Nonlinear, neuronal adaptation in insect vision models improves target discrimination within repetitively moving backgrounds.

Neurons which respond selectively to small moving targets, even against a cluttered background, have been identified in several insect species. To investigate what underlies these robust and highly selective responses, researchers have probed the neuronal circuitry in target-detecting, visual pathways. Observations in flies reveal nonlinear adaptation over time, composed of a fast onset and gradual decay. This adaptive processing is seen in both of the independent, parallel pathways encoding either luminance increments (ON channel) or decrements (OFF channel). The functional significance of this adaptive phenomenon has not been determined from physiological studies, though the asymmetrical time course suggests a role in suppressing responses to repetitive stimuli. We tested this possibility by comparing an implementation of fast adaptation against alternatives, using a model of insect 'elementary small target motion detectors'. We conducted target-detecting simulations on various natural backgrounds, that were shifted via several movement profiles (and target velocities). Using performance metrics, we confirmed that the fast adaptation observed in neuronal systems enhances target detection against a repetitively moving background. Such background movement would be encountered via natural ego-motion as the insect travels through the world. These findings show that this form of nonlinear, fast-adaptation (suitably implementable via cellular biophysics) plays a role analogous to background subtraction techniques in conventional computer vision.

Fluid structure interaction modelling of aortic valve stenosis: Effects of valve calcification on coronary artery flow and aortic root hemodynamics.

BACKGROUND AND OBJECTIVE: Coronary artery diseases and aortic valve stenosis are two of the main causes of mortality and morbidity worldwide. Stenosis of the aortic valve develops due to calcium deposition on the aortic valve leaflets during the cardiac cycle. Clinical investigations have demonstrated that aortic valve stenosis not only affects hemodynamic parameters inside the aortic root but also has a significant influence on the coronary artery hemodynamics and leads to the initiation of coronary artery disease. The aim of this study is to investigate the effect of calcification of the aortic valve on the variation of hemodynamic parameters in the aortic root and coronary arteries in order to find potential locations for initiation of the coronary stenoses. METHODS: Fluid structure interaction modelling methodology was used to simulate aortic valve hemodynamics in the presence of coronary artery flow. A 2-D model of the aortic valve leaflets was developed in ANSYS Fluent based on the available echocardiography images in literature. The k-ω SST turbulence model was utilised to model the turbulent flow downstream of the leaflets. RESULTS: The effects of calcification of the aortic valve on aortic root hemodynamics including transvalvular pressure gradient, valve orifice dimeter, vorticity magnitude in the sinuses and wall shear stress on the ventricularis and fibrosa layers of the leaflets were studied. Results revealed that the transvalvular pressure gradient increases from 792 Pa (∼ 6 mmHg) for a healthy aortic valve to 2885 Pa (∼ 22 mmHg) for a severely calcified one. Furthermore, the influence of the calcification of the aortic valve leaflets on the velocity profile and the wall shear stress in the coronary arteries was investigated and used for identification of potential locations of initiation of the coronary stenoses. Obtained results show that the maximum velocity inside the coronary arteries at early diastole decreases from 1 m/s for the healthy valve to 0.45 m/s for the severely calcified case. CONCLUSIONS: Calcification significantly decreases the wall shear stress of the coronary arteries. This reduction in the wall shear stress can be a main reason for initiation of the coronary atherosclerosis process and eventually results in coronary stenoses.

An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments.

OBJECTIVE: Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. APPROACH: We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets which move along long, continuous trajectories, as seen in our electrophysiological data. To test performance in real-world conditions, we implemented this model on a robotic platform that uses active pursuit strategies based on insect behaviour. MAIN RESULTS: Our robot performs robustly in closed-loop pursuit of targets, despite a range of challenging conditions used in our experiments; low contrast targets, heavily cluttered environments and the presence of distracters. We show that the facilitation stage boosts responses to targets moving along continuous trajectories, improving contrast sensitivity and detection of small moving targets against textured backgrounds. Moreover, the temporal properties of facilitation play a useful role in handling vibration of the robotic platform. We also show that the adoption of feed-forward models which predict the sensory consequences of self-movement can significantly improve target detection during saccadic movements. SIGNIFICANCE: Our results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of our bio-inspired algorithm in an artificial visual system.

Performance of an insect-inspired target tracker in natural conditions.

Robust and efficient target-tracking algorithms embedded on moving platforms, are a requirement for many computer vision and robotic applications. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. As inspiration, we look to biological lightweight solutions-lightweight and low-powered flying insects. For example, dragonflies pursue prey and mates within cluttered, natural environments, deftly selecting their target amidst swarms. In our laboratory, we study the physiology and morphology of dragonfly 'small target motion detector' neurons likely to underlie this pursuit behaviour. Here we describe our insect-inspired tracking model derived from these data and compare its efficacy and efficiency with state-of-the-art engineering models. For model inputs, we use both publicly available video sequences, as well as our own task-specific dataset (small targets embedded within natural scenes). In the context of the tracking problem, we describe differences in object statistics within the video sequences. For the general dataset, our model often locks on to small components of larger objects, tracking these moving features. When input imagery includes small moving targets, for which our highly nonlinear filtering is matched, the robustness outperforms state-of-the-art trackers. In all scenarios, our insect-inspired tracker runs at least twice the speed of the comparison algorithms.

Properties of neuronal facilitation that improve target tracking in natural pursuit simulations.

Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect 'small target motion detector' (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response 'facilitation' (a slow build-up of response to targets that move on long, continuous trajectories) and 'selective attention', a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to 'attend' to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.