Computational, anatomical, and molecular principles of system-wide visual encoding

全系统视觉编码的计算、解剖和分子原理

• 批准号: 10676656
• 负责人: Timothy A Currier
• 金额: $ 6.95万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-03 至 2025-05-02
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676656
• 关键词: Address; Anatomy; Behavior; Cells; Data; Detection; Drosophila genus; Elements; Feedback; Gene Expression; Genes; Genetic; Goals; Human; Individual; Investigation; Lateral; Life; Link; Modeling; Molecular; Monitor; Morphology; Motion; Neurons; Neuropil; Ocular Prosthesis; Pattern; Physiological; Physiology; Play; Property; Randomized; Resolution; Role; Sensory; Shapes; Signal Transduction; Site; Stereotyping; Subcellular Anatomy; Sum; Synapses; System; Technology; Time; Variant; Vision; Visual; Visual Pathways; Visual System; cell type; connectome; experimental study; fly; improved; machine vision; neural; neural circuit; programs; receptive field; recruit; response; retinal prosthesis; retinotopic; spatial integration; spatiotemporal; tool; transcriptome; transcriptomics; visual processing

Abstract The computations performed by circuits in the visual system are numerous and varied, emerging from many cell types with unique combinations of synaptic inputs, molecular composition, and physiological properties. How are distinct computations distributed across neurons and circuits in the visual system, and how are anatomical and molecular features differentially engaged to support them? Addressing these fundamental questions requires a model with deep, cell-by-cell descriptions of gene expression and morphology. This project will examine computation by assessing the varied representation of visual features across cell types in the Drosophila medulla, the first site of major synaptic divergence and signal expansion in the fly visual pathway. By extending established genetic tools, this study will record visual selectivity in most of the ~70 medulla cell types in a fraction of the time required for traditional approaches. This broad physiological assessment will then be leveraged to explore how differences in wiring and gene expression relate to different visual computations. First, a linear/non-linear sum-of-inputs model, weighted according to data from the fly connectome, will describe the extent to which a neuron’s activity can be explained by its feedforward input. Second, a large-scale regression of response features with cell type-specific transcriptome data will reveal how genetic suites and individual molecules contribute to visual feature selectivity. And finally, manipulations of modulatory circuit elements will uncover how feedback and laterally connected cell types contribute to visual computation. Collectively, these experiments will describe how different forms of synaptic input and distinct genetic programs are differentially recruited to build the computations necessary for proper visual processing. The results of this study have the potential to qualitatively improve critical technologies related to the quality of human life, including retinal prosthetics and machine vision.

摘要 视觉系统中的电路所执行的计算是众多的，并且是多种多样的， 具有突触输入、分子组成和生理特性的独特组合的细胞类型。 不同的计算是如何分布在视觉系统的神经元和电路中的， 不同的解剖学和分子学特征来支持它们？解决这些基本问题 问题需要一个模型，对基因表达和形态进行深入的、逐个细胞的描述。这 项目将通过评估不同类型细胞的视觉特征的不同表现来检查计算， 果蝇延髓是果蝇视觉系统中主要突触发散和信号扩展的第一个部位 通路通过扩展已建立的遗传工具，这项研究将记录大约70个物种中的大部分的视觉选择性。 在传统方法所需的时间的一小部分髓质细胞类型。这种广泛的生理 然后，评估将被用来探索布线和基因表达的差异如何与不同的 视觉计算首先，线性/非线性输入和模型，根据飞行数据加权 连接体，将描述神经元的活动在多大程度上可以通过其前馈输入来解释。 第二，用细胞类型特异性转录组数据对反应特征进行大规模回归将揭示 基因组和单个分子有助于视觉特征选择性。最后， 调制电路元件将揭示如何反馈和横向连接的细胞类型有助于视觉 计算总的来说，这些实验将描述不同形式的突触输入和不同的 遗传程序被差异地招募来构建适当的视觉处理所必需的计算。 本研究的结果有可能从质量上改进与产品质量相关的关键技术 人类生命，包括视网膜修复和机器视觉。