Algorithm and neural basis of a fundamental visual motion computation

 描述(申请人提供)：视觉运动知觉对动物至关重要。它指导着一系列的行为，从导航、躲避捕食者到交配。该项目计划解剖果蝇的一种新发现的视觉运动计算，果蝇是一种拥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角色。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。这项工作之所以意义重大，有两个原因。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。Hassenstein-Reichardt Correlator(HRC)很好地描述了经典估计，这是一个有影响力的模型，长期以来一直指导昆虫的视觉运动检测研究，其后代指导从老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，但运动信号的计算方式与HRC模型的预测有本质上的不同。这一新的算法及其实现将增加潜在的视觉运动检测器套件。由于苍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。其次，动物在视觉系统中的几个位置计算运动，但尚不清楚不同的计算是如何使用的。对紧凑型苍蝇脑的分析将揭示理解哺乳动物较大大脑的原理。在飞行中，遗传工具和行为测量可以结合起来调查关于它的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？这些问题引出了本研究的三个主要目的。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限制或排除潜在的模型，并提供如何从视觉输入中提取运动估计的数学分析。目标2将确定新的运动计算所需的神经元。基因工具将被用来让视觉系统中的特定神经元保持沉默，以确定新的计算需要哪些神经元。目的3将测量视觉神经元在新的运动回路中的功能反应特性。对神经元对刺激反应的测量将显示这些神经元是如何组合信号来产生观察到的运动信号的。完成后，这些研究将导致对算法和实现新运动计算的神经机制的详细理解。新的计算及其与经典运动检测器的交互将为理解大脑如何产生和使用多个运动信号提供一个模板。

 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.