Software and hardware tools for hybrid systems analysis

描述（由申请人提供）：大脑功能，如信息 加工、记忆形成和运动控制通常涉及振荡 神经网络数学建模在以下方面特别有用： 深入了解这种振荡是如何产生的， 有助于神经系统功能。动态电流钳技术（夏普 等人，1993），其允许计算机模型和神经元模型的相互作用。 通过人工突触的网络开辟了一种新的建模形式， 混合系统分析计算机硬件的最新进展使 结合神经元的生物病理学上现实的数学模型， 混合系统的可行性。尽管如此，混合动力系统很难 没有模型简化。模拟超大规模集成电路 电路（aVLSI）模型克服了数字模型的速度限制，但 到目前为止，很少有超大规模集成电路模型是现实的，或适合于 参数变化作为其数字对应物。特别是在研究 振荡神经网络，具有模型神经元连接到 真实的对应物结合了数学建模和真实的的优点 电生理实验 在R21阶段，我们将扩展我们对神经元网络的研究， 在水蛭的时代心跳，朝着开发有用的杂交种的目标前进 用于研究神经元网络中振荡的系统。目标1。发展 实时动态钳位和心脏中间神经元的数学模型 在控制器板上实时运行的模型单元。目标2.的研制 aVLSI（硅）心脏中间神经元模型。目标3.混合系统的构建 用于研究水蛭心跳中心模式下的半中心振荡器 一代这些工具开发完成后，我们将在R33阶段使用它们 相互探讨振荡产生的基本问题， 抑制性神经网络目标1：继续发展硅 HN神经元的模型，并彻底确定其动力学。目标2：分析 使用控制器板的硅和活神经元的动态行为 基于动态箝位。目标3：系统地探索混合动力汽车的动力学 半中心振荡器这些工具的发展将使混合动力系统 分析容易获得，从而进一步了解我们的神经网络 各种制剂中的动力学。

DESCRIPTION (provided by applicant): Brain functions such as information processing, memory formation and motor control often involve oscillatory neuronal networks. Mathematical modeling has been particularly useful in gaining insights into how such oscillations are generated and how they contribute to nervous system function. Dynamic current clamp techniques (Sharp et al, 1993) that permit the interactions of computer models and neuronal networks through artificial synapses have opened up a new form of modeling, hybrid systems analysis. Recent progress in computer hardware makes incorporation of biophysically realistic mathematical models of neurons and networks into hybrid systems feasible. Still, hybrid systems are difficult to construct without model simplification. Analog very large-scale integrated circuit (aVLSI) models overcome the speed limitation of digital models, but until now very few aVLSI models have been as realistic or as amenable to parameter variations as their digital counterparts. Especially in studies of oscillatory neural networks, hybrid system having a model neuron connected to real counterpart combines advantages of mathematical modeling and real electrophysiological experiments. In the R21 phase, we will expand upon our research on a neuronal network that times heartbeat in the leech toward the goal of developing useful hybrid systems for the study of oscillation in neuronal networks. Aim 1. Development of a real-time dynamic clamp and a mathematical model of heart interneuron model cell running in real-time on a controller board. Aim 2. Development of an aVLSI (silicon) heart interneuron model. Aim 3. Construction of hybrid systems for studying half-center oscillators underlying leech heartbeat central pattern generation. Once these tools are developed, we will use them in the R33 phase to explore basic questions about the generation of oscillation in mutually inhibitory neuronal networks. Aim 1: Continue the development of the silicon models of a HN neuron and thoroughly ascertain their dynamics. Aim 2: Analyze dynamic behaviors of the silicon and living neurons using a controller board based dynamic clamp. Aim 3: Explore systematically the dynamics of hybrid half-center oscillators. The development of such tools will make hybrid system analysis easily accessible and thus further our insights into neuronal network dynamics in a variety of preparations.