What Determines Thalamic Spatio-Temporal Properties?

什么决定丘脑时空特性？

• 批准号: 7668529
• 负责人: EHUD KAPLAN
• 金额: $ 41.15万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7668529
• 关键词: Address; Behavior; Brain Diseases; Calculi; Cell Nucleus; Complex; Computer Simulation; Computers; Epilepsy; Feedback; Felis catus; Future; Goals; Kinetics; Knowledge; Lateral Geniculate Body; Lead; Measurement; Measures; Modeling; Neurons; Neurosciences; Parkinson Disease; Pathology; Pathway interactions; Pattern; Physiological; Play; Population; Property; Research; Retina; Retinal; Role; Structure; Synapses; Thalamic structure; Visual system structure; innovation; insight; parallel computing; receptive field; research study; simulation; spatiotemporal; supercomputer; visual information; ward

DESCRIPTION (provided by applicant): How do thalamic neurons integrate their various feedforward and feedback inputs? Most of the inputs and synapses in the mammalian lateral geniculate nucleus (LGN) are extra-retinal, but the way in which these diverse inputs are integrated to control the flow of visual information from retina to cortex is not understood. In particular, the influence of the descending inputs from the cortex and the perigeniculate nucleus (PGN) on the spatiotemporal properties of receptive fields of LGN relay neurons is unknown, although the size and complexity of these inputs strongly suggest their importance, without them, the LGN might arguably be unnecessary. To address this knowledge gap, we shall combine physiological experiments with computational modeling to achieve the following aims: 1) Measure the spatiotemporal structure of receptive fields in the cat LGN before and during (reversible) inactivation of the descending feedback pathway from V1; 2) Compare spatial summation in the retina with that of LGN neurons with and without V1 feedback; 3) Measure the temporal transfer function of neurons in layer 6 of V1; 4) Construct computational models of LGN relay neurons that incorporate the descending pathway from V1 and the PGN, and 5) Validate the models' predictions against physiological measurements. The proposed modeling, which builds on our initial (static) feedback model, will employ the innovative simulation approach of population kinetics, and will be one of the first attempts to model the corticothalamic feedback and its dynamics. It will use parallel computation on a scale not commonly found in neuroscience: two clusters of powerful computers, and a very large IBM supercomputer, which can accommodate larger, more complex models than could have been attempted in the past. The results will advance our understanding of the role that the descending inputs to the LGN play in establishing its sensitivity, dynamics, receptive field structure and discharge pattern, and will provide a necessary stepping stone for future expansions of our evolving model of the early visual system. HEALTH RELEVANCE: A more complete knowledge of how the LGN combines its diverse inputs, and especially how its temporal behavior depends on the cortex, should lead to insights into its role in dynamical brain diseases, such as epilepsy. More generally, an understanding of the role of feedback circuits will help us understand other dynamical pathologies, such as Parkinson's disease.

描述（由申请人提供）：丘脑神经元如何整合其各种饲料和反馈输入？哺乳动物外侧元基核（LGN）中的大多数输入和突触都是视网膜外的，但是这些不同的输入被整合以控制视觉信息从视网膜到皮质的视觉信息流动。特别是，从皮质和周围的核（PGN）降临的输入对LGN继电器神经元接受场的时空特性的影响是未知的，尽管这些输入的大小和复杂性强烈表明它们的重要性，而没有它们，LGN可能是不必要的。为了解决这一知识差距，我们应将生理实验与计算建模相结合，以实现以下目的：1）在（可逆）灭活v1之前和期间（可逆）灭活cat lgn中接受场的时空结构。 2）将视网膜中的空间总和与具有和没有V1反馈的LGN神经元的空间总和进行比较； 3）测量V1第6层中神经元的时间传递函数； 4）结合了从V1和PGN的下降途径的LGN中继神经元的计算模型，以及5）验证模型针对生理测量的预测。拟议的建模建立在我们的初始（静态）反馈模型的基础上，将采用人群动力学的创新模拟方法，并将成为模拟皮质丘脑反馈及其动力学的首批尝试之一。它将使用神经科学中通常不常见的比例尺使用并行计算：两个强大的计算机簇和一个非常大的IBM超级计算机，该计算机可以容纳比过去尝试过的更大，更复杂的模型。结果将促进我们对LGN降级输入在建立其敏感性，动态，接受场结构和放电模式中的作用的理解，并将为我们早期视觉系统不断发展的模型的未来扩展提供必要的垫脚石。健康相关性：更完整地了解LGN如何结合其多样化的投入，尤其是其时间行为如何取决于皮质，这应该导致对其在动态脑疾病中的作用，例如癫痫。更一般地，了解反馈电路的作用将有助于我们了解其他动态病理，例如帕金森氏病。