MATHEMATICAL THEORY OF OSCILLATORY NEURAL NETWORKS

描述（改编自申请人的摘要）：振荡行为是 存在于脊椎动物神经系统的许多部位， 无脊椎动物 在许多情况下，人们对参与 神经元 然而，人们并不了解如何利用网络 构造动态复杂的组件，以便能够 执行适当的任务。 该提案的目的是继续 开发一套与解决这一问题有关的技术， 使用这种技术来进一步了解特定的神经 网络. 要研究的神经网络分为三类， 请在下面描述。 它们共同允许对一系列 关于振荡是如何产生的， 网络保持其特定的定时关系。 线性组织的网络包括中央模式发生器， 七鳃鳗、水蛭和爪蟾蝌蚪的运动。 这些系统有许多 特征相同，但在解剖和细胞细节上不同 physiology. 不同设计约束的研究 不同的网络可以产生管理所有这些的一般原则， 了解不同的机制，可以产生类似的 行为 小型网络研究将侧重于甲壳类STG。 特殊 感兴趣的是用于调节诸如相位差之类的量的机制 在网络内神经元的放电和 网络活动时，它是振荡的。 最终目的是了解 如何构建网络，使其在一个 各种各样的调节影响，但仍然能够调节这些 必须保留的数量，以确保 网络 这项工作是一个更大的项目的一部分， 实验 皮质和皮质样网络能够进行非常复杂的动态。 这个项目的这一部分集中在细胞和突触的性质如何 影响网络的涌现行为。 这些研究将被 在一个简单的皮质样网络的数据中， 蝗虫。 一个长期目标是了解节奏行为 参与感觉编码、处理和学习。

DESCRIPTION (Adapted from applicant's abstract): Oscillatory behavior is found in many parts of the nervous system of both vertebrates and invertebrates. In many cases, there is much known about the participating neurons. However, it is not understood how networks that make use of dynamically complicated components are constructed in order to be able to carry out their appropriate tasks. The aim of the proposal is to continue to develop a body of technique relevant to addressing this question, and to use this technique to further the understanding of particular neural networks. The neural networks to be investigated fall into three classes to be described below. Together, they permit investigation of a range of closely related questions about how oscillations are produced and how networks maintain their particular timing relationships. Linearly organized networks include the central pattern generators for locomotion in lamprey, leech and Xenopus tadpoles. These systems have many features in common, but are different in details of anatomy and cell physiology. Investigation of the different design constraints of the different networks can yield general principles governing all of these, and an understanding of the different mechanisms that can produce similar behavior. The small network studies will focus on the crustacean STG. Of special interest are mechanisms for regulating such quantities as phase differences between the firing of neurons within the network and the frequency of the network activity when it is oscillatory. The ultimate aim is to understand how networks can be constructed to be flexible in their outputs under a variety of modulatory influences, yet still be able to regulate those quantities which must be preserved for the appropriate functioning of the network. This work is part of a larger project involving modeling and experiments. Cortical and cortical-like networks are capable of very complex dynamics. This part of the project focuses on how properties of cells and synapses affect the emergent behavior of the network. The studies will be grounded in data from a simple cortical-like network, the olfactory system of the locust. One long-term goal is to understand how rhythmic behavior participates in sensory coding, processing and learning.