Cerebellar Nuclei and Cerebellar Learning

We provide behavioral evidence using monkey smooth pursuit eye movements for four principles of cerebellar learning. Using a circuit-level model of the cerebellum, we link behavioral data to learning’s neural implementation. The four principles are: (1) early, fast, acquisition driven by climbing fiber inputs to the cerebellar cortex, with poor retention; (2) learned responses of Purkinje cells guide transfer of learning from the cerebellar cortex to the deep cerebellar nucleus, with excellent retention; (3) functionally different neural signals are subject to learning in the cerebellar cortex versus the deep cerebellar nuclei; and (4) negative feedback from the cerebellum to the inferior olive reduces the magnitude of the teaching signal in climbing fibers and limits learning. Our circuit-level model, based on these four principles, explains behavioral data obtained by strategically manipulating the signals responsible for acquisition and recall of direction learning in smooth pursuit eye movements across multiple timescales.

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Cerebellar Nuclei and Cerebellar Learning

Handbook of the Cerebellum and Cerebellar Disorders ◽

10.1007/978-3-319-97911-3_47-2 ◽

2019 ◽

pp. 1-24

Author(s):

Dieter Jaeger

Keyword(s):

Cerebellar Nuclei ◽

Cerebellar Learning

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Mapping of long-term cognitive and motor deficits in pediatric cerebellar brain tumor survivors into a cerebellar white matter atlas

Child s Nervous System ◽

10.1007/s00381-021-05244-2 ◽

2021 ◽

Author(s):

Frederik Grosse ◽

Stefan Mark Rueckriegel ◽

Ulrich-Wilhelm Thomale ◽

Pablo Hernáiz Driever

Keyword(s):

Brain Tumor ◽

Executive Function ◽

Cognitive Function ◽

White Matter ◽

Cerebellar Nuclei ◽

Motor Deficits ◽

Deep Cerebellar Nuclei ◽

Cerebellar White Matter ◽

Lesion Symptom Mapping

Abstract Purpose Diaschisis of cerebrocerebellar loops contributes to cognitive and motor deficits in pediatric cerebellar brain tumor survivors. We used a cerebellar white matter atlas and hypothesized that lesion symptom mapping may reveal the critical lesions of cerebellar tracts. Methods We examined 31 long-term survivors of pediatric posterior fossa tumors (13 pilocytic astrocytoma, 18 medulloblastoma). Patients underwent neuronal imaging, examination for ataxia, fine motor and cognitive function, planning abilities, and executive function. Individual consolidated cerebellar lesions were drawn manually onto patients’ individual MRI and normalized into Montreal Neurologic Institute (MNI) space for further analysis with voxel-based lesion symptom mapping. Results Lesion symptom mapping linked deficits of motor function to the superior cerebellar peduncle (SCP), deep cerebellar nuclei (interposed nucleus (IN), fastigial nucleus (FN), ventromedial dentate nucleus (DN)), and inferior vermis (VIIIa, VIIIb, IX, X). Statistical maps of deficits of intelligence and executive function mapped with minor variations to the same cerebellar structures. Conclusion We identified lesions to the SCP next to deep cerebellar nuclei as critical for limiting both motor and cognitive function in pediatric cerebellar tumor survivors. Future strategies safeguarding motor and cognitive function will have to identify patients preoperatively at risk for damage to these critical structures and adapt multimodal therapeutic options accordingly.

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A model of on/off transitions in neurons of the deep cerebellar nuclei: deciphering the underlying ionic mechanisms

The Journal of Mathematical Neuroscience ◽

10.1186/s13408-021-00105-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hugues Berry ◽

Stéphane Genet

Keyword(s):

Cerebellar Nuclei ◽

Biophysical Model ◽

High Threshold ◽

Deep Cerebellar Nuclei ◽

Functional Link ◽

Electrophysiological Properties ◽

Voltage Dependent ◽

Ionic Mechanisms ◽

The Central Nervous System ◽

Overall Functioning

AbstractThe neurons of the deep cerebellar nuclei (DCNn) represent the main functional link between the cerebellar cortex and the rest of the central nervous system. Therefore, understanding the electrophysiological properties of DCNn is of fundamental importance to understand the overall functioning of the cerebellum. Experimental data suggest that DCNn can reversibly switch between two states: the firing of spikes (F state) and a stable depolarized state (SD state). We introduce a new biophysical model of the DCNn membrane electro-responsiveness to investigate how the interplay between the documented conductances identified in DCNn give rise to these states. In the model, the F state emerges as an isola of limit cycles, i.e. a closed loop of periodic solutions disconnected from the branch of SD fixed points. This bifurcation structure endows the model with the ability to reproduce the $\text{F}\to \text{SD}$ F → SD transition triggered by hyperpolarizing current pulses. The model also reproduces the $\text{F}\to \text{SD}$ F → SD transition induced by blocking Ca currents and ascribes this transition to the blocking of the high-threshold Ca current. The model suggests that intracellular current injections can trigger fully reversible $\text{F}\leftrightarrow \text{SD}$ F ↔ SD transitions. Investigation of low-dimension reduced models suggests that the voltage-dependent Na current is prominent for these dynamical features. Finally, simulations of the model suggest that physiological synaptic inputs may trigger $\text{F}\leftrightarrow \text{SD}$ F ↔ SD transitions. These transitions could explain the puzzling observation of positively correlated activities of connected Purkinje cells and DCNn despite the former inhibit the latter.

