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完成的，这些细胞形成了兴奋性和抑制性神经元（或兴奋性脑细胞）的功能网络，产生有节奏的输出。这些与特定行为状态相关的群体振荡的存在取决于参与细胞及其连接的特定属性。如果我们要理解大脑节律的起源和控制，有多少细胞和哪些特性是至关重要的，这是我们需要解决的基本问题。此外，已知不同种群的抑制细胞的贡献在许多脑节律的产生中是必不可少的。海马体是一种相对简单的大脑皮层结构，在学习和记忆中起着重要作用。在本提案中，我们使用体外海马制剂的可用性，该制剂在没有药理学操作的情况下产生种群振荡（因此与整个动物相关），以了解大脑振荡。这些振荡在频率和药理学反应方面模仿行为相关的振荡。它们也被认为是严重依赖于抑制性细胞的特性。我们将使用一种新开发的方法，该方法允许从细胞内记录中对脑细胞的许多输入进行统计表征。结合先前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，并了解大脑节律是如何产生的。

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.