Algorithm and neural basis of a fundamental visual motion computation

基本视觉运动计算的算法和神经基础

• 批准号: 9079038
• 负责人: Damon Alistair Clark
• 金额: $ 40.49万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9079038
• 关键词: Algorithms; Animal Behavior; Animal Model; Animals; Behavior; Behavioral Assay; Blindness; Brain; Calcium; Cells; Computational algorithm; Computer Systems; Cues; Custom; Detection; Devices; Discontinuous Capillary; Drosophila genus; Eye; Genetic; Goals; Image; Individual; Influentials; Insecta; Lead; Mammals; Maps; Measurement; Measures; Modeling; Motion; Motion Perception; Mus; Neurons; Organism; Partner in relationship; Peripheral; Primates; Property; Psychophysics; Research; Retina; Role; Rotation; Signal Transduction; Speed; Stimulus; Structure; Techniques; Translating; Vertebrates; Vision; Visual; Visual Cortex; Visual Fields; Visual Motion; Visual impairment; Visual system structure; Work; base; behavior measurement; behavioral study; detector; fly; improved; in vivo; mathematical analysis; movie; neural circuit; neuromechanism; neuron component; novel; public health relevance; relating to nervous system; research study; response; retinal prosthesis; sensory input; tool; two-photon; visual stimulus; voltage; walking speed

 DESCRIPTION (provided by applicant): Visual motion perception is critical to animals. It guides a wide range of behaviors, from navigation and predator avoidance to mating. This project proposes to dissect a newfound visual motion computation in the fruit fly Drosophila, a model organism with powerful genetic tools that can define the roles of individual neurons in neural computations. With these tools, this research will map out the neurons that compute the new motion signal and the way they compute it. This work is significant for two reasons. First, this new motion computation is a different algorithm from classical motion estimation. Classical estimation is well-described by the Hassenstein-Reichardt correlator (HRC), an influential model that has long guided research on visual motion detection in insects, and whose descendants guide motion perception research in animals from mice to primates. The motion-guided behavior studied here appears to be fundamental, but the motion signal is computed in a qualitatively different way from the HRC model's predictions. This new algorithm and its implementation will add to the suite of potential visual motion detectors. Because of the parallels in visual computations between flies and vertebrates, it is likely that vertebrate visual systems make use of a similar algorithm. Second, animals compute motion at several places in their visual systems, but it remains unclear how the different computations are used. Analysis in the compact fly brain will uncover principles for understanding the larger brains of mammals. In the fly, genetic tools and behavioral measurements can be combined to investigate three questions about its motion detection: How do its two motion systems compute different signals? How do they use overlapping circuitry? And how do they guide different behaviors? These questions lead to three main aims of this research. Aim 1 will characterize the algorithm that computes the new motion signal. Behavioral measurements and targeted visual stimuli will constrain or rule out potential models and provide a mathematical analysis of how this motion estimate is extracted from visual inputs. Aim 2 will identify neurons required for the new motion computation. Genetic tools will be used to silence specific neurons in the visual system in order to identify which neurons are required for the new computation. Aim 3 will measure the functional response properties of visual neurons in the new motion circuit. Measurements of neuron responses to stimuli will show how these neurons combine signals to generate the observed motion signals. On completion, these studies will result in a detailed understanding of the algorithm and the neural mechanisms that implement the new motion computation. The new computation and its interactions with the classical motion detector will provide a template for understanding how multiple motion signals are generated and used by the brain.

 描述（由申请人提供）：视觉运动感知对动物至关重要。它指导着广泛的行为，从导航和躲避捕食者到交配。该项目提出解剖果蝇中新发现的视觉运动计算，果蝇是一种具有强大遗传工具的模式生物，可以定义单个神经元在神经计算中的作用。有了这些工具，这项研究将绘制出计算新运动信号的神经元及其计算方式。首先，这种新的运动计算是一种不同于经典运动估计的算法。经典估计由Hassenstein-Reichardt相关器（HRC）很好地描述，这是一个有影响力的模型，长期以来一直指导昆虫视觉运动检测的研究，其后代指导从小鼠到灵长类动物的运动感知研究。这里研究的运动引导行为似乎是基本的，但是运动信号的计算方式与HRC模型的预测在性质上不同。这种新的算法及其实现将增加到潜在的视觉运动检测器套件。由于果蝇和脊椎动物在视觉计算方面的相似之处，脊椎动物的视觉系统很可能使用类似的算法。第二，动物在视觉系统的几个地方计算运动，但目前还不清楚如何使用不同的计算。对紧凑型苍蝇大脑的分析将揭示理解哺乳动物更大大脑的原理。在飞行中，遗传工具和行为测量可以结合起来研究关于其运动检测的三个问题：它的两个运动系统如何计算不同的信号？他们如何使用重叠电路？他们如何引导不同的行为？这些问题导致了本研究的三个主要目标。目标1将描述计算新运动信号的算法。行为测量和目标视觉刺激将约束或排除潜在的模型，并提供如何从视觉输入中提取该运动估计的数学分析。目标2将识别新运动计算所需的神经元。遗传工具将用于沉默视觉系统中的特定神经元，以确定新计算所需的神经元。目的3将测量新运动回路中视觉神经元的功能反应特性。神经元对刺激的反应的测量将显示这些神经元如何组合联合收割机信号以产生所观察到的运动信号。完成后，这些研究将导致对实现新运动计算的算法和神经机制的详细理解。新的计算及其与经典运动检测器的相互作用将为理解大脑如何生成和使用多个运动信号提供模板。