What Determines Thalamic Spatio-Temporal Properties?

什么决定丘脑时空特性？

• 批准号: 7898046
• 负责人: EHUD KAPLAN
• 金额: $ 23.04万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7898046
• 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前馈和反馈输入？哺乳动物外侧膝状体核（LGN）中的大多数输入和突触是视网膜外的，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层的流动的方式尚不清楚。特别是，下行输入从皮层和周膝状体（PGN）的LGN中继神经元的感受野的时空特性的影响是未知的，虽然这些输入的大小和复杂性强烈表明他们的重要性，没有他们，LGN可能是不必要的。为了弥补这一知识缺口，我们将联合收割机与计算机模拟相结合，以实现以下目标：1）测量猫LGN感受野的时空结构，（2）比较视网膜和LGN神经元在有和没有V1反馈时的空间总和; 3）测量V1第6层神经元的时间传递函数; 4）构建LGN中继神经元的计算模型，该模型包含来自V1和PGN的下行通路，以及5）将模型的预测与生理测量进行比较。建议的建模，它建立在我们的初始（静态）反馈模型，将采用创新的模拟方法的人口动力学，并将是第一次尝试模拟皮质丘脑反馈及其动力学。它将使用在神经科学中不常见的规模上的并行计算：两个强大的计算机集群和一个非常大的IBM超级计算机，它可以容纳比过去更大，更复杂的模型。这些结果将促进我们对LGN的下行输入在建立其敏感性，动态，感受野结构和放电模式中所起作用的理解，并将为我们未来扩展早期视觉系统的发展模型提供必要的垫脚石。健康相关性：更完整地了解LGN如何结合其不同的输入，特别是它的时间行为如何依赖于皮层，应该有助于了解它在动力性脑疾病（如癫痫）中的作用。更一般地说，了解反馈回路的作用将有助于我们了解其他动力学病理，如帕金森病。