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.

项目摘要 在小鼠视网膜中，大约40种类型的视网膜神经节细胞（RGC）将视觉信息传达给其余 大脑。在RGC将其下游输出发送之前，进行了大量处理。一些RGC 选择性地应对狭窄的形状，对比和运动方向或更喜欢局部刺激 那与周围环境有所不同。这些计算由 一百多个中间神经元的相互作用引起了描述的接收场（RF） 刺激对RGC响应的关系。 但是，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哪些视觉功能 大多数RGC类型。进步的一个障碍是研究RF组成的当前技术，这是 要么需要延长记录会话，具有挑战性的实验技术，要么无法检测到关键的RF 成分。我们在对神经电路组织如何翻译的概念上也受到限制 功能以及什么RF主题引起了特定的视觉计算。 在此提案中，我们将采用一种创新的方法，将机器学习技术结合在一起 现实的建模，电生理学和谷氨酸 /钙成像，以开发全面的描述 在复杂的视觉场景中多种RGC​​类型的视觉能力，这些视觉景观基于经验数据。 拟议的研究将大大提高我们对基本和高级响应的理解 视觉活性细胞的特征，在检查神经元功能和 超越视网膜。