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Computational and experimental investigation of heterosynaptic plasticity and pattern separation in adult-born dentate granule cells

Computational and experimental investigation of heterosynaptic plasticity and pattern separation in adult-born dentate granule cells
成年齿状颗粒细胞异质突触可塑性和模式分离的计算和实验研究
批准号:
467764793
负责人:
Professor Dr. Peter Jedlicka
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
--
资助国家:
德国
项目状态:
未结题
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中文摘要
翻译
成体颗粒细胞(abGCs)具有独特的电生理特征,在海马记忆形成和模式分离中起着特殊的作用。年轻的abGCs在细胞龄4周时开始表现出兴奋性增强、突触可塑性增强和抑制性输入减少的关键阶段。此外,我们最近表明,从第5周开始,年轻的abGCs表现出同源和异突触的结构可塑性。然而,abgc的这些特性及其对模式分离的贡献之间的相互作用尚未得到充分理解,并且从未在生物学现实模拟中建模。因此,我们将解决以下主要问题:年轻abgc的特征属性如何塑造其输入输出转换,从而影响单细胞水平的模式分离?主要目标是更好地理解生物物理和计算规则,这些规则推动了在齿状回中建立年轻的abgc作为有效的模式分离器。我们的具体问题是:观察到的较低的突触密度结合较高的兴奋性对年轻abGCs的输入-输出功能的功能后果是什么?年轻abGCs的特征如何影响其放电的稀疏性?年轻abGCs中独特的离子通道表达如何调节其输入输出功能、突触可塑性和模式分离?已知的离子通道表达、抑制和nmda受体亚基表达在年轻abGCs中的差异能否完全解释年轻abGCs与成熟abGCs相比增强的同突触可塑性?年轻abGCs的同突触增强(LTP)和异突触减弱(LTD)如何影响其模式分离性能?同突触LTP和异突触LTD在个体年轻abGCs水平上是否平衡?关于稀疏颗粒细胞放电和模式分离,内在和外在(突触)参数之间是否存在协同或简并(即部分冗余)?参数空间中可变性的实际范围是什么,这将解释实验观察到的可变性?为了回答这些问题,我们有以下目标。采用计算和电生理相结合的方法,我们的目标是:(1)改进和扩展现有的成熟GCs和年轻abGCs的室室模型;(2)建立年轻abGCs同突触可塑性增强的模型,并分析其潜在机制;(3)建立年轻abGCs异突触可塑性模型;(4)研究年轻abGCs独特的内/外特性及其异突触可塑性在稀疏放电和模式分离中的计算作用;(5)建立模式分离的简化细胞和电路模型;(6)通过实验验证计算模型并检验其预测结果。新的和大大改进的齿状GCs模型将免费提供。
英文摘要
Adult-born granule cells (abGCs) possess unique electrophysiological features and play a special role in hippocampal memory formation and pattern separation. Young abGCs display a critical phase starting at 4 weeks of cell age when they exhibit increased excitability, enhanced synaptic plasticity, and decreased inhibitory input. Moreover, we have recently shown that from the 5th week on, young abGCs show homo- and heterosynaptic structural plasticity. However, the interplay between these properties of abGCs and their contribution to pattern separation is not sufficiently understood and has never been modeled in biologically realistic simulations. Therefore, we will address the following major questions: How do characteristic properties of young abGCs shape their input-output transformation and thereby affect pattern separation at the single cell level? The principal goal is to better understand the biophysical and computational rules, which drive the establishment of young abGCs as efficient pattern separators in the dentate gyrus. Our specific questions are: What are the functional consequences of observed lower synapse density combined with higher excitability in young abGCs for their input-output function? How do characteristic features of young abGCs affect the sparseness of their firing? How does unique ion channel expression in young abGCs modulate their input-output function, synaptic plasticity and pattern separation? Can the known differences in ion channel expression, inhibition and NMDA-receptor subunit expression in young abGCs fully explain enhanced homosynaptic plasticity in young abGCs as compared to mature abGCs? How does homosynaptic synaptic strengthening (LTP) and heterosynaptic weakening (LTD) in young abGCs affect their pattern separation performance? Are homosynaptic LTP and heterosynaptic LTD balanced at the level of individual young abGCs? Is there a synergy or degeneracy (i.e. partial redundancy) between intrinsic and extrinsic (synaptic) parameters with respect to sparse granule cell firing and pattern separation? What is a realistic range of variability in the parameter space, which would explain experimentally observed variability? To answer these questions, we have the following objectives. Using a combination of computational and electrophysiological methods we aim to: (1) improve and extend existing compartmental models of mature GCs and young abGCs; (2) develop models of enhanced homosynaptic plasticity in young abGCs and analyze its underlying mechanisms; (3) develop models of heterosynaptic plasticity in young abGCs; (4) study the computational role of unique intrinsic/extrinsic properties of young abGCs and their heterosynaptic plasticity for sparse firing and pattern separation; (5) develop reduced cellular and circuit models of pattern separation; (6) validate computational models experimentally and test their predictions. New and greatly improved models of dentate GCs will be freely available.
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