Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity
Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity
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DOI：
10.1109/tnnls.2016.2598190
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发表时间：
2017-11-01
影响因子：
10.4
通讯作者：
Pedrocchi, Alessandra
中科院分区：
文献类型：
--
作者：
Antonietti, Alberto;Casellato, Claudia;Pedrocchi, Alessandra
小脑在感觉运动控制中起着至关重要的作用。然而，小脑的特定回路和可塑性机制如何参与闭环处理仍不清楚。我们开发了一个人工感觉运动控制系统，嵌入一个详细的尖峰小脑微电路与三个双向可塑性网站。这被证明能够再现小脑驱动的联想范式，眨眼经典条件反射（EBCC），其中建立了非条件刺激（US）和条件刺激（CS）之间的精确时间关系。我们挑战尖峰模型，以适应人类受试者的实验数据集。随后两个会议的EBCC收购和灭绝的记录和经颅磁刺激（TMS）施加在小脑改变电路功能和可塑性。进化算法被用来找到近似最优的模型参数，以再现受试者在协议的不同会话中的行为。主要发现是，优化的小脑模型能够学习预测（预测）条件反应，具有准确的时间和成功率，表现出快速获取，记忆稳定，快速消退和更快的重新获取，如人类的EBCC。浦肯野细胞（PC）和小脑深核（DCN）的放电变化在学习过程中的突触可塑性的控制下，以不同的速度发展，与更快的收购在小脑皮层比在DCN突触。最终，PC活动的减少在CS之后释放了DCN放电，精确地预测了美国并导致眨眼。此外，皮质可塑性的一个特定改变解释了人类小脑经颅磁刺激引起的EBCC变化。在本文中，第一次，它是如何闭环模拟，使用详细的小脑微电路模型，可以成功地用于拟合真实的实验数据集。因此，在协议的不同会话中模型参数的变化揭示了隐式微电路机制如何产生正常和改变的关联行为。
The cerebellum plays a critical role in sensorimotor control. However, how the specific circuits and plastic mechanisms of the cerebellum are engaged in closed-loop processing is still unclear. We developed an artificial sensorimotor control system embedding a detailed spiking cerebellar microcircuit with three bidirectional plasticity sites. This proved able to reproduce a cerebellar-driven associative paradigm, the eyeblink classical conditioning (EBCC), in which a precise time relationship between an unconditioned stimulus (US) and a conditioned stimulus (CS) is established. We challenged the spiking model to fit an experimental data set from human subjects. Two subsequent sessions of EBCC acquisition and extinction were recorded and transcranial magnetic stimulation (TMS) was applied on the cerebellum to alter circuit function and plasticity. Evolutionary algorithms were used to find the near-optimal model parameters to reproduce the behaviors of subjects in the different sessions of the protocol. The main finding is that the optimized cerebellar model was able to learn to anticipate (predict) conditioned responses with accurate timing and success rate, demonstrating fast acquisition, memory stabilization, rapid extinction, and faster reacquisition as in EBCC in humans. The firing of Purkinje cells (PCs) and deep cerebellar nuclei (DCN) changed during learning under the control of synaptic plasticity, which evolved at different rates, with a faster acquisition in the cerebellar cortex than in DCN synapses. Eventually, a reduced PC activity released DCN discharge just after the CS, precisely anticipating the US and causing the eyeblink. Moreover, a specific alteration in cortical plasticity explained the EBCC changes induced by cerebellar TMS in humans. In this paper, for the first time, it is shown how closed-loop simulations, using detailed cerebellar microcircuit models, can be successfully used to fit real experimental data sets. Thus, the changes of the model parameters in the different sessions of the protocol unveil how implicit microcircuit mechanisms can generate normal and altered associative behaviors.