Inherent to this sensory-motor transformation is a decision regarding which behavior from the animal's combinatorially large repertoire of motor acts is most "appropriate." How are these decision processes enacted at the level of neuronal circuits to produce ecologically relevant behavior?
We study the fruit fly, Drosophila melanogaster, because the fly displays flexible, interesting behaviors and its nervous system is small enough to analyze at the level of individual neurons.
We record video of flies at 6,000 frames per second to characterize a naturalistic fly behavior—escape, and we are automating this process to achieve rapid throughput. We also use new genetic and electrophysiological techniques to identify cells involved in escape circuits and assess their functional roles.
We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior.
Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors.
Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway.
The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway.
Response to a looming threat thus appears to involve activation of numerous different behavioral pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. This would provide a model system with which to investigate how the fly makes behavioral choices at the level of neuronal circuits.
To pursue the neural substrate of fly decision behavior, we are developing a new kind of high-throughput genetic screen.
Historically, genetic screens involved perturbing specific areas of the nervous system, either by mutagenesis or by selective silencing of neurons using the UAS-GAL4 system, and then screening for altered behavioral phenotypes. This approach is limited by (1) selectivity of available GAL4 driver lines and (2) ability to detect subtle behavioral changes.
Gerry Rubin's lab at Janelia, with which we collaborate, has overcome the first limitation by creating a library of several thousand limited-expression GAL4 lines that target small groups of cells throughout the entire brain. These new GAL4 constructs enable much more specific perturbations of the fly's central nervous system.
Our lab is surmounting the second limitation with a new behavioral tracking system to automatically capture fly escape sequences and quantify individual behaviors. We will use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies.
Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors.
Complementary to our automated screen, we use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons.
Flies will still perform escape behaviors when their head and thorax are immobilized for physiological recording. In this case, we give the fly a small Styrofoam ball to hold. In response to a looming stimulus, the fly will kick the ball away and initiate flight. Thus we can record from candidate neurons while simultaneously observing the fly's behavior. This allows us to link a neuron's responses directly to a behavioral decision.
Our work is focused on finding the neural circuitry that determines how a fly chooses which escape behaviors to perform and when to perform them. We hope to show that the fly escape response, a behavior that was once the paragon of stereotyped reflexes, may instead be an ideal model system in which to study the neural bases of decision processes.
We discovered a bimodal behavior in the genetically tractable organism Drosophila melanogaster that allowed us to directly probe the neural mechanisms of an action selection process. When confronted by a predator-mimicking looming stimulus, a fly responds with either a long-duration escape behavior sequence that initiates stable flight or a distinct, short-duration sequence that sacrifices flight stability for speed. Intracellular recording of the descending giant fiber (GF) interneuron during head-fixed escape revealed that GF spike timing relative to parallel circuits for escape actions determined which of the two behavioral responses was elicited. The process was well described by a simple model in which the GF circuit has a higher activation threshold than the parallel circuits, but can override ongoing behavior to force a short takeoff. Our findings suggest a neural mechanism for action selection in which relative activation timing of parallel circuits creates the appropriate motor output.
Escape behaviors are, by necessity, fast and robust, making them excellent systems with which to study the neural basis of behavior. This is especially true in insects, which have comparatively tractable nervous systems and members who are amenable to manipulation with genetic tools. Recent technical developments in high-speed video reveal that, despite their short duration, insect escape behaviors are more complex than previously appreciated. For example, before initiating an escape jump, a fly performs sophisticated posture and stimulus-dependent preparatory leg movements that enable it to jump away from a looming threat. This newfound flexibility raises the question of how the nervous system generates a behavior that is both rapid and flexible. Recordings from the cricket nervous system suggest that synchrony between the activity of specific interneuron pairs may provide a rapid cue for the cricket to detect the direction of an approaching predator and thus which direction it should run. Technical advances make possible wireless recording from neurons while locusts escape from a looming threat, enabling, for the first time, a direct correlation between the activity of multiple neurons and the time-course of an insect escape behavior.
Prior Publications (3)
We have approached the problem of reverse-engineering the flight control mechanism of the fruit fly by studying the dynamics of the responses to a visual stimulus during takeoff. Building upon a prior framework [G. Card and M. Dickinson, J. Exp. Biol., vol. 211, pp. 341-353, 2008], we seek to understand the strategies employed by the animal to stabilize attitude and orientation during these evasive, highly dynamical maneuvers. As a first step, we consider the dynamics from a gray-box perspective: examining lumped forces produced by the insect's legs and wings. The reconstruction of the flight initiation dynamics, based on the unconstrained motion formulation for a rigid body, allows us to assess the fly's responses to a variety of initial conditions induced by its jump. Such assessment permits refinement by using a visual tracking algorithm to extract the kinematic envelope of the wings [E. I. Fontaine, F. Zabala, M. Dickinson, and J. Burdick, "Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking," submitted for publication] in order to estimate lift and drag forces [F. Zabala, M. Dickinson, and R. Murray, "Control and stability of insect flight during highly dynamical maneuvers," submitted for publication], and recording actual leg-joint kinematics and using them to estimate jump forces [F. Zabala, "A bio-inspired model for directionality control of flight initiation," to be published.]. In this paper, we present the details of our approach in a comprehensive manner, including the salient results.
A key feature of reactive behaviors is the ability to spatially localize a salient stimulus and act accordingly. Such sensory-motor transformations must be particularly fast and well tuned in escape behaviors, in which both the speed and accuracy of the evasive response determine whether an animal successfully avoids predation . We studied the escape behavior of the fruit fly, Drosophila, and found that flies can use visual information to plan a jump directly away from a looming threat. This is surprising, given the architecture of the pathway thought to mediate escape [2, 3]. Using high-speed videography, we found that approximately 200 ms before takeoff, flies begin a series of postural adjustments that determine the direction of their escape. These movements position their center of mass so that leg extension will push them away from the expanding visual stimulus. These preflight movements are not the result of a simple feed-forward motor program because their magnitude and direction depend on the flies' initial postural state. Furthermore, flies plan a takeoff direction even in instances when they choose not to jump. This sophisticated motor program is evidence for a form of rapid, visually mediated motor planning in a genetically accessible model organism.
The fruit fly Drosophila melanogaster performs at least two distinct types of flight initiation. One kind is a stereotyped escape response to a visual stimulus that is mediated by the hard-wired giant fiber neural pathway, and the other is a more variable ;voluntary' response that can be performed without giant fiber activation. Because the simpler escape take-offs are apparently successful, it is unclear why the fly has multiple pathways to coordinate flight initiation. In this study we use high-speed videography to observe flight initiation in unrestrained wild-type flies and assess the flight performance of each of the two types of take-off. Three-dimensional kinematic analysis of take-off sequences indicates that wing use during the jumping phase of flight initiation is essential for stabilizing flight. During voluntary take-offs, early wing elevation leads to a slower and more stable take-off. In contrast, during visually elicited escapes, the wings are pulled down close to the body during take-off, resulting in tumbling flights in which the fly translates faster but also rotates rapidly about all three of its body axes. Additionally, we find evidence that the power delivered by the legs is substantially greater during visually elicited escapes than during voluntary take-offs. Thus, we find that the two types of Drosophila flight initiation result in different flight performances once the fly is airborne, and that these performances are distinguished by a trade-off between speed and stability.