The modelling cycle for collective animal behaviour

Collective animal behaviour is the study of how interactions between individuals produce group level patterns, and why these interactions have evolved. This study has proved itself uniquely interdisciplinary, involving physicists, mathematicians, engineers as well as biologists. Almost all experimental work in this area is related directly or indirectly to mathematical models, with regular movement back and forth between models, experimental data and statistical fitting. In this paper, we describe how the modelling cycle works in the study of collective animal behaviour. We classify studies as addressing questions at different levels or linking different levels, i.e. as local, local to global, global to local or global. We also describe three distinct approaches—theory-driven, data-driven and model selection—to these questions. We show, with reference to our own research on species across different taxa, how we move between these different levels of description and how these various approaches can be applied to link levels together

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1-1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals

Enhanced flight performance by genetic manipulation of wing shape in Drosophila

Insect wing shapes are remarkably diverse and the combination of shape and kinematics determines both aerial capabilities and power requirements. However, the contribution of any specific morphological feature to performance is not known. Using targeted RNA interference to modify wing shape far beyond the natural variation found within the population of a single species, we show a direct effect on flight performance that can be explained by physical modelling of the novel wing geometry. Our data show that altering the expression of a single gene can significantly enhance aerial agility and that the Drosophila wing shape is not, therefore, optimized for certain flight performance characteristics that are known to be important. Our technique points in a new direction for experiments on the evolution of performance specialities in animals

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modelling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research of insects' visual systems in the literature. These motion perception models or neural networks comprise the looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation sensitive neural systems of direction selective neurons (DSNs) in fruit flies, bees and locusts, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models

A multiplexed reverse transcriptase PCR assay for identification of viral respiratory pathogens at point-of-care

We have developed a nucleic acid-based assay that is rapid, sensitive, specific, and can be used for the simultaneous detection of 5 common human respiratory pathogens including influenza A, influenza B, parainfluenza type 1 and 3, respiratory syncytial virus, and adenovirus group B, C, and E. Typically, diagnosis on an un-extracted clinical sample can be provided in less than 3 hours, including sample collection, preparation, and processing, as well as data analysis. Such a multiplexed panel would enable rapid broad-spectrum pathogen testing on nasal swabs, and therefore allow implementation of infection control measures, and timely administration of antiviral therapies. This article presents a summary of the assay performance in terms of sensitivity and specificity. Limits of detection are provided for each targeted respiratory pathogen, and result comparisons are performed on clinical samples, our goal being to compare the sensitivity and specificity of the multiplexed assay to the combination of immunofluorescence and shell vial culture currently implemented at the UCDMC hospital. Overall, the use of the multiplexed RT-PCR assay reduced the rate of false negatives by 4% and reduced the rate of false positives by up to 10%. The assay correctly identified 99.3% of the clinical negatives, 97% of adenovirus, 95% of RSV, 92% of influenza B, and 77% of influenza A without any extraction performed on the clinical samples. The data also showed that extraction will be needed for parainfluenza virus, which was only identified correctly 24% of the time on un-extracted samples

Multi-camera real-time three-dimensional tracking of multiple flying animals

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time—with minimal latency—opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behaviour of freely flying animals. If combined with other techniques, such as ‘virtual reality’-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals

The Generation of Forces and Moments during Visual-Evoked Steering Maneuvers in Flying Drosophila

Sideslip force, longitudinal force, rolling moment, and pitching moment generated by tethered fruit flies, Drosophila melanogaster, were measured during optomotor reactions within an electronic flight simulator. Forces and torques were acquired by optically measuring the angular deflections of the beam to which the flies were tethered using a laser and a photodiode. Our results indicate that fruit flies actively generate both sideslip and roll in response to a lateral focus of expansion (FOE). The polarity of this behavior was such that the animal's aerodynamic response would carry it away from the expanding pattern, suggesting that it constitutes an avoidance reflex or centering response. Sideslip forces and rolling moments were sinusoidal functions of FOE position, whereas longitudinal force was proportional to the absolute value of the sine of FOE position. Pitching moments remained nearly constant irrespective of stimulus position or strength, with a direction indicating a tonic nose-down pitch under tethered conditions. These experiments expand our understanding of the degrees of freedom that a fruit fly can actually control in flight

Diagnostic evaluation of a multiplexed RT-PCR microsphere array assay for the detection of foot-and-mouth and look-alike disease viruses

A high-throughput multiplexed assay (Multiplex Version 1.0) was developed for the differential laboratory diagnosis of foot-and-mouth disease virus (FMDV) from viruses which cause clinically similar diseases of livestock. This assay simultaneously screens for five RNA and two DNA viruses using multiplexed reverse transcription PCR (mRT-PCR) amplification coupled with a microsphere hybridization array and flow-cytometric detection. Two of the seventeen primer-probe sets included in this multiplex assay were adopted from previously characterized real-time RT-PCR (rRT-PCR) assays for FMDV. The diagnostic accuracy of the mRT-PCR was evaluated using 287 field samples, including 248 (true positive n= 213, true negative n=34) from suspect cases of foot-and-mouth disease collected from 65 countries between 1965 and 2006 and 39 true negative samples collected from healthy animals. The mRT-PCR assay results were compared with two singleplex rRT-PCR assays, using virus isolation with antigen-ELISA as the reference method. The diagnostic sensitivity of the mRT-PCR assay for FMDV was 93.9% [95% C.I. 89.8-96.4%], compared to 98.1% [95% C.I. 95.3-99.3%] for the two singleplex rRTPCR assays used in combination. In addition, the assay could reliably differentiate between FMDV and other vesicular viruses such as swine vesicular disease virus and vesicular exanthema of swine virus. Interestingly, the mRT-PCR detected parapoxvirus (n=2) and bovine viral diarrhea virus (n=2) in clinical samples, demonstrating the screening potential of this mRT-PCR assay to identify viruses in FMDV-negative material not previously recognized using focused single-target rRT-PCR assays

Order in Spontaneous Behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents