Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks
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DOI：
10.3389/fncom.2015.00024
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发表时间：
2015-02-25
影响因子：
3.2
通讯作者：
Pedrocchi, Alessandra
中科院分区：
文献类型：
--
作者：
Casellato, Claudia;Antonietti, Alberto;Pedrocchi, Alessandra
小脑在运动学习中起着至关重要的作用，它充当预测控制器。对它进行建模并将其嵌入到感觉运动任务中，使我们能够在可塑性机制、神经回路和行为学习之间建立功能联系。此外，如果应用于神经机器人的实时控制，小脑模型必须处理真实的噪声和变化的环境，从而显示出其学习的鲁棒性和有效性。已经使用了一种受生物学启发的小脑模型，该模型在皮质和核部位均具有分布式可塑性。设计了两种小脑介导的范例：联想巴甫洛夫任务和前庭眼反射，具有多次获得和消退过程以及不同的刺激和扰动模式。小脑控制器成功地产生了条件反应和精细调节的眼球运动补偿，从而再现了类人行为。通过从皮质到核位点的富有成效的可塑性转移，分布式小脑控制器在这两项任务中都显示出在多个时间尺度上优化学习、存储运动记忆和有效适应动态刺激范围的能力。
The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.