What Determines Thalamic Spatio-Temporal Properties?

描述（由申请人提供）：丘脑神经元如何整合它们的各种前馈和反馈输入？哺乳动物外侧膝状核（LGN）中的大多数输入和突触都位于视网膜外，但这些不同的输入如何整合以控制视觉信息从视网膜流向皮层的方式尚不清楚。特别是，来自皮层和周状核（PGN）的下行输入对LGN中继神经元接受野时空特性的影响尚不清楚，尽管这些输入的大小和复杂性强烈表明它们的重要性，但如果没有它们，LGN可能是不必要的。为了解决这一知识缺口，我们将生理实验与计算建模相结合，以实现以下目标：1)测量猫LGN在V1下行反馈通路（可逆）失活之前和失活期间的感受野时空结构；2)将视网膜空间求和与有无V1反馈的LGN神经元空间求和进行比较；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，并结合V1和PGN的下行通路，5)通过生理测量验证模型的预测。该模型建立在我们的初始（静态）反馈模型的基础上，将采用群体动力学的创新模拟方法，并将成为首次尝试建立皮质丘脑反馈及其动力学模型的方法之一。它将以神经科学中不常见的规模使用并行计算：两个强大的计算机集群和一台非常大的IBM超级计算机，它可以容纳比过去更大、更复杂的模型。这一结果将有助于我们进一步理解下行输入对LGN的敏感性、动力学、感受野结构和放电模式的影响，并为进一步扩展早期视觉系统的进化模型提供必要的基础。健康相关性：更全面地了解LGN如何结合其各种输入，特别是其时间行为如何依赖于皮层，将有助于深入了解其在动态脑疾病（如癫痫）中的作用。更一般地说，对反馈回路作用的理解将有助于我们理解其他动力学病理，如帕金森病。

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.