Algorithm and neural basis of a fundamental visual motion computation

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 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.