Cellular-based Mechanisms Underlying Oscillatory Brain Dynamics

大脑是如何工作的？有节奏的大脑活动是大脑记录中的一个常见特征，现在有很多证据表明，我们大脑回路中这些节奏的产生是我们大脑组织信息的一种方式。 然而，这些节奏是如何产生的还远不清楚。 大脑的复杂网络是一个挑战，因为大脑的多尺度性质沿着其无数的生物细节。 我们非常需要数学模型来帮助我们解决这种复杂性，但是如果我们要理解生物系统，而不仅仅是数学模型系统，那么我们需要明确模型与实验的相互作用。我实验室的长期目标是确定振荡网络动力学的细胞机制，我们在研究的初始阶段将理论、实验和建模相结合。 重要的是要注意，确定基于细胞的理解是至关重要的，因为我们知道细胞特异性很重要。 在这个建议中，我专注于特定大脑结构中的特定节奏，以使我们能够包括生物学细节，并与实验研究紧密结合。 Theta（3-12 Hz）和Gamma（25-140 Hz）节律以及大脑中称为海马体的区域中的组合是我们的重点。 这是因为海马体中的这些节律不仅在记忆处理和空间导航中至关重要，而且也被证明是大脑编码方案的组成部分。 为了理解这些节律的产生，我们将建立以抑制性细胞类型为中心的神经元和神经元网络的计算模型，因为它们表达了广泛的多样性，并且以特定的方式控制着这些节律。 我们的一般方法是将我们的神经元和神经元网络模型的开发和分析与定义明确的实验环境中的数据紧密结合起来。 通过这种方式，可以测试我们模型中的预测和假设。 我们与实验学家和数学家合作，使我们能够获得实验数据，并能够利用深入的理论。考虑到这些节奏对大脑工作的重要性，我们的工作所获得的任何理解都将是神经科学界的根本利益。 同样，它将为理解神经系统疾病提供基础，当大脑回路出错并反映在这些节律的调制中时。

How does the brain work? Rhythmic brain activities are a common feature in brain recordings and there is now much evidence indicating that the generation of these rhythms in our brain circuits is a way in which our brains work to organize information. However, it is far from clear how these rhythms are generated. The complex networks of the brain are a challenge to appreciate because of the multi-scale nature of the brain along with its myriad of biological details. Mathematical models are strongly needed to help us tackle this complexity, but experimental interactions with our models need to be clear if we are to achieve an understanding of the biological system, and not only the mathematical model system. The long-term goal of my lab is to determine cellular-based mechanisms underlying oscillatory network dynamics, and we do this by combining theory, experiment and modeling at initial stages of the research. It is important to note that determining a cellular-based understanding is critical as we know that cell specifics matter. In this proposal I focus on particular rhythms in particular brain structures to allow us to include biological details and to closely interface with experimental studies. Theta (3-12 Hz) and gamma (25-140 Hz) rhythms and combinations of them in a region of the brain called the hippocampus are our focus. This is because these rhythms in the hippocampus are not only known to be essential in memory processing and spatial navigation, but have also been shown to be components of the brain's coding scheme. To understand the generation of these rhythms, we will build computational models of neurons and networks of neurons that are centered on inhibitory cell types since they express a wide diversity and are controllers of these rhythms in specific ways. Our general approach is to closely align our neuronal and neuronal network model development and analyses with data in well-defined experimental contexts. In this way, predictions and hypotheses that arise from our models can be tested. We collaborate with both experimentalists and mathematicians allowing us to have access to experimental data and to be able to harness in depth theory. Given the importance of these rhythms to brain workings, any understanding achieved by our work would be of fundamental interest to the Neuroscience community. As well, it would provide a basis to understand neurological disease when brain circuits go awry and are reflected in modulation of these rhythms.