Central complex and mushroom bodies mediate novelty choice behavior in Drosophila

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Neural mechanisms involved in female mate choice in invertebrates

Mate choice is a critical decision with direct implications for fitness. Although it has been recognized for over 150 years, our understanding of its underlying mechanisms is still limited. Most studies on mate choice focus on the evolutionary causes of behavior, with less attention given to the physiological and molecular mechanisms involved. This is especially true for invertebrates, where research on mate choice has largely focused on male behavior. This review summarizes the current state of knowledge on the neural, molecular and neurohormonal mechanisms of female choice in invertebrates, including behaviors before, during, and after copulation. We identify areas of research that have not been extensively explored in invertebrates, suggesting potential directions for future investigation. We hope that this review will stimulate further research in this area.

Visual processing in the fly, from photoreceptors to behavior

Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.

Balancing Prediction and Surprise: A Role for Active Sleep at the Dawn of Consciousness?

The brain is a prediction machine. Yet the world is never entirely predictable, for any animal. Unexpected events are surprising, and this typically evokes prediction error signatures in mammalian brains. In humans such mismatched expectations are often associated with an emotional response as well, and emotional dysregulation can lead to cognitive disorders such as depression or schizophrenia. Emotional responses are understood to be important for memory consolidation, suggesting that positive or negative 'valence' cues more generally constitute an ancient mechanism designed to potently refine and generalize internal models of the world and thereby minimize prediction errors. On the other hand, abolishing error detection and surprise entirely (as could happen by generalization or habituation) is probably maladaptive, as this might undermine the very mechanism that brains use to become better prediction machines. This paradoxical view of brain function as an ongoing balance between prediction and surprise suggests a compelling approach to study and understand the evolution of consciousness in animals. In particular, this view may provide insight into the function and evolution of 'active' sleep. Here, we propose that active sleep - when animals are behaviorally asleep but their brain seems awake - is widespread beyond mammals and birds, and may have evolved as a mechanism for optimizing predictive processing in motile creatures confronted with constantly changing environments. To explore our hypothesis, we progress from humans to invertebrates, investigating how a potential role for rapid eye movement (REM) sleep in emotional regulation in humans could be re-examined as a conserved sleep function that co-evolved alongside selective attention to maintain an adaptive balance between prediction and surprise. This view of active sleep has some interesting implications for the evolution of subjective awareness and consciousness in animals.

Multi-regional circuits underlying visually guided decision-making in Drosophila

Visually guided decision-making requires integration of information from distributed brain areas, necessitating a brain-wide approach to examine its neural mechanisms. New tools in Drosophila melanogaster enable circuits spanning the brain to be charted with single cell-type resolution. Here, we highlight recent advances uncovering the computations and circuits that transform and integrate visual information across the brain to make behavioral choices. Visual information flows from the optic lobes to three primary central brain regions: a sensorimotor mapping area and two 'higher' centers for memory or spatial orientation. Rapid decision-making during predator evasion emerges from the spike timing dynamics in parallel sensorimotor cascades. Goal-directed decisions may occur through memory, navigation and valence processing in the central complex and mushroom bodies.

Automated real-time quantification of group locomotor activity in Drosophila melanogaster

Recent advances in neurogenetics have highlighted Drosophila melanogaster as an exciting model to study neural circuit dynamics and complex behavior. Automated tracking methods have facilitated the study of complex behaviors via high throughput behavioral screening. Here we describe a newly developed low-cost assay capable of real-time monitoring and quantifying Drosophila group activity. This platform offers reliable real-time quantification with open source software and a user-friendly interface for data acquisition and analysis. We demonstrate the utility of this platform by characterizing ethanol-induced locomotor activity in a dose-dependent manner as well as the effects of thermo and optogenetic manipulation of ellipsoid body neurons important for ethanol-induced locomotor activity. As expected, low doses of ethanol induced an initial startle and slow ramping of group activity, whereas high doses of ethanol induced sustained group activity followed by sedation. Advanced offline processing revealed discrete behavioral features characteristic of intoxication. Thermogenetic inactivation of ellipsoid body ring neurons reduced group activity whereas optogenetic activation increased activity. Together, these data establish the fly Group Activity Monitor (flyGrAM) platform as a robust means of obtaining an online read out of group activity in response to manipulations to the environment or neural activity, with an opportunity for more advanced post-processing offline.

