Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model
Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model
批准号:
10206078
负责人:
Richy Yun
金额:
$4.28万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-01 至 2023-07-31
关键词:
AffectAnimal ModelAnimalsArchitectureBiomedical EngineeringBiophysicsBrainBrain regionCollaborationsCommunitiesComplexComputer ModelsCortical ColumnDisciplineElectric StimulationEquipmentEssential TremorFire - disastersFrequenciesImplantIn VitroInjuryInterventionLaboratoriesLearningMacacaMediatingMental DepressionMethodsModelingMotor CortexNervous system structureNeural Network SimulationNeuronsNeurosciencesPatternPhysical RehabilitationPhysiologic pulsePhysiologicalPhysiologyPopulationPrimatesProtocols documentationReportingResearchResearch TrainingResourcesRoleScienceScientistSignal TransductionSleep StagesSpinal cord injuryStimulusStrokeSynapsesSystemTechniquesTestingTrainingTraumatic Brain InjuryUniversitiesValidationWashingtonbasechronic painclinical applicationclinically relevantconditioningexperimental studyhabituationin vivoinsightloss of functionmodels and simulationnervous system disordernetwork modelsneural circuitnonhuman primatenovelpopulation basedresponseskillstool
中文摘要
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
建模技巧除了科学之外,他还将学习如何作为一名研究科学家进行有效的沟通,
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
英文摘要
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.
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会议论文
Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model
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批准号:10449090
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项目类别:
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资助金额:$1.97万
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财政年份:2020
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负责人:Richy Yun
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依托单位:
Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model
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批准号:10065860
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项目类别:
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资助金额:$4.19万
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财政年份:2020
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负责人:Richy Yun
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依托单位:
海外基金