Mammalian hearing operates on three basic steps: 1) sound capturing, 2) impedance conversion, and 3) frequency analysis. While these canonical steps are vital for acoustic communication and survival in mammals, they are not unique to them. An equivalent mechanism has been described for katydids (Insecta), and it is unique to this group among invertebrates. The katydid inner ear resembles an uncoiled cochlea, and has a length less than 1 mm. Their inner ears contain the crista acustica, which holds tonotopically arranged sensory cells for frequency mapping via travelling waves. The crista acustica is located on a curved triangular surface formed by the dorsal wall of the ear canal. While empirical recordings show tonotopic vibrations in the katydid inner ear for frequency analysis, the biophysical mechanism leading to tonotopy remains elusive due to the small size and complexity of the hearing organ. In this study, robust numerical simulations are developed for an in silico investigation of this process. Simulations are based on the precise katydid inner ear geometry obtained by synchrotron-based micro-computed tomography, and empirically determined inner ear fluid properties for an accurate representation of the underlying mechanism. We demonstrate that the triangular structure below the hearing organ drives the tonotopy and travelling waves in the inner ear, and thus has an equivalent role to the mammalian basilar membrane. This reveals a stronger analogy between the inner ear basic mechanical networks of two organisms with ancient evolutionary differences and independent phylogenetic histories.

Birds of prey rely on vision to execute flight manoeuvres that are key to their survival, such as intercepting fast-moving targets or navigating through clutter. A better understanding of the role played by vision during these manoeuvres is not only relevant within the field of animal behaviour, but could also have applications for autonomous drones. In this paper, we present a novel method that uses computer vision tools to analyse the role of active vision in bird flight, and demonstrate its use to answer behavioural questions. Combining motion capture data from Harris' hawks with a hybrid 3D model of the environment, we render RGB images, semantic maps, depth information and optic flow outputs that characterise the visual experience of the bird in flight. In contrast with previous approaches, our method allows us to consider different camera models and alternative gaze strategies for the purposes of hypothesis testing, allows us to consider visual input over the complete visual field of the bird, and is not limited by the technical specifications and performance of a head-mounted camera light enough to attach to a bird's head in flight. We present pilot data from three sample flights: a pursuit flight, in which a hawk intercepts a moving target, and two obstacle avoidance flights. With this approach, we provide a reproducible method that facilitates the collection of large volumes of data across many individuals, opening up new avenues for data-driven models of animal behaviour. Supplementary Information: The online version contains supplementary material available at 10.1007/s11263-022-01733-2.

Flying at speed through complex environments is a challenging task that has been performed successfully by insects since the Carboniferous, but which remains a challenge for robotic and autonomous systems. Insects navigate the world using optical flow sensed by their compound eyes, which they process using a deep neural network weighing just a few milligrams. Deploying an insect-inspired network architecture in computer vision could therefore enable more efficient and effective ways of estimating structure and self-motion using optical flow. Training a bio-informed deep network to implement these tasks requires biologically relevant training, test, and validation data. To this end, we introduce FlyView, a novel bio-informed truth dataset for visual navigation. This simulated dataset is rendered using open source 3D scenes in which the observer's position is known at every frame, and is accompanied by truth data on depth, self-motion, and motion flow. This dataset comprising 42,475 frames has several key features that are missing from existing optical flow datasets, including: (i) panoramic cameras with a monocular and binocular field of view matched to that of a fly's compound eyes; (ii) dynamically meaningful self-motion modelled on motion primitives, or the 3D trajectories of drones and flies; and (iii) complex natural and indoor environments including reflective surfaces.

The peregrine falcon Falco peregrinus is renowned for attacking its prey from high altitude in a fast controlled dive called a stoop. Many other raptors employ a similar mode of attack, but the functional benefits of stooping remain obscure. Here we investigate whether, when, and why stooping promotes catch success, using a three-dimensional, agent-based modeling approach to simulate attacks of falcons on aerial prey. We simulate avian flapping and gliding flight using an analytical quasi-steady model of the aerodynamic forces and moments, parametrized by empirical measurements of flight morphology. The model-birds' flight control inputs are commanded by their guidance system, comprising a phenomenological model of its vision, guidance, and control. To intercept its prey, model-falcons use the same guidance law as missiles (pure proportional navigation); this assumption is corroborated by empirical data on peregrine falcons hunting lures. We parametrically vary the falcon's starting position relative to its prey, together with the feedback gain of its guidance loop, under differing assumptions regarding its errors and delay in vision and control, and for three different patterns of prey motion. We find that, when the prey maneuvers erratically, high-altitude stoops increase catch success compared to low-altitude attacks, but only if the falcon's guidance law is appropriately tuned, and only given a high degree of precision in vision and control. Remarkably, the optimal tuning of the guidance law in our simulations coincides closely with what has been observed empirically in peregrines. High-altitude stoops are shown to be beneficial because their high airspeed enables production of higher aerodynamic forces for maneuvering, and facilitates higher roll agility as the wings are tucked, each of which is essential to catching maneuvering prey at realistic response delays.