Learning and Its Neural Correlates in a Virtual Environment for Honeybees

The search for neural correlates of operant and observational learning requires a combination of two (experimental) conditions that are very difficult to combine: stable recording from high order neurons and free movement of the animal in a rather natural environment. We developed a virtual environment (VE) that simulates a simplified 3D world for honeybees walking stationary on an air-supported spherical treadmill. We show that honeybees perceive the stimuli in the VE as meaningful by transferring learned information from free flight to the virtual world. In search for neural correlates of learning in the VE, mushroom body extrinsic neurons were recorded over days during learning. We found changes in the neural activity specific to the rewarded and unrewarded visual stimuli. Our results suggest an involvement of the mushroom body extrinsic neurons in operant learning in the honeybee (Apis mellifera).

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

Parallel encoding of recent visual experience and self-motion during navigation in Drosophila

Animal navigation requires multiple types of information for decisions on directional heading. We identified neural processing channels that encode multiple cues during navigational decision-making in Drosophila melanogaster. In a flight simulator, we found that flies made directional choices on the basis of the location of a recently presented landmark. This experience-guided navigation was impaired by silencing neurons in the bulb (BU), a region in the central brain. Two-photon calcium imaging during flight revealed that the dorsal part of the BU encodes the location of a recent landmark, whereas the ventral part of the BU tracks self-motion reflecting turns. Photolabeling-based circuit tracing indicated that these functional compartments of the BU constitute adjacent, yet distinct, anatomical pathways that both enter the navigation center. Thus, the fly's navigation system organizes multiple types of information in parallel channels, which may compactly transmit signals without interference for decision-making during flight.

A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents

Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control—enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.

Visual Attention in Flies-Dopamine in the Mushroom Bodies Mediates the After-Effect of Cueing

Visual environments may simultaneously comprise stimuli of different significance. Often such stimuli require incompatible responses. Selective visual attention allows an animal to respond exclusively to the stimuli at a certain location in the visual field. In the process of establishing its focus of attention the animal can be influenced by external cues. Here we characterize the behavioral properties and neural mechanism of cueing in the fly Drosophila melanogaster. A cue can be attractive, repulsive or ineffective depending upon (e.g.) its visual properties and location in the visual field. Dopamine signaling in the brain is required to maintain the effect of cueing once the cue has disappeared. Raising or lowering dopamine at the synapse abolishes this after-effect. Specifically, dopamine is necessary and sufficient in the αβ-lobes of the mushroom bodies. Evidence is provided for an involvement of the αβ_posterior Kenyon cells.

Outcome learning, outcome expectations, and intentionality in Drosophila

An animal generates behavioral actions because of the effects of these actions in the future. Occasionally, the animal may generate an action in response to a certain event or situation. If the outcome of the action is adaptive, the animal may keep this stimulus–response link in its behavioral repertoire, in case the event or situation occurs again. If a responsive action is innate but the outcome happens to be less adaptive than it had been before, the link may be loosened. This adjustment of outcome expectations involves a particular kind of learning, which will be called “outcome learning.” The present article discusses several examples of outcome learning in Drosophila. Learning and memory are intensely studied in flies, but the focus is on classical conditioning. Outcome learning, a particular form of operant learning, is of special significance, because it modulates outcome expectations that are operational components of action selection and intentionality.

Chemosensory apparatus of Drosophila larvae

Many insects, including Drosophila melanogaster, have a rich repertoire of olfactory behavior. Combination of robust behavioral assays, physiological and molecular tools render D. melanogaster as highly suitable system for olfactory studies. The small number of neurons in the olfactory system of fruit flies, especially the number of sensory neurons in the larval stage, makes the exploration of sensory coding at all stages of its nervous system a potentially tractable goal, which is not possible in the foreseeable future in any mammalian preparation. Advances in physiological recordings, olfactory signaling and detailed analysis of behavior, can place larvae in a position to ask previously unanswerable questions.