Neuroscience
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Parkinson's disease (PD) is characterized by tremor, rigidity, and bradykinesia. PD is caused mainly by depletion of the nigrostriatal pathway. Conventional medications such as levodopa are highly effective in the early stage of PD; however, these medications fail to prevent the underlying neurodegeneration. ⋯ For example, although fetal ventral midbrain is efficacious in some patients, its ethical issues and the existence of graft-induced dyskinesias (GID) have prevented its use in large-scale clinical applications. ESCs have reliable isolation protocols and the potential to differentiate into dopaminergic progenitors. iPSCs and induced neural cells are suitable for autologous grafting. Here we review milestone improvements and emerging sources for cell-based PD therapy to serve as a framework for clinicians and a key reference to develop replacement therapy for other neurological disorders.
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Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. ⋯ In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.
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As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.
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Performance of supercomputers has been steadily and exponentially increasing for the past 20 years, and is expected to increase further. This unprecedented computational power enables us to build and simulate large-scale neural network models composed of tens of billions of neurons and tens of trillions of synapses with detailed anatomical connections and realistic physiological parameters. Such "human-scale" brain simulation could be considered a milestone in computational neuroscience and even in general neuroscience. ⋯ Then, we direct our attention to the cerebellum, with a review of more simulation studies specific to that region. Furthermore, we present recent simulation results of a human-scale cerebellar network model composed of 86 billion neurons on the Japanese flagship supercomputer K (now retired). Finally, we discuss the necessity and importance of human-scale brain simulation, and suggest future directions of such large-scale brain simulation research.
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The cerebellum is thought to have a variety of functions because it developed with the evolution of the cerebrum and connects with different areas in the frontoparietal cortices. Like neurons in the cerebral cortex, those in the cerebellum also exhibit strong activity during planning in addition to the execution of movements. However, their specific roles remain elusive. ⋯ During a recently developed synchronized eye movement task, cerebellar nuclear neurons exhibited periodic preparatory activity for predictive synchronization. In all cases, the cerebellum generated preparatory activity lasting for several hundred milliseconds. These signals may regulate neuronal activity in the cerebral cortex that adjusts movement timing and predicts the timing of rhythmic events.