THE STUDY OF NEURONAL SYNCHRONY USING COUPLED OSCILLATOR MODEL FOR BRAIN FUNCTIO

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 脑功能恢复疗法处于生物医学研究的前沿，在生物医学研究中，计算机模型和神经元之间的接口是为了补偿因阿尔茨海默氏症、创伤或痴呆症等神经疾病而造成的认知损失。现有的基于输入输出白噪声系统辨识的核估计方法存在两大缺陷。一些负责记忆巩固的海马亚区表现出内在的振荡行为，可能不能用核方法适当地表示。第二，缺乏适应突触特征的能力。在这里，我们建议使用耦合振子。它能够将神经元建模为通过生物相关耦合因子连接的自主振荡器或阈值驱动振荡器。我们推测，该模型更适合于对具有强烈振荡行为的海马区进行建模，在认知神经修复方面具有很大的应用潜力。在这个项目中，我们研究了噪声输入作为一种在选择频率下增强神经元同步性的方法的实用性。利用状态空间重构和聚类技术将仿真波形与生物数据进行比较。我们的初步结果表明，在与学习和较高认知功能相关的频率范围内，利用较小和适当范围的电场噪声可以增强对阈值下节律活动的检测。我们小组目前正在研究噪声对脉冲序列重复性的影响，以及基于计算机模拟和体外实验验证选择适当受体参数的优化技术。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Brain function restoration therapy is on the frontier of biomedical research in which the interface between computer models and neurons is created to compensate for cognitive loss due to neurological disorders such as Alzheimer's, trauma or dementia. The existing approach utilizes kernel estimation based on input-output white-noise system identification has two major shortcomings. Some hippocampal sub-regions responsible for memory consolidation exhibit intrinsic oscillatory behavior and may not be suitably represented using a kernel method. Second, it lacks the capability for adaptive synaptic characteristics. Here, we propose the use of coupled oscillators. It is capable of modeling neurons as either autonomous or threshold-driven oscillators connected through biologically-relevant coupling factors. We hypothesize that this model is more suitable for the modeling of the hippocampal regions with strong oscillatory behavior and has a great potential for the cognitive neuroprosthetic application. In this project, we investigate the utility of noisy input as a way to enhance neuronal synchronization at selective frequencies. The simulated waveforms are compared with the biological data using state space reconstruction and clustering techniques. Our preliminary result indicated that the detection of subthreshold rhythmic activities in frequency ranges associated with learning and higher cognitive functions can be enhanced with a small and appropriate range of electric field noise. Our group is currently investigating the effect of noise on pulse train reproducibility and the optimization techniques to select the appropriate receptor parameters based on the computer simulation and in vitro experimental validation.