We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental setup (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and RT operation. The RT supervisor module provides for incremental countermeasures that dynamically slow down or speed up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e., STDP mechanisms, spike propagation, and neural updates) to cope with the RT constraints imposed by the real robot operation. This neurorobotic experimental setup is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This paper underlies the need for a two-stage learning process to facilitate VOR acquisition.

中文翻译：

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使用尖峰小脑模型的人形 iCub 机器人的 VOR 适应

我们在自适应实时 (RT) 控制回路中嵌入了尖峰小脑模型，该回路能够在执行不同的前庭眼反射 (VOR) 任务时操作真正的机器人身体 (iCub)。尖峰神经网络计算，包括事件和时间驱动的神经动力学、神经活动和尖峰定时相关可塑性 (STDP) 机制，导致由小脑模拟过程中遇到的神经活动齐射引起的非确定性计算时间。这种不确定的计算时间促使 RT 监督模块的集成，该模块能够确保精心安排的神经计算时间和机器人操作。实际上，我们的神经机器人实验装置 (VOR) 受益于小脑和身体之间的生物感觉运动延迟，以缓冲计算过载，并在调整神经计算时间和 RT 操作方面提供灵活性。RT 管理器模块提供增量对策，通过停止模拟或禁用某些神经计算功能（即 STDP 机制、尖峰传播和神经更新）来动态减慢或加速小脑模拟，以应对由真正的机器人操作。这种神经机器人实验装置适用于不同的水平和垂直 VOR 自适应任务，这些任务被神经科学界广泛用于解决小脑功能问题。我们的目标是阐明小脑神经基质和分布式可塑性的组合如何塑造小脑神经活动以调节运动适应。本文强调需要一个两阶段的学习过程来促进 VOR 获取。