首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Simulating the shaping of the fastigial deep nuclear saccade command by cerebellar Purkinje cells.
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Simulating the shaping of the fastigial deep nuclear saccade command by cerebellar Purkinje cells.

机译:模拟小脑浦肯野细胞对小脑深部核扫视命令的形成。

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摘要

Early lesion and physiological studies established the key contributions of the cerebellar cortex and fastigial deep nuclei in maintaining the accuracy of saccades. Recent evidence has demonstrated that fastigial oculomotor region cells (FORCs) provide commands that are critical both for driving and braking saccades. Modeling studies have largely ignored the mechanisms by which the FORC activity patterns, and those of the Purkinje cells (PCs) that inhibit them, are produced by the mossy fiber (MF) inputs common to both. We have created a hybrid network of integrate-and-fire and summation units to model the circuitry between PCs, FORCs, and MFs that can account for all observed PC and FORC activity patterns. The model demonstrates that a crucial component of FORC activity may be due to the rebound depolarization intrinsic to FORC neurons that, like the MF-driven activity of FORCs, is also shaped by PC inhibition and disinhibition. The model further demonstrates that the shaping of the FORC saccade command by PCs can be adaptively modified through plausible learning rules based on cerebellar long-term depression (LTD) and long-term potentiation (LTP), which are guided by climbing fiber (CF) input to PCs that realistically indicates only the direction (but not the magnitude) of saccade error. These modeling results provide new insights into the adaptive control by the cerebellum of the deep nuclear saccade command.
机译:早期病变和生理学研究确定了小脑皮层和小脑深核在维持扫视的准确性方面的关键作用。最近的证据表明,小脑动眼区域细胞(FORC)提供的命令对于驾驶和制动扫视都至关重要。建模研究在很大程度上忽略了两者共同的苔藓纤维(MF)输入所产生的FORC活性模式以及抑制它们的Purkinje细胞(PC)的机理。我们创建了一个由集成,发射和求和单元组成的混合网络,以对PC,FORC和MF之间的电路进行建模,从而可以解释所有观察到的PC和FORC活动模式。该模型表明,FORC活性的关键成分可能是由于FORC神经元固有的反弹去极化作用,与FORCs的MF驱动活性一样,它也受PC抑制和去抑制作用的影响。该模型进一步证明,可以通过基于小脑长期压抑(LTD)和长期增强(LTP)的合理学习规则,通过爬山纤维(CF)指导的合理学习规则,来自适应地修改PC的FORC扫视命令的形状。实际输入PC的输入,仅指示扫视误差的方向(而不指示幅度)。这些建模结果为深部核扫视命令的小脑对自适应控制提供了新的见解。

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