NONLINEAR SYSTEMS ANALYSIS OF HIPPOCAMPUS

海马体非线性系统分析

• 批准号: 2251081
• 负责人: THEODORE W. BERGER
• 金额: $ 18.39万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-30 至 1999-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2251081
• 关键词: brain mapping; computational neuroscience; evoked potentials; hippocampus; intercellular connection; laboratory rabbit; neural information processing

The goals of this proposal are to further develop and apply a combined theoretical and experimental approach, utilizing principles of nonlinear systems theory, for achieving a biologically-based model of the hippocampal formation. In this approach, the functional properties resulting from interaction among the elements of a neural network are quantitatively characterized as input/output functions, i.e., the kernels of a functional power series. The linear and higher-order nonlinear components of the input/output relationship are determined experimentally by stimulating afferents to the network with random inputs to generate a wide range of interactions among the network elements, simultaneously recording activity of the output neurons, and estimating the kernels using cross-correlation or other techniques. The hippocampal formation consists of five subsystems (entorhinal cortex, dentate gyrus, the CA3/4 and CA1/2 pyramidal cell regions of Ammons' horn, and the subicular cortex) interconnected through feedforward and feedback pathways. Studies will focus on entorhinal input to the dentate gyrus, and will extend to CA3/4. Entorhinal afferents to the dentate will be activated with a train of electrical impulses having randomly determined (Poisson) inter-impulse intervals; evoked responses will be recorded electrophysiologically from dentate granule cells. Cross-correlation and a novel Laguerre expansion techniques will be used to estimate the kernels. The identical procedures will be repeated for progressively simplified in vivo and in vitro preparations in which the dentate (and later, other subsystems) are isolated from the remaining network granule cells isolated from intrinsic interneurons, and ultimately, feedback mechanisms intrinsic to granule cells (e.g., voltage-dependent conductances) isolated from the synaptic currents generated in response to the randomized input. In this manner, the biological mechanisms responsible for the nonlinearities expressed by the intact system can be identified. Models of single cells and circuits characterized experimentally will be developed using multi-dimensional Laplace transforms, allowing a progressively more complex representation of the global hippocampal system as a composite of the input/output functions of its subsystems. Our ultimate objective is to utilize such a model is to identify the functional dynamics of the hippocampus expressed at a systems level, and to investigate the relationship between those dynamics and learning-related changes in hippocampal activity recorded in behaving animals and humans.

本提案的目标是进一步发展和应用一种综合的 理论和实验方法，利用非线性原理 系统理论，以实现生物为基础的模型， 海马结构在这种方法中， 由神经网络的元素之间的相互作用产生的结果是 定量地表征为输入/输出函数，即，玉米粒 函数幂级数。线性和高阶非线性 输入/输出关系的分量是通过实验确定的 通过用随机输入刺激网络的传入神经， 网络元件之间的广泛交互，同时 记录输出神经元的活动，并使用 互相关或其它技术。海马结构由 内嗅皮层、齿状回、海马CA 3/4和CA 1/2 Ammons角的锥体细胞区和下托皮质） 通过前馈和反馈途径相互连接。研究将 专注于齿状回的内嗅输入，并将扩展到CA 3/4。 齿状回的内嗅传入神经会被一系列的 具有随机确定的（泊松）脉冲间的电脉冲 间隔;诱发反应将被记录电生理从 齿状颗粒细胞互相关和一种新的拉盖尔展开 技术将被用来估计内核。相同的程序 将重复进行逐步简化的体内和体外试验 其中齿状系统（以及后来的其他子系统） 与剩余的网络颗粒细胞分离，与内在的分离 中间神经元，最终，反馈机制固有的颗粒 细胞（例如，电压依赖性电导）与突触分离 响应于所述随机化输入而产生的电流。以这种方式， 生物机制负责的非线性表示， 可以识别完整的系统。单电池和电路模型 将使用多维数据集开发实验特性 拉普拉斯变换，允许逐渐更复杂的表示 作为输入/输出的复合物， 子系统的功能。我们的最终目标是利用这样一个 模型是确定海马表达的功能动力学 在系统层面，并研究这些之间的关系， 动态和学习相关的变化，海马活动记录， 行为动物和人类。