An Automated Paradigm for Drosophila Visual Psychophysics

Background: Mutations that cause learning and memory defects in Drosophila melanogaster have been found to also compromise visual responsiveness and attention. A better understanding of attention-like defects in such Drosophila mutants therefore requires a more detailed characterization of visual responsiveness across a range of visual parameters. Methodology/Principal Findings: We designed an automated behavioral paradigm for efficiently dissecting visual responsiveness in Drosophila. Populations of flies walk through multiplexed serial choice mazes while being exposed to moving visuals displayed on computer monitors, and infra-red fly counters at the end of each maze automatically score the responsiveness of a strain. To test our new design, we performed a detailed comparison between wild-type flies and a learning and memory mutant, dunce. We first confirmed that the learning mutant dunce displays increased responsiveness to a black/green moving grating compared to wild type in this new design. We then extended this result to explore responses to a wide range of psychophysical parameters for moving gratings (e.g., luminosity, contrast, spatial frequency, velocity) as well as to a different stimulus, moving dots. Finally, we combined these visuals (gratings versus dots) in competition to investigate how dunce and wild-type flies respond to more complex and conflicting motion effects. Conclusions/Significance: We found that dunce responds more strongly than wild type to high contrast and highly structured motion. This effect was found for simple gratings, dots, and combinations of both stimuli presented in competition

Oscillatory stimuli differentiate adapting circuit topologies

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this record.Biology emerges from interactions between molecules, which are challenging to elucidate with current techniques. An orthogonal approach is to probe for 'response signatures' that identify specific circuit motifs. For example, bistability, hysteresis, or irreversibility are used to detect positive feedback loops. For adapting systems, such signatures are not known. Only two circuit motifs generate adaptation: negative feedback loops (NFLs) and incoherent feed-forward loops (IFFLs). On the basis of computational testing and mathematical proofs, we propose differential signatures: in response to oscillatory stimulation, NFLs but not IFFLs show refractory-period stabilization (robustness to changes in stimulus duration) or period skipping. Applying this approach to yeast, we identified the circuit dominating cell cycle timing. In Caenorhabditis elegans AWA neurons, which are crucial for chemotaxis, we uncovered a Ca2+ NFL leading to adaptation that would be difficult to find by other means. These response signatures allow direct access to the outlines of the wiring diagrams of adapting systems.The work was supported by US National Institutes of Health grant 5RO1-GM078153-07 (F.R.C.), NRSA Training Grant CA009673-36A1 (S.J.R.), a Merck Postdoctoral Fellowship at The Rockefeller University (S.J.R.), and the Simons Foundation (S.J.R.). J.L. was supported by a fellowship from the Boehringer Ingelheim Fonds. E.D.S. was partially supported by the US Office of Naval Research (ONR N00014-13-1-0074) and the US Air Force Office of Scientific Research (AFOSR FA9550-14-1-0060)

classification_params_r

Parameter file for script classify_submodes_cmsec_degsec_30fps.

mode_data_scripts

Scripts, parameter files, and data structures operating on segmented trajectorie

dataset_L

Velocity trajectories and segmentation annotation

Data from: Dynamic structure of locomotor behavior in walking fruit flies

The function of the brain is unlikely to be understood without an accurate description of its output, yet the nature of movement elements and their organization remains an open problem. Here, movement elements are identified from dynamics of walking in flies, using unbiased criteria. On one time scale, dynamics of walking are consistent over hundreds of milliseconds, allowing elementary features to be defined. Over longer periods, walking is well described by a stochastic process composed of these elementary features, and a generative model of this process reproduces individual behavior sequences accurately over seconds or longer. Within elementary features, velocities diverge, suggesting that dynamical stability of movement elements is a weak behavioral constraint. Rather, long-term instability can be limited by the finite memory between these elementary features. This structure suggests how complex dynamics may arise in biological systems from elements whose combination need not be tuned for dynamic stability

dataset_S

Velocity trajectories and segmentation annotation

classify_submodes_cmsec_degsec_30fps

MATLAB script to classify behavior elements (submodes, modes) in velocity time series of walking fruit flies. Requires parameter file classification_params_r.mat