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

新颖的实验和机器学习辅助技术来评估视网膜感受野功能

• 批准号: 10712234
• 负责人: Alon Poleg-Polsky
• 金额: $ 47.67万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712234
• 关键词: Amacrine Cells; Animals; Architecture; Behavioral; Biophysics; Brain; Calcium; Cell Communication; Cell physiology; Cells; Characteristics; Complex; Data; Electrophysiology (science); Elements; Glutamates; Goals; Image; Interneurons; Linear Models; Machine Learning; Maps; Masks; Methodology; Mission; Modeling; Motion; Mus; Nature; Neurons; Organism; Output; Outsourcing; Population; Positioning Attribute; Primates; Procedures; Process; Research; Rest; Retina; Retinal Ganglion Cells; Shapes; Signal Transduction; Site; Speed; Stimulus; Stream; Surveys; Techniques; Technology; Testing; Time; Translating; Visual; Visual Perception; Work; animation; cell type; computer studies; design; expectation; experimental study; extracellular; feature detection; improved; innovation; insight; machine learning model; neural circuit; novel; novel strategies; object motion; receptive field; response; simulation; spatiotemporal; theories; visual information; visual processing

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.

项目概要 在小鼠视网膜中，大约 40 种类型的视网膜神经节细胞 (RGC) 向其余细胞传递视觉信息 大脑的。在 RGC 将其输出发送到下游之前，需要进行大量处理。一些研资局 有选择地对较小范围的形状、对比度和运动方向做出反应，或者更喜欢局部刺激 其移动方式与周围环境不同。这些计算由之间的相互作用支持 一百多个中间神经元的相互作用产生了描述神经元的感受野（RF） 刺激与 RGC 反应之间的关系。 然而，尽管该领域最近取得了重大进展，但我们仍然不知道检测到了哪些视觉特征 大多数 RGC 类型。进步的一个障碍是当前研究射频成分的技术， 要么需要长时间的记录时间、具有挑战性的实验技术，要么无法检测到关键的射频 成分。我们对神经回路组织如何转化的概念理解也有限 功能以及哪些 RF 图案会产生特定的视觉计算。 在这个提案中，我们将采取一种创新的方法，结合机器学习技术、生物物理学 逼真的建模、电生理学和谷氨酸/钙成像来开发全面的描述 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增进我们对基本和高级反应的理解 视觉活跃细胞的特征，为检查神经元功能开辟了新视野 超出视网膜。