Software and hardware tools for hybrid systems analysis

用于混合系统分析的软件和硬件工具

• 批准号: 6847987
• 负责人: Ronald L Calabrese
• 金额: $ 28.65万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2007-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6847987
• 关键词: Hirudinea; biological models; biotechnology; brain regulatory center; computer data analysis; computer program /software; computer simulation; computer system design /evaluation; heart; interneurons; mathematical model; model design /development; nervous system; tissue engineering

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.

描述（申请人提供）：大脑功能，例如信息 处理，记忆形成和运动控制通常涉及振荡 神经元网络。数学建模在 了解如何产生这种振荡以及如何产生这种振荡 有助于神经系统功能。动态电流夹具技术（锋利 等，1993）允许计算机模型与神经元的相互作用 通过人造突触的网络打开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 纳入神经元的生物物理逼真的数学模型和 网络进入混合系统可行。尽管如此，混合动力系统还是很难 没有模型简化的构造。模拟非常大规模整合 电路（AVLSI）模型克服了数字模型的速度限制，但是 到目前为止 参数变化是其数字对应物。特别是在研究 振荡性神经网络，具有与模型神经元相关的模型神经元的混合系统 真正的对应物结合了数学建模和真实的优势 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 时代的心跳在水ech中朝着开发有用的混合动力的目标 研究神经元网络中振荡的系统。目标1。发展 实时动态夹具和心脏中间神经元的数学模型 模型单元在控制器板上实时运行。目标2。发展 AVLSI（硅）心脏中间神经模型。目标3。混合系统的构建 用于研究半中心振荡器的底面振荡器中心模式 一代。一旦开发了这些工具，我们将在R33阶段使用它们 探索有关相互振荡产生的基本问题 抑制性神经元网络。目标1：继续开发硅 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 基于动态夹。目标3：系统地探索混合动力的动力学 半中心振荡器。此类工具的开发将使混合体系 分析易于访问，从而进一步进一步了解神经元网络 各种制剂中的动态。