The cerebellum is known to have an important role in sensing and execution of precise time intervals, but the mechanism by which arbitrary time intervals can be recognized and replicated with high precision is unknown. We propose a computational model in which precise time intervals can be identified from the pattern of individual spike activity in a population of parallel fibers in the cerebellar cortex. The model depends on the presence of repeatable sequences of spikes in response to conditioned stimulus input. We emulate granule cells using a population of Izhikevich neuron approximations driven by random but repeatable mossy fiber input. We emulate long-term depression (LTD) and long-term potentiation (LTP) synaptic plasticity at the parallel fiber to Purkinje cell synapse. We simulate a delay conditioning paradigm with a conditioned stimulus (CS) presented to the mossy fibers and an unconditioned stimulus (US) some time later issued to the Purkinje cells as a teaching signal. We show that Purkinje cells rapidly adapt to decrease firing probability following onset of the CS only at the interval for which the US had occurred. We suggest that detection of replicable spike patterns provides an accurate and easily learned timing structure that could be an important mechanism for behaviors that require identification and production of precise time intervals.

中文翻译：

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用于精确时间间隔延迟调节的小脑计算机制

众所周知，小脑在感知和执行精确时间间隔方面发挥着重要作用，但可以高精度识别和复制任意时间间隔的机制尚不清楚。我们提出了一种计算模型，在该模型中，可以从小脑皮层平行纤维群中的个体尖峰活动模式中确定精确的时间间隔。该模型取决于响应条件刺激输入的可重复尖峰序列的存在。我们使用由随机但可重复的苔藓纤维输入驱动的一组 Izhikevich 神经元近似值来模拟颗粒细胞。我们在平行纤维到浦肯野细胞突触模拟长期抑制 (LTD) 和长期增强 (LTP) 突触可塑性。我们模拟延迟条件反射范式，条件刺激 (CS) 呈现给苔藓纤维，一段时间后作为教学信号发送给浦肯野细胞的无条件刺激 (US)。我们表明浦肯野细胞仅在美国发生的时间间隔内迅速适应降低 CS 发作后的发射概率。我们建议检测可复制的尖峰模式提供了一种准确且易于学习的时间结构，这可能是需要识别和产生精确时间间隔的行为的重要机制。我们表明浦肯野细胞仅在美国发生的时间间隔内迅速适应降低 CS 发作后的发射概率。我们建议检测可复制的尖峰模式提供了一种准确且易于学习的时间结构，这可能是需要识别和产生精确时间间隔的行为的重要机制。我们表明，浦肯野细胞仅在 US 发生的时间间隔内，在 CS 发生后迅速适应降低发射概率。我们建议检测可复制的尖峰模式提供了一种准确且易于学习的时间结构，这可能是需要识别和产生精确时间间隔的行为的重要机制。