Integrating visual counterevidence to detect self-motion in a small visual circuit

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Animals perceive optic flow and use it to guide a range of navigational behaviors, such as stabilizing body trajectories and eye movements. In this project, we propose to dissect how different visual cues are combined to guide stabilizing behaviors, and how these cues serve as evidence for and against an animal’s self-motion. We will investigate these computations in the fruit fly Drosophila, which allows us to use powerful genetic tools to define the roles of individual neurons in neural computations. With these tools, our research will (a) characterize the algorithms that combine types of visual evidence, (b) identify the circuits that encode and combine these cues, and (c) determine how neurons perform these computations. This work is significant for two reasons. First, our studies will examine how the fly integrates visual evidence both for and against its own self-motion. This form of counterevidence integration cannot be accounted for by current models of optic flow detection. Thus, this project will establish new approaches to studying how animals estimate their own self-motion. Second, the types of visual evidence for and against self-motion are constrained by the geometry of the visual world. Because of this common geometry and parallels in visual processing between flies and mammals, it is likely that mammalian visual systems make use of algorithms similar to those we will find in the fly. Thus, analysis in the compact fly brain will uncover principles for understanding these computations in the brains of larger animals. In our research, we will combine genetic tools and behavioral measurements to investigate three questions: How do different visual cues interact in stabilizing the fly’s heading? What circuitry is required for the different types of cues? And how do the circuits process and integrate these cues to generate behavior? These questions lead to the three aims of our research. Aim 1 characterizes the algorithm that integrates evidence for and against the fly’s self-motion and guides its turning behavior. Behavioral measurements with targeted visual stimuli will constrain or rule out potential models. Aim 2 identifies neurons required to integrate visual counterevidence into turning behavior. We will use genetic tools to silence specific neurons in the visual system in order to identify neurons required for this computation. Aim 3 measures functional response properties of visual neurons in the circuits that encode and integrate these visual cues. We will use measurements of neuron responses to stimuli to test hypotheses for how these neurons combine signals to generate the observed motion signals and behaviors. On completion, these studies will result in a detailed understanding of the algorithms and neural mechanisms that integrate different visual cues to stabilize heading in flies. This will provide a template for understanding how visual evidence is combined to estimate self-motion in other animals as well.