THE STUDY OF NEURONAL SYNCHRONY USING COUPLED OSCILLATOR MODEL

利用耦合振荡器模型研究神经元同步性

• 批准号: 8168137
• 负责人: Alan Wing Lun Chiu
• 金额: $ 5.55万
• 依托单位: LOUISIANA STATE UNIV A&M COL BATON ROUGE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8168137
• 关键词: Address; Biological Neural Networks; Characteristics; Collaborations; Complex; Computer Retrieval of Information on Scientific Projects Database; Computer Simulation; Coupled; Coupling; Development; Electric Stimulation; Frequencies; Funding; GABA Receptor; Grant; Hippocampus (Brain); Individual; Institution; Learning; Literature; Memory; Mentors; Modeling; N-Methylaspartate; Neurons; Noise; Pathway interactions; Process; Property; Rattus; Research; Research Personnel; Resources; Source; Synaptic plasticity; System; Techniques; Therapeutic; Time; United States National Institutes of Health; Validation; design; electric field; extracellular; neural prosthesis; neurotransmitter release; relating to nervous system; research study; uptake

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. Neurons are connected in an intricate yet organized manner with multiple levels of excitatory and inhibitory pathways that control its system dynamics. The combined effect of different unit properties and coupling modes gives rise to the complex characteristics of the neural network. Recent developments in neural-prosthetic research offer hope for potential therapeutic strategy using kernel estimation modeling for electric stimulation. To address the deficiencies of the kernel technique, we propose the use of coupled oscillator model as a mean to design and apply perturbations to the neural systems. A computer model consisting of 3 possible unit types (Oscillators, Labile clock and Hourglass) is proposed. Here, we outline the current literature and our experimental process for estimating the unit characteristics of individual neurons. In collaboration with the research mentors, our group is designing topologies of small networks of rat hippocampal neurons for the validation of the model parameter estimation. The computer simulation and preliminary experimental studies suggested that frequencies related to learning and memory can be enhanced by intermediate level of electric field noise. Additional analysis on the effect of field orientation is also obtained experimentally. The effects of neurotransmitter release and uptake as well as the channel dynamics in cellular oscillations are incorporated for the first time in an oscillator model to accommodate for synaptic plasticity. The current model can now accommodate combination of AMPA, NMDA and GABA receptors. The effect of extracellular ionic concentrations on the network firing frequency with respect to the intrinsic unit frequency is also presented.

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