Bridging single cell and population dynamics

描述（由申请人提供）：本研究的目标是测量单个神经元和突触的动态如何促进神经元群体的同步振荡活动，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，以及先进的实时实验控制，测量内嗅皮层（EC）脑切片中神经元的动态。这些实验将辅以计算工作。本研究有四个目的：(1)EC中至少有三个不同的神经元群，它们具有不同的电生理特性。将使用应用数学中的“尖峰时间响应”（STR）方法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）。在STR技术中，神经元的特征在于周期性放电神经元的尖峰如何被人工突触输入提前或延迟。(2)已知神经调节剂乙酰胆碱（ACh）可以改变EC中神经元的放电特性，并改变脑切片中的种群节律。因此，假设乙酰胆碱以支持增强同步的方式改变细胞的内在特性。乙酰胆碱激动剂对神经元的影响将通过STR测量来研究。(3) STR方法可用于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生物神经元和计算机模拟对应的“混合”网络来直接测量。(4)随着神经元数量和类型的增加以及神经元类型的增加，网络活动变得复杂。基于STR的模型网络模型可以预测大型网络的行为。建模可以用来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.