A frequent assumption in behavioural science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here, we introduce a method for mapping an animal's actions, relying only upon the underlying structure of postural movement data to organize and classify behaviours. Applying this method to the ground-based behaviour of the fruit fly, Drosophila melanogaster, we find that flies perform stereotyped actions roughly 50% of the time, discovering over 100 distinguishable, stereotyped behavioural states. These include multiple modes of locomotion and grooming. We use the resulting measurements as the basis for identifying subtle sex-specific behavioural differences and revealing the low-dimensional nature of animal motions.

We investigate aspects of hovering insect flight by finding the optimal wing kinematics which minimize power consumption while still providing enough lift to maintain a time-averaged constant altitude over one flapping period. In particular, we study the flight of three insects whose masses vary by approximately three orders of magnitude: fruitfly (Drosophila melanogaster), bumblebee (Bombus terrestris), and hawkmoth (Manduca sexta). Here, we model an insect wing as a rigid body with three rotational degrees of freedom. The aerodynamic forces are modelled via a quasisteady model of a thin plate interacting with the surrounding fluid. The advantage of this model, as opposed to the more computationally costly method of direct numerical simulation via computational fluid dynamics, is that it allows us to perform optimization procedures and detailed sensitivity analyses which require many cost function evaluations. The optimal solutions are found via a hybrid optimization algorithm combining aspects of a genetic algorithm and a gradient-based optimizer. We find that the results of this optimization yield kinematics which are qualitatively and quantitatively similar to previously observed data. We also perform sensitivity analyses on parameters of the optimal kinematics to gain insight into the values of the observed optima. Additionally, we find that all of the optimal kinematics found here maintain the same leading edge throughout the stroke, as is the case for nearly all insect wing motions. We show that this type of stroke takes advantage of a passive wing rotation in which aerodynamic forces help to reverse the wing pitch, similar to the turning of a free-falling leaf.

Just as the Wright brothers implemented controls to achieve stable airplane flight, flying insects have evolved behavioral strategies that ensure recovery from flight disturbances. Pioneering studies performed on tethered and dissected insects demonstrate that the sensory, neurological, and musculoskeletal systems play important roles in flight control. Such studies, however, cannot produce an integrative model of insect flight stability because they do not incorporate the interaction of these systems with free-flight aerodynamics. We directly investigate control and stability through the application of torque impulses to freely flying fruit flies (Drosophila melanogaster) and measurement of their behavioral response. High-speed video and a new motion tracking method capture the aerial "stumble," and we discover that flies respond to gentle disturbances by accurately returning to their original orientation. These insects take advantage of a stabilizing aerodynamic influence and active torque generation to recover their heading to within 2°in <60 ms. To explain this recovery behavior, we form a feedback control model that includes the fly's ability to sense body rotations, process this information, and actuate the wing motions that generate corrective aerodynamic torque. Thus, like early man-made aircraft and modern fighter jets, the fruit fly employs an automatic stabilization scheme that reacts to short time-scale disturbances.flight control | insect flight | stability | perturbation | fruit fly L ocomotion through natural environments demands mechanisms that maintain stability in the face of unpredictable disturbances. Behavioral strategies play a particularly important role in controlling the flight of insects (1-7), because even gentle air currents can cause large disruptions to the intended flight path. Insects must also contend with the intrinsic instability of flapping flight (8, 9) and the large fluctuations in aerodynamic forces caused by slight variations in wing motions (10, 11). Corrective behavior often takes advantage of vision (1, 2). For fruit flies, however, reaction time to visual stimuli is at least 10 wingbeats (12), so these insects must employ faster sensory circuits to recover from short time-scale disturbances and instabilities. To probe this fast control strategy, we devised an experimental method that imposes impulsive mechanical disturbances (6, 13) to flying insects while allowing us to measure relevant aspects of flight behavior. We first glue tiny ferromagnetic pins to fruit flies and image their free flight using three orthogonally oriented highspeed video cameras (Methods and SI Text). When a fly enters the filming volume, an optical trigger detects the insect, initiates recording, and activates a pair of Helmholtz coils that produce a magnetic field. The field and pin are both oriented horizontally, so the resulting torque on the pin reorients the yaw, or heading angle, of the insect (Fig. 1). We then use a new motion tracking technique to extract the three-dimensional body and w...

The need for high-throughput, precise, and meaningful methods for measuring behavior has been amplified by our recent successes in measuring and manipulating neural circuitry. The largest challenges associated with moving in this direction, however, are not technical but are instead conceptual: what numbers should one put on the movements an animal is performing (or not performing)? In this review, I will describe how theoretical and data analytical ideas are interfacing with recently-developed computational and experimental methodologies to answer these questions across a variety of contexts, length scales, and time scales. I will attempt to highlight commonalities between approaches and areas where further advances are necessary to place behavior on the same quantitative footing as other scientific fields.

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.A nimals perform a vast array of behaviors as they go about their daily lives, often in what appear to be repeated and nonrandom patterns. These sequences of actions, some innate and some learned, have dramatic consequences with respect to survival and reproductive function: from feeding, grooming, and locomotion to mating, child rearing, and the establishment of social structures. Moreover, these patterns of movement can be viewed as the final output of the complicated interactions between an organism's genes, metabolism, and neural signaling. As a result, elucidating the principles that govern the generation of behavioral sequences provides a window into the biological mechanisms underlying an animal's movements, appetites, and interactions with its environment, potentially allowing for broader insights into how behaviors evolve.The prevailing theory for the temporal organization of behavior, rooted in work from neuroscience, psychology, and evolution, is that the pattern of actions performed by animals is hierarchical (1-3). In such a framework, actions are nested into modules on many scales, from simple motor primitives to complex behaviors to sequences of actions. In the case of a fly grooming itself, for example, small movements of the leg and wing muscles are organized into grooming modules for a particular location of the body. These modules are then orchestrated into patterns that exhibit their own complicated dynamics, and this whole pattern is only a small part of the entirety of the animal's activities (4, 5). Additionally, neural architectures related to behavior, such as the motor cortex, are anatomically hierarchical, supporting the idea that animals use a hierarchical representation of behavior in the brain (6-9). Hierarchical organization is also a hallmark of human design, from the layout of cities to the wiring of the internet, and its potential use in various biological contexts has been proposed as an organizing principle (2).Despite the theoretical attractiveness of behavioral hierarchy, measurements showing that a particular animal's b...