Brain rhythms and dynamic states: control by cortical inhibitory networks

大脑中的信息处理是通过许多细胞之间的相互作用来进行的，这些细胞形成了兴奋和抑制神经元(或可兴奋的脑细胞)的功能网络，产生有节奏的输出。这些群体振荡的存在可以与特定的行为状态相关，这取决于参与细胞的特定属性及其联系。如果我们要了解大脑节奏的发生和控制，需要解决的基本问题是有多少个细胞以及哪些属性是关键的。此外，众所周知，不同群体的抑制细胞对许多大脑节奏的产生是必不可少的。海马体结构是一种相对简单的大脑皮质结构，已知在学习和记忆中起重要作用。在这项建议中，我们利用体外海马体准备的可用性来理解脑振荡。这种体外海马体准备产生群体振荡，而不需要药学操作(因此它与整个动物相关)。这些振荡在频率和药理反应方面模仿与行为相关的振荡。尽管它们也被认为严重依赖抑制细胞的特征。但我们将使用一种新开发的方法，允许从细胞内记录来统计表征脑细胞的许多输入。结合之前对兴奋性和抑制性网络模型的理论见解，我们将开发基于生物学的模型，并进行模拟，将实验数据与建模研究和理论见解直接联系起来。通过这种方式，我们将能够探索大脑动力学的非线性复杂性，并对大脑节律是如何产生的有一个理解。

Information processing in the brain is performed through interactions between many cells which form functional networks of excitatory and inhibitory neurons (or excitable brain cells) that produce rhythmic outputs.  The existence of these population oscillations which can be correlated with specific behavioural states depends on the particular properties of the participating cells and their connections.  How many cells and what properties are critical, are fundamental questions that we need to address if we are to understand the genesis and control of brain rhythms.  Furthermore, the contribution of diverse populations of inhibitory cells is known to be essential in the production of many brain rhythms. The hippocampal formation is a relatively simple cerebral cortical brain structure that is known to be important in learning and memory.  In this proposal, we use the availability of an in vitro hippocampal preparation which produces population oscillations without pharamacological manipulation (hence its relevance to the whole animal) to understand brain oscillations.  These oscillations mimic behaviourally relevant oscillations in terms of their frequency and pharmacological responses.  They are also known to be critically dependent on inhibitory cell characteristics.  We will use a newly developed method which allows the many inputs to brain cells to be statistically characterized from intracellular recordings.  Together with previous theoretical insights of excitatory and inhibitory network models, we will develop biologically-based models and perform simulations that will directly link the experimental data with the modelling studies and theoretical insights.  In this way, we will be able to probe the nonlinear complexities of brain dynamics and develop an understanding of how brain rhythms arise.