NONLINEAR SYSTEMS ANALYSIS OF HIPPOCAMPUS

本提案的目标是进一步开发和应用一种组合的 理论和实验相结合，运用非线性原理 系统论，用于实现基于生物学的生物模型 海马体结构。在这种方法中，函数属性 由神经网络元素之间的相互作用产生的结果是 定量地描述为输入/输出函数，即核 一个功能性的级数。线性和高阶非线性 通过实验确定输入/输出关系的组成部分 通过用随机输入刺激网络的传入以产生 网络元素之间的广泛交互，同时 记录输出神经元的活动，并使用 互相关或其他技术。海马体结构由 五个子系统(内嗅皮层、齿状回、CA3/4和CA1/2 阿蒙斯角锥体细胞区和皮质下皮质) 通过前馈和反馈路径相互连接。研究将会 将重点放在齿状回的内嗅觉输入，并将延伸至CA3/4。 齿状回的内嗅觉传入将被一系列的 具有随机确定的(泊松)间脉冲的电脉冲 间期；诱发反应将从电生理上记录下来 齿状颗粒细胞。互相关和一种新的拉盖尔展开 将使用技术来估计内核。相同的程序 将在体内和体外重复以逐步简化 其中齿状突起(以及后来的其他子系统) 从剩余的网络颗粒细胞中分离出固有的 中间神经元，最终，颗粒固有的反馈机制 从突触分离的细胞(例如，电压依赖的电导) 响应随机输入而产生的电流。以这种方式， 所表达的非线性的生物学机制 完整的系统可以被识别出来。单电池和电路的模型 以实验为特征的将使用多维开发 拉普拉斯变换，允许逐步更复杂的表示 作为输入/输出的复合体的整体海马体系统 其子系统的功能。我们的最终目标是利用这样一个 模型是鉴定海马区的功能动力学表达 在系统层面上，并调查它们之间的关系 记录的海马区活动的动力学和学习相关的变化 表现出动物和人类的行为。

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.