中尺度模拟预测了小脑协同可塑性在经典眼动条件反射过程中的作用。

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2024-04-04 DOI:10.1371/journal.pcbi.1011277
A. Geminiani, C. Casellato, H. Boele, A. Pedrocchi, C. D. De Zeeuw, E. D'Angelo
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引用次数: 0

摘要

根据阿尔伯斯和伊藤的运动学习理论,平行纤维与浦肯野细胞突触(pf-PC)上的突触抑制是在爬行纤维控制下学习感觉运动或然性的主要基质。然而,最近的实验证据对小脑学习的这种相对垄断性观点提出了挑战。双向可塑性似乎对学习至关重要,其中不同微区的突触强度会发生相反的变化(例如,下行微区更可能是抑制,上行微区更可能是增效),而且已发现多种形式的可塑性分布在不同的小脑回路突触上。在这里,我们使用先进的尖峰小脑模型模拟了经典眼动调节(CEBC),该模型嵌入了受多种可塑性规则影响的下行和上行模块。模拟结果表明,突触可塑性调节了遍布整个小脑皮层和小脑核的一连串精确尖峰模式。CEBC得到了pf-PC突触和分子层中间神经元(MLIs)突触可塑性的支持,但只有这两个部位的可塑性联合关闭才会显著影响学习。通过不同程度地利用爬行纤维信息和相关形式的突触可塑性,两个微区都有助于产生适时的条件反应,但在这一过程中起主要作用的是下行模块。我们的模拟结果与细胞特异性突变影响 PC 和/或 MLI 突触处理的突变小鼠的行为和电生理学表型密切吻合。我们的数据突出表明,分布在整个小脑的双向可塑性规则的协同作用可以促进在高时空分辨率下对适应性联想行为进行微调。
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Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning.
According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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