A fast machine learning dataloader for epigenetic tracks from BigWig files.

SUMMARY: We created bigwig-loader, a data-loader for epigenetic profiles from BigWig files that decompresses and processes information for multiple intervals from multiple BigWig files in parallel. This is an access pattern needed to create training batches for typical machine learning models on epigenetics data. Using a new codec, the decompression can be done on a graphical processing unit (GPU) making it fast enough to create the training batches during training, mitigating the need for saving preprocessed training examples to disk. AVAILABILITY AND IMPLEMENTATION: The bigwig-loader installation instructions and source code can be accessed at https://github.com/pfizer-opensource/bigwig-loader.

Asymmetric processing of visual motion for simultaneous object and background responses.

Visual object fixation and figure-ground discrimination in Drosophila are robust behaviors requiring sophisticated computation by the visual system, yet the neural substrates remain unknown. Recent experiments in walking flies revealed object fixation behavior mediated by circuitry independent from the motion-sensitive T4-T5 cells required for wide-field motion responses. In tethered flight experiments under closed-loop conditions, we found similar results for one feedback gain, whereas intact T4-T5 cells were necessary for robust object fixation at a higher feedback gain and in figure-ground discrimination tasks. We implemented dynamical models (available at http://strawlab.org/asymmetric-motion/) based on neurons downstream of T4-T5 cells­one a simple phenomenological model and another, physiologically more realistic model­and found that both predict key features of stripe fixation and figure-ground discrimination and are consistent with a classical formulation. Fundamental to both models is motion asymmetry in the responses of model neurons, whereby front-to-back motion elicits stronger responses than back-to-front motion. When a bilateral pair of such model neurons, based on well-understood horizontal system cells, downstream of T4-T5, is coupled to turning behavior, asymmetry leads to object fixation and figure-ground discrimination in the presence of noise. Furthermore, the models also predict fixation in front of a moving background, a behavior previously suggested to require an additional pathway. Thus, the models predict several aspects of object responses on the basis of neurons that are also thought to serve a key role in background stabilization.

FlyMAD: rapid thermogenetic control of neuronal activity in freely walking Drosophila.

Rapidly and selectively modulating the activity of defined neurons in unrestrained animals is a powerful approach in investigating the circuit mechanisms that shape behavior. In Drosophila melanogaster, temperature-sensitive silencers and activators are widely used to control the activities of genetically defined neuronal cell types. A limitation of these thermogenetic approaches, however, has been their poor temporal resolution. Here we introduce FlyMAD (the fly mind-altering device), which allows thermogenetic silencing or activation within seconds or even fractions of a second. Using computer vision, FlyMAD targets an infrared laser to freely walking flies. As a proof of principle, we demonstrated the rapid silencing and activation of neurons involved in locomotion, vision and courtship. The spatial resolution of the focused beam enabled preferential targeting of neurons in the brain or ventral nerve cord. Moreover, the high temporal resolution of FlyMAD allowed us to discover distinct timing relationships for two neuronal cell types previously linked to courtship song.