Adaptive functions of visual systems

Introduction

The processing of visual information allows humans, animals, and computer-vision based machines to navigate the world. All visual systems face common challenges when the world rapidly changes. Such changes are often generated by an animal’s own movement.

Self-Motion and Visual Challenges

Self-motion, for example, causes fast changes in illumination and generates global motion patterns on the eye, due to the movement of the world relative to the observer. Diverse visual systems face these common challenges but must also deal with important differences.

Environmental and Behavioral Differences

1. Animals experience different environments.
2. Animals show different types of behavior, such as walking or flying, which will alter the visual cues that the animal encounters.

Project Goals

The goal of AdaptiveVision is to first understand common principles of visual system function, and to then work out how diverse visual systems adapt to specific environmental and behavioral constraints. To achieve this, AdaptiveVision will study two essential visual computations:

1. The robust estimation of contrast in dynamically changing environments.
2. The encoding of global motion cues generated by self-motion.

Research Approach

For both topics, AdaptiveVision will follow a common approach:

1. We will first study the mechanisms of visual computation in D. melanogaster. This model organism allows us to identify molecular, biophysical, and circuit mechanisms of visual system function and link these back to behavior, ensuring a comprehensive understanding of visual computation.
2. A comparative approach will answer how diverse visual systems adapt to the individual constraints brought about by the environments and by the animal’s own behavior.

Genetic Models and Molecular Signatures

Developing different Drosophila species as genetic models or applying transcriptomic techniques in different Diptera will allow us to obtain molecular signatures of homologous cell types and lead toward an understanding of the molecular basis of the evolution of visual computation.