaptation, amplitudes of quantal excitatory postsynaptic currents at PF-PC synapses were decreased, and induction of LTD was suppressed in the flocculus. These results suggest that LTD occurs at PF-PC synapses during OKR adaptation.