Objective.中枢神经系统的功能图直接或间接地将许多身体运动的协调和控制归因于小脑。尽管有这样的一般情况,在电路层面上,关于小脑神经组件功能的信息很少。多个突触连接的存在和不同类型可塑性的协同作用使得几乎很难确定小脑神经过程对行为表现的不同贡献。在这项研究中,研究长期突触变化对小脑运动学习的影响,我们打算提供定位小脑突触可塑性主要形式缺陷的定量标准.方法。为此,我们开发了小脑回路的放电率模型来模拟视动反射(OKR)的学习,最著名的小脑依赖性运动任务之一。在下文中,通过比较正常和病理突触条件的模拟OKR学习概况,我们提取受长期可塑性障碍影响的学习特征。接下来,用不同的质量(连续无休息)和间隔(与休息期交错)的学习范式进行模拟,我们估计了皮质核突触可塑性缺陷对短期和长期运动记忆的有害影响.主要结果。我们的计算方法预测了缺陷的位置和等级与一些学习因素之间的相关性,例如运动记忆的形成率和保留率,基准性能,甚至小脑马达储备能力。Further,间距分析揭示了学习范式效率对网络缺陷时空特征的依赖性。的确,皮质记忆形成和核记忆巩固的缺陷主要危害群体性和间隔性学习,分别。此结果用于设计差异测定法,以识别小脑学习的错误阶段。意义。提出的计算框架可以帮助开发神经筛查系统并准备小脑回路的中尺度功能图。
Objective.Functional maps of the central nervous system attribute the coordination and control of many body movements directly or indirectly to the cerebellum. Despite this general picture, there is little information on the function of cerebellar neural components at the circuit level. The presence of multiple synaptic junctions and the synergistic action of different types of plasticity make it virtually difficult to determine the distinct contribution of cerebellar neural processes to behavioral manifestations. In this study, investigating the effect of long-term synaptic changes on cerebellar motor learning, we intend to provide quantitative criteria for localizing defects in the major forms of synaptic plasticity in the cerebellum.Approach.To this end, we develop a firing rate model of the cerebellar circuits to simulate learning of optokinetic reflex (OKR), one of the most well-known cerebellar-dependent motor tasks. In the following, by comparing the simulated OKR learning profile for normal and pathosynaptic conditions, we extract the learning features affected by long-term plasticity disorders. Next, conducting simulation with different massed (continuous with no rest) and spaced (interleaved with rest periods) learning paradigms, we estimate the detrimental impact of plasticity defects at corticonuclear synapses on short- and long-term motor memory.Main results.Our computational approach predicts a correlation between location and grade of the defect with some learning factors such as the rate of formation and retention of motor memory, baseline performance, and even cerebellar motor reserve capacity. Further, spacing analysis reveal the dependence of learning paradigm efficiency on the spatiotemporal characteristic of defect in the network. Indeed, defects in cortical memory formation and nuclear memory consolidation mainly harm massed and spaced learning, respectively. This result is used to design a differential assay for identifying the faulty phases of cerebellar learning.Significance.The proposed computational framework can help develop neural-screening systems and prepare meso-scale functional maps of the cerebellar circuits.