Novel experimental and machine learning - assisted techniques to assess receptive field functionality in the retina

项目总结 在小鼠的视网膜中，大约有40种类型的视网膜神经节细胞(RGC)与其他细胞交流视觉信息 大脑的一部分。在RGC将其输出的产品向下游输送之前，需要进行大量的处理。一些RGC 有选择地对狭窄的形状、对比度和运动方向做出反应，或更喜欢局部刺激 它们与周围环境的运动方式不同。这些计算得到以下交互作用的支持 一百多个中间神经元，它们的相互作用产生了描述 刺激与研资局反应之间的关系。 然而，尽管最近在该领域取得了重大进展，我们仍然不知道检测到了哪些视觉特征 按大部分研资局类型划分。进步的一个障碍是目前研究射频成分的技术，这 要么需要长时间的录制，要么需要挑战实验技术，要么无法检测到关键的射频 组件。我们对神经回路组织如何翻译的概念性理解也受到限制 以发挥作用，以及什么RF主题引起特定的视觉计算。 在这项提案中，我们将采取一种创新的方法，将机器学习技术与生物物理学相结合 逼真的建模、电生理学和谷氨酸/钙成像，以开发全面的描述 在基于经验数据的复杂视觉场景中，多种RGC类型的视觉能力。 拟议的研究将极大地促进我们对基本和高级反应的理解 视觉活性细胞的特征，开辟了神经功能检测的新领域 在视网膜之外。

PROJECT SUMMARY In the mouse retina, about 40 types of retinal ganglion cells (RGCs) communicate visual information to the rest of the brain. A great deal of processing takes place before RGCs send their output downstream. Some RGCs respond selectively to a narrow range of shapes, contrasts, and directions of motion or prefer localized stimuli that move differentially from their surroundings. These computations are supported by interactions between more than a hundred interneurons whose interactions give rise to the receptive fields (RFs) that describe the relationship between the stimulus to the response of the RGC. However, despite significant recent advances in the field, we still do not know what visual features are detected by the majority of RGC types. One obstacle to progress is current techniques to study RF composition, which either require prolonged recording sessions, challenging experimental techniques, or fail to detect crucial RF components. We are also limited in the conceptual understanding of how neural circuit organization translates to function and what RF motifs give rise to specific visual computations. In this proposal, we will take an innovative approach that combines machine learning techniques, biophysically realistic modeling, electrophysiology, and glutamate / calcium imaging to develop a comprehensive description of the visual abilities of multiple RGC types in complex visual scenes that is grounded in empirical data. The proposed research will substantially advance our understanding of basic and advanced response characteristics of visually active cells, opening new horizons in the examination of neuronal function in and beyond the retina.