Many animals keep track of their angular heading over time while navigating through their environment. However, a neural-circuit architecture for computing heading has not been experimentally defined in any species. Here we describe a set of clockwise- and anticlockwise-shifting neurons in the Drosophila central complex whose wiring and physiology provide a means to rotate an angular heading estimate based on the fly's angular velocity. We show that each class of shifting neurons exists in two subtypes, with spatiotemporal activity profiles that suggest different roles for each subtype at the start and end of tethered-walking turns. Shifting neurons are required for the heading system to properly track the fly's heading in the dark, and stimulation of these neurons induces predictable shifts in the heading signal. The central features of this biological circuit are analogous to those of computational models proposed for head-direction cells in rodents and may shed light on how neural systems, in general, perform integration.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

Our goal is to describe a specific case of a general process gaining traction amongst biologists: testing biological hypotheses with biomimetic structures that operate in bioinspired robots. As an example, we present MARMT (mobile autonomous robot for mechanical testing), a surface-swimmer that undulates a submerged biomimetic tail to power cruising and accelerations. Our goal was to test the hypothesis that stiffness of the body controls swimming behavior and that both stiffness and behavior can be altered by changes in the morphology of the vertebral column. To test this hypothesis, we built biomimetic vertebral columns (BVC) outfitted with variable numbers of rigid ring centra; as the number of centra increased the axial length of the intervertebral joints decreased. Each kind of BVC was tested in dynamic bending to measure the structure's apparent stiffness as the storage and loss moduli. In addition, each kind of BVC was used as the axial skeleton in a tail that propelled MARMT. We varied MARMT's tail-beat frequency, lateral amplitude of the tail, and swimming behavior. MARMT's locomotor performance was measured using an on-board accelerometer and external video. As the number of vertebrae in the BVC of fixed length increased, so, too, did the BVC's storage modulus, the BVC's loss modulus, MARMT's mean speed during cruising, and MARMT's peak acceleration during a startle response. These results support the hypothesis that stiffness of the body controls swimming behavior and that both stiffness and behavior can be altered by changes in the morphology of the vertebral column.