论文标题
在具有突触可塑性的小脑环网络中，颗粒细胞多样化的颗粒细胞对光（
Effect of Diverse Recoding of Granule Cells on Optokinetic Response in A Cerebellar Ring Network with Synaptic Plasticity
论文作者
论文摘要
我们考虑了用于光动力学反应（OKR）的小脑环网络（OKR），并通过改变Golgi到GR细胞的连接概率$ P_C $来研究OKR颗粒（GR）细胞对OKR的各种效果。对于$ p_c^*〜（= 0.06）$的最佳值，单个GR细胞表现出各种峰值模式，相对于其种群平均的射击活动，它们是同相，反相或复杂的相位。然后，这些通过平行纤维（PFS）从GR细胞中进行的多样重新编码的信号被误读信号通过从下橄榄的攀爬纤维有效地抑制，这也是同相内的纤维。活性GR细胞的同相PF-Purkinje细胞（PC）突触的突触重量通过强长期抑郁（LTD）强烈降低，而在抗相规和复杂相位的PF-PC突触中的突触则通过弱LTD弱降低。 PF-PC突触处的这种“有效”抑郁症（即强/弱LTD）会引起PC发射的重大调节，然后对前庭核（VN）神经元（唤起OKR）发挥有效的抑制性协调。对于VN神经元的触发，学习增益度$ {\ cal {l}} _ g $，对应于调制增长率，随着学习周期的增加而增加，并且在第300个周期中饱和。通过从$ p_c^*$之间改变$ p_c $，我们发现饱和学习获得度$ {\ cal l} _g^*$ vess $ p_c $形成一个钟形曲线，峰值为$ p_c^*$（在$ p_c^*$中（其中，GR的多样性峰值模式的gr均值也最高）。因此，重新编码GR细胞的多样性，对OKR适应性的运动学习更有效。
We consider a cerebellar ring network for the optokinetic response (OKR), and investigate the effect of diverse recoding of granule (GR) cells on OKR by varying the connection probability $p_c$ from Golgi to GR cells. For an optimal value of $p_c^*~(=0.06)$, individual GR cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of "effective" depression (i.e., strong/weak LTD) at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree ${\cal{L}}_g$, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates at about the 300th cycle. By varying $p_c$ from $p_c^*$, we find that a plot of saturated learning gain degree ${\cal L}_g^*$ versus $p_c$ forms a bell-shaped curve with a peak at $p_c^*$ (where the diversity degree in spiking patterns of GR cells is also maximum). Consequently, the more diverse in recoding of GR cells, the more effective in motor learning for the OKR adaptation.
