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的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 脑功能恢复疗法是生物医学研究的前沿，其中计算机模型和神经元之间的界面被创建以补偿由于阿尔茨海默氏症、创伤或痴呆等神经系统疾病引起的认知丧失。现有的基于核估计的输入输出白噪声系统辨识方法存在两大缺点。一些负责记忆巩固的海马子区域表现出内在的振荡行为，可能不适合使用内核方法表示。第二，它缺乏适应性突触特征的能力。在这里，我们建议使用耦合振荡器。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或阈值驱动振荡器。我们推测该模型更适合于具有强振荡行为的海马区域的建模，并且在认知神经假体的应用中具有很大的潜力。在这个项目中，我们调查的工具，嘈杂的输入作为一种方式，以提高神经元同步在选择的频率。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了比较。我们的初步研究结果表明，检测阈下节律活动的频率范围内与学习和更高的认知功能，可以增强与一个小的和适当的范围内的电场噪声。我们的团队目前正在研究噪声对脉冲序列再现性的影响，以及基于计算机模拟和体外实验验证来选择合适的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.