Kristin Branson

TL;DR: A camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting in a planar arena finds that behavioral differences between individuals were consistent over time and were sufficient to accurately predict gender and genotype.

TL;DR: This work studied the role of MBONs in several associative learning tasks and in sleep regulation, revealing the extent to which information flow is segregated into distinct channels and suggesting possible roles for the multi-layered MBON network.

TL;DR: This work presents a machine learning–based system that can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

TL;DR: Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.

TL;DR: It is shown that combining mechanosensory and nociceptive cues synergistically enhances the selection of the fastest mode of escape locomotion in Drosophila larvae, and proposed that the multilevel multimodal convergence architecture may be a general feature of multisensory circuits enabling complex input–output functions and selective tuning to ecologically relevant combinations of cues.