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Anatomy of Motor Learning. II. Subcortical Structures and Learning by Trial and Error

Journal of Neurophysiology ◽

10.1152/jn.1997.77.3.1325 ◽

1997 ◽

Vol 77 (3) ◽

pp. 1325-1337 ◽

Cited By ~ 294

Author(s):

M. Jueptner ◽

C. D. Frith ◽

D. J. Brooks ◽

R.S.J. Frackowiak ◽

R. E. Passingham

Keyword(s):

Motor Learning ◽

Response Times ◽

Cerebellar Nuclei ◽

Selection Task ◽

Systematic Change ◽

Trial And Error ◽

Subcortical Structures ◽

Learning Condition ◽

Positron Emission ◽

New Learning

Jueptner, M., C. D. Frith, D. J. Brooks, R.S.J. Frackowiak, and R. E. Passingham. Anatomy of motor learning. II. Subcortical structures and learning by trial and error. J. Neurophysiol. 77: 1325–1337, 1997. We used positron emission tomography to study motor learning by trial and error. Subjects learned sequences of eight finger movements. Tones generated by a computer told the subjects whether any particular move was correct or incorrect. A control condition was used in which the subjects generated moves, but there was no feeback to indicate success or failure, and so no learning occured. In this condition (free selection) the subjects were required to make a finger movement on each trial and to vary the movements randomly over trials. The subjects had a free choice of which finger to move on any one trial. On this task there was no systematic change in responses over trials and no change in the response times. Two other conditions were included. In one the subjects repetitively moved the same finger on all trials and in a baseline condition the subjects heard the pacing tones and auditory feedback but made no movements. Comparing new learning with the free selection task, there was a small activation in the right prefrontal cortex. This may reflect the fact that in new learning, but not free selection, the subjects rehearse past moves and adapt their responses accordingly. The caudate nucleus was strongly activated during new learning. It is suggested that this activity may be related either to mental rehearsal or to reinforcement of the movements as a consequence of the outcomes. The putamen was activated anteriorly on the free selection task and more posteriorly when the subjects repetitively made the same movement. It is suggested that the differences in the location of the peak activation in the striatum may represent the operation of different corticostriatal loops. The cerebellar nuclei (bilaterally) and vermis were more active in the new learning condition than during the performance of the free selection task. There was no difference in the activation of the cerebellum when the free selection task was compared with repetitive performance of the same movement. We tentatively suggest that the basal ganglia may be involved in the specification of movement on the basis of memory of either the movements or the outcomes, but that the cerebellum may be more directly involved in changes in the parameters of movement execution.

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Role of the Vermal Cerebellum in Visually Guided Eye Movements and Visual Motion Perception

Annual Review of Vision Science ◽

10.1146/annurev-vision-091718-015000 ◽

2019 ◽

Vol 5 (1) ◽

pp. 247-268 ◽

Cited By ~ 1

Author(s):

Peter Thier ◽

Akshay Markanday

Keyword(s):

Eye Movements ◽

Motion Perception ◽

Visual Motion ◽

Cerebellar Nuclei ◽

Visually Guided ◽

Past Performance ◽

Visual Motion Perception ◽

Oculomotor Vermis ◽

Retinal Information

The cerebellar cortex is a crystal-like structure consisting of an almost endless repetition of a canonical microcircuit that applies the same computational principle to different inputs. The output of this transformation is broadcasted to extracerebellar structures by way of the deep cerebellar nuclei. Visually guided eye movements are accommodated by different parts of the cerebellum. This review primarily discusses the role of the oculomotor part of the vermal cerebellum [the oculomotor vermis (OMV)] in the control of visually guided saccades and smooth-pursuit eye movements. Both types of eye movements require the mapping of retinal information onto motor vectors, a transformation that is optimized by the OMV, considering information on past performance. Unlike the role of the OMV in the guidance of eye movements, the contribution of the adjoining vermal cortex to visual motion perception is nonmotor and involves a cerebellar influence on information processing in the cerebral cortex.

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