Flies and humans share a motion estimation strategy that exploits natural scene statistics.

Sighted animals extract motion information from visual scenes by processing spatiotemporal patterns of light falling on the retina. The dominant models for motion estimation exploit intensity correlations only between pairs of points in space and time. Moving natural scenes, however, contain more complex correlations. We found that fly and human visual systems encode the combined direction and contrast polarity of moving edges using triple correlations that enhance motion estimation in natural environments. Both species extracted triple correlations with neural substrates tuned for light or dark edges, and sensitivity to specific triple correlations was retained even as light and dark edge motion signals were combined. Thus, both species separately process light and dark image contrasts to capture motion signatures that can improve estimation accuracy. This convergence argues that statistical structures in natural scenes have greatly affected visual processing, driving a common computational strategy over 500 million years of evolution.

A versatile in vivo system for directed dissection of gene expression patterns.

Tissue-specific gene expression using the upstream activating sequence (UAS)­GAL4 binary system has facilitated genetic dissection of many biological processes in Drosophila melanogaster. Refining GAL4 expression patterns or independently manipulating multiple cell populations using additional binary systems are common experimental goals. To simplify these processes, we developed a convertible genetic platform, the integrase swappable in vivo targeting element (InSITE) system. This approach allows GAL4 to be replaced with any other sequence, placing different genetic effectors under the control of the same regulatory elements. Using InSITE, GAL4 can be replaced with LexA or QF, allowing an expression pattern to be repurposed. GAL4 can also be replaced with GAL80 or split-GAL4 hemi-drivers, allowing intersectional approaches to refine expression patterns. The exchanges occur through efficient in vivo manipulations, making it possible to generate many swaps in parallel. This system is modular, allowing future genetic tools to be easily incorporated into the existing framework.