Regulation and plasticity of dendritic compartmentalization features

树突区室化特征的调节和可塑性

• 批准号: RGPIN-2020-06901
• 负责人: Béïque, JeanClaude
• 金额: $ 4.23万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746572
• 关键词: Regulation; plasticity; dendritic; compartmentalization; features

Pyramidal neurons are the principal excitatory cells in the brain and they receive thousands of synaptic inputs onto their elaborate dendritic arbors. Remarkable progress has been made in recent decades outlining the integrative properties of pyramidal cell dendrites, and it is now well appreciated that one key aspect of `dendritic computation' involves a wealth on non-linear mechanisms that ultimately controls how synaptic inputs are transformed into spiking activity, therefore powerfully modulating information transfer in neural circuits. A number of recent obtained in vivo further gives credence to the idea that a central component of pyramidal cell computation involves the decoding and transmission of spatiotemporal correlations in synaptic activity. These insights support a growing consensus that neurons, by virtue of their dendrites, are highly compartmentalized information processors. Dendritic excitability is governed by ion channels in the plasma membrane, acting within the constraints of passive cable properties. Activation of voltage-gated sodium channels (VCSCs) and/or voltage-gated calcium channels (VGCCs) are involved in dendritic spike generation, while the presence of potassium channels regulates key aspects of these events. Whereas the computational operations supported by dendrites are themselves highly dynamic in nature, the collective rules guiding these operations in a given neuron have historically largely (though not exclusively) been conceptualized as being static. However, neuromodulators can phasicaly modulate a vast array of active ionic conductances. As such, despite its well-recognized importance, the potential role of neuromodulator systems to dynamically and bi-directionally regulate the rules governing integrative features of dendrites throughout the brain remains largely unexplored experimentally. The disclosure of such a function would provide critical insights into the role played by these modulatory transmitters in regulating information processing in the brain. The overall objective of the current research program is :1) to identify and characterize the catalogue of dendritic operation and compartmentalization features of dendrite function by using a combination of cellular electrophysiology, two-photon (2P) imaging and uncaging of caged analogs of neurotransmitters and to identify how this set of operations is dynamically regulated by neurotransmission; 2) to understand how dendritic events are involved in a novel form of synaptic plasticity we have recently begun to investigate that binds events separated by hundreds of milliseconds. This novel plasticity offers an intriguing solution to the well-known temporal credit assignement problem of modern learning theories. We will investigate with sophisticated and state-of-the-art modeling approaches the computational meanings of our findings.

锥体神经元是大脑中主要的兴奋性细胞，它们接受成千上万的突触输入到它们精心制作的树突状乔木上。最近几十年来，在概述锥体细胞树突的整合特性方面取得了显着的进展，现在人们很好地认识到，“树突计算”的一个关键方面涉及大量的非线性机制，这些机制最终控制突触输入如何转化为尖峰活动，从而有力地调节神经回路中的信息传递。最近在体内获得的一些结果进一步证实了这样一种观点，即锥体细胞计算的中心组成部分涉及突触活动中时空相关性的解码和传输。这些见解支持了一个日益增长的共识，即神经元凭借其树突是高度分隔的信息处理器。树突的兴奋性是由质膜中的离子通道，在被动电缆性能的约束内发挥作用。电压门控钠通道（VCSC）和/或电压门控钙通道（VGCC）的激活参与树突棘的产生，而钾通道的存在调节这些事件的关键方面。尽管树突支持的计算操作本身在本质上是高度动态的，但在给定神经元中指导这些操作的集体规则在历史上很大程度上（但不完全）被概念化为静态的。然而，神经调节剂可以相位地调节大量的活性离子电导。因此，尽管其公认的重要性，神经调质系统的潜在作用，以动态和双向调节的规则，整个大脑的树突的整合功能仍然在很大程度上未被探索的实验。这种功能的披露将为这些调节性递质在调节大脑中的信息处理过程中所扮演的角色提供重要的见解。 目前研究计划的总体目标是：1）通过使用细胞电生理学，双光子（2 P）成像和神经递质的笼状类似物的释放的组合来识别和表征树突操作和树突功能的区室化特征的目录，并识别这组操作如何被神经传递动态调节; 2）了解树突事件如何参与我们最近开始研究的一种新形式的突触可塑性，该突触可塑性结合了数百毫秒间隔的事件。这种新颖的可塑性为现代学习理论中著名的时间学分分配问题提供了一个有趣的解决方案。我们将调查与复杂的和国家的最先进的建模方法，我们的研究结果的计算意义。