Collaborative Research: Decorrelation of natural inputs in lateral geniculate nucleus of behaving monkeys

离开眼睛的信息首先会遇到一种叫做外侧膝状核(LGN)的结构。从LGN，信息被传递到大脑皮层，那里的神经元具有与我们的感知经验密切相关的特性。为了了解大脑皮层如何获得其非凡的能力，需要从LGN获得关于其输入的数据。当我们观察世界上的物体时，外侧膝状核(LGN)中的神经元受到称为感受野的小空间区域的刺激。该项目将记录执行视觉任务的猴子LGN神经元的电反应。将根据LGN神经元对简单形式的反应来构建LGN神经元感受野的线性数学模型。这些模型将被用来预测LGN细胞对自然场景的反应。此外，还将评估感受野周围空间区域的影响。这些周围区域预计会减少LGN神经元的活动，从而使它们更有效率，并节省能量。除了提高对人类感知机制的理解外，这些结果还可能有助于产生关于人工视觉系统的改进设计的想法，这些系统在工业上有广泛的应用。该合作项目将为德克萨斯大学的一名神经科学研究生提供培训，还将为波士顿大学进行数学建模的研究生提供培训和数据。

Information that leaves the eye first encounters a structure called the lateral geniculate nucleus (LGN). From the LGN, information is relayed to the cortex, where neurons have properties closely related to our perceptual experience. To understand how the cortex acquires its remarkable abilities, data on its inputs from the LGN are needed. When we view objects in the world, the neurons in the lateral geniculate nucleus (LGN) are stimulated by small regions of space called their receptive fields. This project will record the electrical responses of the LGN neurons of monkeys performing a visual task. Linear mathematical models of the receptive fields of LGN neurons will be constructed based on their responses to simple forms. These models will be used to predict responses of the LGN cells to natural scenes. In addition, the effects of regions of space surrounding the receptive fields will be assessed. These surrounding regions are expected to reduce the activity of the LGN neurons so that they are more efficient and they conserve energy. In addition to improved understanding of the mechanisms underlying human perception, the results may help to generate ideas about improved designs of artificial vision systems that have wide applications in industry. This collaborative project will provide training for a graduate student in neuroscience at the University of Texas and will also provide training and data for graduate students doing mathematical modeling at Boston University.