We consider the Pavlovian eyeblink conditioning (EBC) via repeated presentation of paired conditioned stimulus (tone) and unconditioned stimulus (US; airpuff). In an effective cerebellar ring network, we change the connection probability \(p_c\) from Golgi to granule (GR) cells, and make a dynamical classification of various firing patterns of the GR cells. Individual GR cells are thus found to show various well- and ill-matched firing patterns relative to the US timing signal. Then, these variously-recoded signals are fed into the Purkinje cells (PCs) through the parallel-fibers (PFs). Based on such unique dynamical classification of various firing patterns, we make intensive investigations on the influence of various temporal recoding (i.e., firing patterns) of the GR cells on the synaptic plasticity of the PF-PC synapses and the subsequent learning process for the EBC. We first note that the variously-recoded PF signals are effectively depressed by the (error-teaching) instructor climbing-fiber (CF) signals from the inferior olive neuron. In the case of well-matched PF signals, they are strongly depressed through strong long-term depression (LTD) by the instructor CF signals due to good association between the in-phase PF and the instructor CF signals. On the other hand, practically no LTD occurs for the ill-matched PF signals because most of them have no association with the instructor CF signals. This kind of “effective” depression at the PF-PC synapses coordinates firings of PCs effectively, which then makes effective inhibitory coordination on the cerebellar nucleus neuron [which elicits conditioned response (CR; eyeblink)]. When the learning trial passes a threshold, acquisition of CR begins. In this case, the timing degree \(\mathcal{T}_d\) of CR becomes good due to presence of the ill-matched firing group which plays a role of protection barrier for the timing. With further increase in the number of trials, strength \(\mathcal S\) of CR (corresponding to the amplitude of eyelid closure) increases due to strong LTD in the well-matched firing group, while its timing degree \(\mathcal{T}_d\) decreases. In this way, the well- and the ill-matched firing groups play their own roles for the strength and the timing of CR, respectively. Thus, with increasing the number of learning trials, the (overall) learning efficiency degree \(\mathcal{L}_e\) (taking into consideration both timing and strength of CR) for the CR is increased, and eventually it becomes saturated. Finally, we also discuss dependence of the variety degree for firing patterns of the GR cells and the saturated learning efficiency degree \(\mathcal{L}_e\) of the CR on \(p_c\) and their relations.

我们通过重复呈现成对的条件刺激（音调）和非条件刺激（美国；吹气）来考虑巴甫洛夫眨眼条件反射 (EBC)。在一个有效的小脑环网络中，我们改变连接概率\(p_c\)从高尔基体到颗粒 (GR) 细胞，并对 GR 细胞的各种放电模式进行动态分类。因此，发现各个 GR 细胞相对于 US 定时信号显示出各种匹配良好和不匹配的发射模式。然后，这些经过不同编码的信号通过平行纤维 (PF) 馈入浦肯野细胞 (PC)。基于各种放电模式的独特动态分类，我们深入研究了 GR 细胞的各种时间记录（即放电模式）对 PF-PC 突触的突触可塑性和 EBC 的后续学习过程的影响. 我们首先注意到，来自下橄榄神经元的（错误教学）指导员攀爬纤维（CF）信号有效地抑制了各种重新编码的 PF 信号。在匹配良好的 PF 信号的情况下，由于同相 PF 和教练 CF 信号之间的良好关联，他们通过教练 CF 信号的强烈长期抑制 (LTD) 受到强烈抑制。另一方面，对于不匹配的 PF 信号，实际上不会出现 LTD，因为它们中的大多数与指导 CF 信号没有关联。这种 PF-PC 突触的“有效”抑制有效地协调 PC 的放电，然后对小脑核神经元进行有效的抑制性协调 [引发条件反应（CR；眨眼）]。当学习试验通过阈值时，CR 的获取开始。在这种情况下，时序度 对于不匹配的 PF 信号，几乎没有 LTD 发生，因为它们中的大多数与教练 CF 信号没有关联。这种 PF-PC 突触的“有效”抑制有效地协调 PC 的放电，然后对小脑核神经元进行有效的抑制性协调 [引发条件反应（CR；眨眼）]。当学习试验通过阈值时，CR 的获取开始。在这种情况下，时序度 对于不匹配的 PF 信号，几乎没有 LTD 发生，因为它们中的大多数与教练 CF 信号没有关联。这种 PF-PC 突触的“有效”抑制有效地协调 PC 的放电，然后对小脑核神经元进行有效的抑制性协调 [引发条件反应（CR；眨眼）]。当学习试验通过阈值时，CR 的获取开始。在这种情况下，时序度 当学习试验通过阈值时，CR 的获取开始。在这种情况下，时序度 当学习试验通过阈值时，CR 的获取开始。在这种情况下，时序度CR 的\(\mathcal{T}_d\)变得良好是因为存在不匹配的触发组，它起到了时序保护屏障的作用。随着试验次数的进一步增加，CR强度\(\mathcal S\) (对应于眼睑闭合幅度)由于良好匹配的发射组中的强LTD而增加，而其时机度\(\mathcal{ T}_d\)减少。通过这种方式，匹配良好和不匹配的射击组分别对 CR 的强度和时机发挥各自的作用。因此，随着学习试验次数的增加，（整体）学习效率程度\(\mathcal{L}_e\)（同时考虑 CR 的时间和强度）因为 CR 增加，最终变得饱和。最后，我们还讨论了 GR 细胞放电模式的多样性程度和 CR 的饱和学习效率程度\(\mathcal{L}_e\)对\(p_c\)的依赖性及其关系。