Bridging single cell and population dynamics

描述（由申请人提供）：本研究的目的是测量单个神经元和突触的动力学如何促进神经元群体中的同步振荡活动，并了解神经调节剂乙酰胆碱如何改变细胞内和网络特性。实验将使用全细胞膜片钳技术测量内嗅皮层（EC）脑切片中神经元的动力学，沿着先进的实时实验控制。实验将由计算工作加以补充。本研究有四个目的：（1）EC中至少有三个不同的神经元群体，它们具有不同的电生理特性。将测试这些神经元是否具有不同的同步机制（例如，基于激励或抑制的同步），使用应用数学的“尖峰时间响应”（STR）方法。在STR技术中，神经元的特征在于周期性放电神经元中的尖峰如何被人工突触输入提前或延迟。(2)已知神经调质乙酰胆碱（ACh）可改变EC中神经元的放电特性，并改变脑切片中的群体节律。因此，有人假设，乙酰胆碱改变细胞的内在特性的方式，支持增强同步。将使用STR测量研究ACh激动剂对神经元的作用。(3)STR方法可以用来预测小神经元网络将如何同步。这一假设将通过使用实时控制系统来构建耦合的生物神经元和计算机建模的对应物的“混合”网络来直接测量。(4)随着神经元的数量和类型以及神经元的类型的增加，网络活动变得复杂。基于STR的模型网络的模型可以预测大型网络的行为。建模可用于理解具有多个细胞组件和更复杂的突触耦合模式的网络。

DESCRIPTION (provided by applicant): The goals of this study are to measure how the dynamics of individual neurons and synapses contribute to synchronous oscillatory activity in populations of neurons, and to understand how the neuromodulator acetylcholine changes intracellular and network properties. Experiments will measure the dynamics of neurons in brain slices of the entorhinal cortex (EC) using whole-cell patch-clamp techniques, along with advanced real-time experimental control. The experiments will be supplemented by computational work. This study has four aims: (1) There are at least three distinct neuronal populations in the EC that have distinct electrophysiological properties. It will be tested if these neurons have different mechanisms of synchronization (e.g., excitation- or inhibition-based synchronization) using 'spike time response' (STR) methods from applied mathematics. In STR techniques, neurons are characterized in terms of how spikes in periodically firing neurons are advanced or delayed by artificial synaptic inputs. (2) The neuromodulator acetylcholine (ACh) is known to alter firing properties of neurons in the EC, and change population rhythms in brain slices. Therefore, it is hypothesized that ACh changes cellular intrinsic properties in a manner that supports enhanced synchronization. The effect of ACh agonists on neurons will be studied using STR measurements. (3) STR methods can be used to predict how small neuronal networks will synchronize. This hypothesis will be directly measured by using real-time control system to construct "hybrid" networks of coupled biological neurons and computer-modeled counterparts. (4) Network activity becomes complicated with increasing number and types of neurons and types of neurons. Models from STR based model networks can predict the behavior of large networks. Modeling can be used to understand networks with multiple cellular components and more complex patterns of synaptic coupling.