1 Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy.
2 Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Italy.
Int J Neural Syst. 2018 Nov;28(9):1850020. doi: 10.1142/S012906571850020X. Epub 2018 Apr 24.
During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under behavioral feedback and should involve changes distributed across multiple synaptic sites. In eyeblink classical conditioning (EBCC), the cerebellum learns to predict the precise timing between two stimuli, hence EBCC represents an elementary yet meaningful paradigm to investigate the cerebellar network functioning. We have simulated EBCC mechanisms by reconstructing a realistic cerebellar microcircuit model and embedding multiple plasticity rules imitating those revealed experimentally. The model was tuned to fit experimental EBCC human data, estimating the underlying learning time-constants. Learning started rapidly with plastic changes in the cerebellar cortex followed by slower changes in the deep cerebellar nuclei. This process was characterized by differential development of long-term potentiation and depression at individual synapses, with a progressive accumulation of plasticity distributed over the whole network. The experimental data included two EBCC sessions interleaved by a trans-cranial magnetic stimulation (TMS). The experimental and the model response data were not significantly different in each learning phase, and the model goodness-of-fit was [Formula: see text] for all the experimental conditions. The models fitted on TMS data revealed a slowed down re-acquisition (sessions-2) compared to the control condition ([Formula: see text]). The plasticity parameters characterizing each model significantly differ among conditions, and thus mechanistically explain these response changes. Importantly, the model was able to capture the alteration in EBCC consolidation caused by TMS and showed that TMS affected plasticity at cortical synapses thereby altering the fast learning phase. This, secondarily, also affected plasticity in deep cerebellar nuclei altering learning dynamics in the entire sensory-motor loop. This observation reveals dynamic redistribution of changes over the entire network and suggests how TMS affects local circuit computation and memory processing in the cerebellum.
在自然学习过程中，突触可塑性被认为是动态演变的，并在子电路内部和之间重新分配。这个过程应该出现在受行为反馈影响的可塑神经网络中，并且应该涉及分布在多个突触位置的变化。在眨眼经典条件反射（EBCC）中，小脑学会预测两个刺激之间的精确时间，因此 EBCC 代表了一个基本而有意义的范例，可以研究小脑网络的功能。我们通过重建一个现实的小脑微电路模型并嵌入多个模仿实验中揭示的可塑性规则来模拟 EBCC 机制。该模型经过调整以适应实验性 EBCC 人类数据，估计潜在的学习时间常数。学习开始迅速，小脑皮层的可塑性变化紧随其后，然后是深部小脑核的较慢变化。这个过程的特点是个体突触的长时程增强和长时程抑制的差异发展，随着整个网络的可塑性分布逐渐积累。实验数据包括两个 EBCC 会话，由经颅磁刺激（TMS）穿插。在每个学习阶段，实验和模型的响应数据没有显著差异，并且模型拟合度对于所有实验条件均为 [Formula: see text]。在 TMS 数据上拟合的模型揭示了与对照条件相比（[Formula: see text]），重新获取（会话 2）速度较慢。表征每个模型的可塑性参数在条件之间显著不同，从而从机制上解释了这些响应变化。重要的是，该模型能够捕获 TMS 引起的 EBCC 巩固的改变，并表明 TMS 影响皮质突触的可塑性，从而改变快速学习阶段。这也会影响深部小脑核的可塑性，改变整个感觉运动回路的学习动态。这一观察结果揭示了整个网络中变化的动态重新分配，并表明 TMS 如何影响小脑局部回路计算和记忆处理。
