A general method to recognize and track unmarked animals within a population will enable new studies of social behavior and individuality.

Hb9 is a homeodomain-containing transcription factor that acts in combination with Nkx6, Lim3, and Tail-up (Islet) to guide the stereotyped differentiation, connectivity, and function of a subset of neurons in Drosophila. The role of Hb9 in directing neuronal differentiation is well documented, but the lineage of Hb9(+) neurons is only partly characterized, its regulation is poorly understood, and most of the downstream genes through which it acts remain at large. Here, we complete the lineage tracing of all embryonic Hb9(+) neurons (to eight neuronal lineages) and provide evidence that hb9, lim3, and tail-up are coordinately regulated by a common set of upstream factors. Through the parallel use of micro-array gene expression profiling and the Dam-ID method, we searched for Hb9-regulated genes, uncovering transcription factors as the most over-represented class of genes regulated by Hb9 (and Nkx6) in the CNS. By a nearly ten-to-one ratio, Hb9 represses rather than activates transcription factors, highlighting transcriptional repression of other transcription factors as a core mechanism by which Hb9 governs neuronal determination. From the small set of genes activated by Hb9, we characterized the expression and function of two - fd59a/foxd, which encodes a transcription factor, and Nitric oxide synthase. Under standard lab conditions, both genes are dispensable for Drosophila development, but Nos appears to inhibit hyper-active behavior and fd59a appears to act in octopaminergic neurons to control egg-laying behavior. Together our data clarify the mechanisms through which Hb9 governs neuronal specification and differentiation and provide an initial characterization of the expression and function of Nos and fd59a in the Drosophila CNS.

We present a machine learning–based system for automatically computing interpretable, quantitative measures of animal behavior. Through our interactive system, users encode their intuition about behavior by annotating a small set of video frames. These manual labels are converted into classifiers that can automatically annotate behaviors in screen-scale data sets. Our general-purpose system can create a variety of accurate individual and social behavior classifiers for different organisms, including mice and adult and larval Drosophila.

An important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion, however, this strategy is compromised because the observer must detect a moving object within the pattern of optic flow created by its own motion through the stationary background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.