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Underlying Mechanisms of Cooperativity Input Specificity and Associativity of Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis

机译:通过局部蛋白合成的正反馈长期增强的协同性输入特异性和缔合性的潜在机制

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摘要

Long-term potentiation (LTP) is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF) signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.
机译:长期增强(LTP)是活动依赖型突触可塑性的一种特殊形式,是哺乳动物学习和记忆的主要机制。协作性,输入特异性和关联性的属性对于LTP至关重要。但是,其潜在机制尚不清楚。在这里,基于实验观察到的现象,我们介绍了锥体细胞中突触可塑性的计算模型,以探索负责LTP的协同性,输入特异性和缔合性的机制。该模型基于涉及突触可塑性的分子过程,并整合了涉及神经元活动调节的基因表达。在模型中,我们在每个突触处引入了蛋白质合成的局部正反馈回路,这对于双峰反应和突触特异性至关重要。对脑源性神经营养因子(BDNF)信号的局部正反馈回路的分叉分析说明了双稳态的存在,这是LTP诱导的基础。局部分叉图为LTP的实现提供了指导,整个系统轨迹在两参数分叉图上的投影证实了从分叉分析获得的预测。此外,模型分析表明,突触前和突触后的组件是实现LTP的三个属性所必需的。这项研究提供了有关LTP的协同性,输入特异性和关联性的机制的见解,以及用于学习和记忆的神经网络的进一步构建。

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