Neural circuit mechanisms underlying hierarchical visual processing in Drosophila

果蝇分层视觉处理的神经回路机制

• 批准号: 9790935
• 负责人: Maxwell Holte Turner
• 金额: $ 6.66万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2021-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9790935
• 关键词: Address; Anatomy; Architecture; Area; Biological; Brain; Calcium; Cells; Columnar Cell; Complex; Computational algorithm; Computer Simulation; Data Set; Drosophila genus; Elements; Ensure; Environment; Exhibits; Fellowship; Functional Imaging; Genetic; Imaging Techniques; Investigation; Label; Maps; Measures; Modeling; Neural Pathways; Neurons; Optic Lobe; Output; Pathway interactions; Perception; Population; Population Heterogeneity; Property; Reporter; Research; Retina; Route; Sensory; Series; Shapes; Signal Transduction; Structure; Synapses; System; Techniques; Testing; Translations; Visual; Visual Pathways; Visual system structure; Work; cell type; connectome data; fly; insight; nervous system disorder; neural circuit; neural model; novel; object recognition; optogenetics; post-doctoral training; presynaptic neurons; receptive field; relating to nervous system; response; sensory system; visual information; visual process; visual processing; visual receptive field; visual stimulus; voltage

Project summary Understanding how neural circuits give rise to sensory computation and, ultimately, perception, requires connecting biological features of neural circuits to abstract models of neural computation. In vison, a model of the visual receptive field (RF) describes how a neuron's responses are determined by the visual inputs it encounters. The visual RF can also provide a compact description of a neuron's function, revealing which features of the external environment that neuron is responsible for encoding. Complex RFs in the visual system are responsible for high-level computations like object recognition as well as mid-level features of visual representation like size selectivity and translation invariance. The complexity of many higher-order visual circuits has made it difficult to connect models of these representations to the biological circuits that they are supposed to represent. Neural circuits across sensory systems and species often rely on convergent computational and algorithmic strategies to encode features of the external environment. This project will leverage this fact to address the question of the biological basis of complex RF structure in a tractable context, the visual system of Drosophila. In this postdoctoral training fellowship, the applicant will use state-of-the-art imaging techniques and fluorescent reporters of neural activity to describe complex RF features in neurons of the Drosophila optic lobe which project to the central brain. These results will be used to generate hierarchical models of complex RFs that are grounded in biological circuits. !

项目摘要 了解神经回路如何产生感官计算，最终需要感知，需要 将神经回路的生物学特征与神经计算的抽象模型联系起来。在Vison中，一个模型 视觉接受场（RF）描述了神经元的反应如何由视觉输入确定 相遇。视觉RF还可以提供对神经元功能的紧凑描述，揭示了哪个 神经元负责编码的外部环境的功能。视觉系统中的复杂RF 负责高级计算，例如对象识别以及视觉的中级特征 表示大小选择性和翻译不变性。许多高阶视觉电路的复杂性 很难将这些表示模型连接到他们应该的生物电路 代表。 跨感觉系统和物种的神经回路通常依赖于收敛的计算和 编码外部环境特征的算法策略。这个项目将利用这一事实 在易于处理的环境下解决复杂RF结构的生物学基础的问题 果蝇。在这项博士后培训奖学金中，申请人将使用最先进的成像技术和 神经活动的荧光记者描述果蝇神经元中复杂的RF特征 哪个项目向中央大脑。这些结果将用于生成复杂RF的分层模型 基于生物电路。 呢