Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model

使用集成和激发尖峰神经网络模型优化刺激诱导的皮质可塑性

• 批准号: 10065860
• 负责人: Richy Yun
• 金额: $ 4.19万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10065860
• 关键词: Affect; Animal Model; Animals; Architecture; Biomedical Engineering; Biophysics; Brain; Brain region; Collaborations; Communities; Complex; Computer Models; Cortical Column; Discipline; Electric Stimulation; Equipment; Essential Tremor; Fire - disasters; Frequencies; Implant; In Vitro; Injury; Intervention; Laboratories; Learning; Macaca; Mediating; Mental Depression; Methods; Modeling; Motor Cortex; Nervous system structure; Neural Network Simulation; Neurons; Neurosciences; Pattern; Physical Rehabilitation; Physiologic pulse; Physiological; Physiology; Population; Primates; Protocols documentation; Reporting; Research; Research Training; Resources; Role; Science; Scientist; Signal Transduction; Sleep Stages; Spinal cord injury; Stimulus; Stroke; Synapses; System; Techniques; Testing; Training; Traumatic Brain Injury; Universities; Validation; Washington; base; chronic pain; clinical application; clinically relevant; conditioning; experimental study; habituation; in vivo; insight; loss of function; models and simulation; nervous system disorder; network models; neural circuit; nonhuman primate; novel; population based; response; skills; tool

Project Summary / Abstract Cortical stimulation has become a prevalent therapy for various ailments including stroke and traumatic brain injury. Although targeted plasticity using closed-loop stimulation paradigms has been extensively characterized in vitro, mechanisms in vivo remain to be further explored. Previous results have also been difficult to compare due to differences in stimulation methods, brain regions, implants, and animal models. Thus, we will incorporate a integrate-and-fire (IF) spiking neural network model in conjunction with experimental validation to methodically compare and uncover novel conditioning paradigms as well as better understand how stimulation affects neural circuitry. The simplicity and computational efficiency of the IF neural network model allows us to quickly simulate hundreds of interconnected neurons. The model will be initialized with a spike-timing dependent plasticity (STDP) rule that reflects previous findings reported from the laboratory, as experimental results strongly suggest classic STDP mediates stimulus-induced plasticity in vivo. The proposed experiments will seek to optimize spike- triggered stimulation by discerning whether various stimulus parameters, including number of pulses and stimulation frequency, affect the induced plasticity. They will also explore novel conditioning protocols such as gamma-triggered stimulation and brain-state dependent stimulation to determine if population-based paradigms could be more effective methods of inducing plasticity. I will conduct these studies within the primary motor cortex of intact macaques for greatest clinical relevance. Experiments will be performed in conjunction with the IF neural network model simulations to coevolve the stimulation parameters and model architecture. The results of this study will provide a framework for comparing stimulation methods and inform clinical applications of cortical stimulation. Through these projects the PI will be trained in a wide array of disciplines including experimental techniques with behaving non-human primates, analytical techniques involving circuit level analyses, and computational modeling skills. Beyond science, he will also learn how to communicate effectively as a research scientist and continue participating in the neuroscience community through collaborations with different laboratories and involvement with various research centers. The research training will take place in the Fetz laboratory at the University of Washington with the facilities, equipment, and resources made available through the Department of Bioengineering, Department of Physiology & Biophysics, and the Washington National Primate Research Center.

项目总结/摘要 皮层刺激已成为一种流行的治疗各种疾病，包括中风和创伤性脑 损伤尽管使用闭环刺激范例的靶向可塑性已被广泛表征， 在体外，在体内的机制仍有待进一步探讨。此前的成绩也难以与之相比 由于刺激方法、脑区域、植入物和动物模型的差异。因此，我们将 一个集成和发射（IF）尖峰神经网络模型结合实验验证，以有条不紊地 比较和发现新的条件反射范例，以及更好地了解刺激如何影响神经 电路IF神经网络模型的简单性和计算效率使我们能够快速模拟 数百个相互连接的神经元。该模型将使用尖峰时间依赖可塑性（STDP）进行初始化 规则，反映了以前的研究结果报告，从实验室，因为实验结果强烈建议经典 STDP介导体内刺激诱导的可塑性。拟议的实验将寻求优化穗- 通过辨别各种刺激参数，包括脉冲数和 刺激频率，影响诱发可塑性。他们还将探索新的条件反射协议，如 γ触发刺激和脑状态依赖刺激，以确定是否基于人群的范例 可能是更有效的诱导可塑性的方法。我将在初级运动皮层进行这些研究 完整的猕猴来获得最大的临床意义。实验将结合IF神经网络进行。 网络模型模拟，以共同进化刺激参数和模型架构。的结果 这项研究将提供一个框架，比较刺激方法，并告知临床应用的皮质 刺激. 通过这些项目，PI将接受广泛的学科培训，包括实验技术， 行为非人类灵长类动物，包括电路水平分析的分析技术，以及计算 建模技巧除了科学，他还将学习如何有效地沟通，作为一个研究科学家， 通过与不同实验室的合作，继续参与神经科学界， 参与多个研究中心。研究培训将在位于纽约的Fetz实验室进行。 华盛顿大学的设施，设备和资源，通过该部门提供 生理学和生物物理学系，以及华盛顿国家灵长类动物研究所 中心