The Drosophila visual system: a super-efficient encoder

In order to survive and reproduce, every animal needs to run accurate and diverse visual processes efficiently. However, understanding how they see is limited by our lack of insight into how evolution optimises resource- and area-constrained neural machinery. It was shown recently in Drosophila that its photoreceptor cells, corresponding to individual “pixels” of the scene, react photomechanically to these light changes by generating an ultrafast counter-motion, a photoreceptor microsaccade. Each photoreceptor moves in a specific direction at its particular location inside the compound eye, transiently readjusting its own light input. These mirror-symmetrically opposing microsaccades cause small timing differences in the eye and the brain networks’ electrical signals, rapidly and accurately informing the fly of the 3D world structure. Remarkably, it has been shown that the Drosophila can resolve angles finer than 1°, five times less than what the optic laws would predict in a static fly eye. The results presented in this thesis demonstrate that hyperacute visual information is transmitted from the photoreceptors to the visual pathway and I report a deep learning approach for discovering how the Drosophila compound eyes' biological neural network (BNN) samples and represents hyperacute stimuli. Using in vivo two-photon calcium imaging on a transgenic fly, I recorded the responses of 17 flies’ L2 neurons, OFF neurons in the early visual pathway, while presenting fine resolution visual patterns. I showed that the Drosophila’s visual hyperacute information is transmitted from the photoreceptors to the medulla layer (2nd layer in the visual system). Additionally, I found that the L2 neurons show direction-specific acuity and proved that this is a consequence of the photoreceptors’ microsaccades. Next, I show that an artificial neural network (ANN), with precisely-positioned and photomechanically-moving photoreceptors, shaping and feeding visual information to a lifelike-wired neuropile, learns to reproduce natural response dynamics. Remarkably, this ANN predicts realistic stimulus-locked responses and synaptic connection eights at each eye location, mapping the eyes' experimentally verified hyperacute orientation sensitivity. By systematically altering sampling dynamics and connections, I further show that without the realistic orientation-tuned photoreceptor microsaccades and connectome, performance falters to suboptimal. My results demonstrate the importance of precise microsaccades and connectivity for efficient visual encoding and highlight the effect of morphodynamic information sampling on accurate perception

An Insect-Inspired Target Tracking Mechanism for Autonomous Vehicles

Target tracking is a complicated task from an engineering perspective, especially where targets are small and seen against complex natural environments. Due to the high demand for robust target tracking algorithms a great deal of research has focused on this area. However, most engineering solutions developed for this purpose are often unreliable in real world conditions or too computationally expensive to be used in real-time applications. While engineering methods try to solve the problem of target detection and tracking by using high resolution input images, fast processors, with typically computationally expensive methods, a quick glance at nature provides evidence that practical real world solutions for target tracking exist. Many animals track targets for predation, territorial or mating purposes and with millions of years of evolution behind them, it seems reasonable to assume that these solutions are highly efficient. For instance, despite their low resolution compound eyes and tiny brains, many flying insects have evolved superb abilities to track targets in visual clutter even in the presence of other distracting stimuli, such as swarms of prey and conspecifics. The accessibility of the dragonfly for stable electrophysiological recordings makes this insect an ideal and tractable model system for investigating the neuronal correlates for complex tasks such as target pursuit. Studies on dragonflies identified and characterized a set of neurons likely to mediate target detection and pursuit referred to as ‘small target motion detector’ (STMD) neurons. These neurons are selective for tiny targets, are velocity-tuned, contrast-sensitive and respond robustly to targets even against the motion of background. These neurons have shown several high-order properties which can contribute to the dragonfly’s ability to robustly pursue prey with over a 97% success rate. These include the recent electrophysiological 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. In this thesis, I adopted a bio-inspired approach to develop a solution for the problem of target tracking and pursuit. Directly inspired by recent physiological breakthroughs in understanding the insect brain, I developed a closed-loop target tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. First, I tested this model in virtual world simulations using MATLAB/Simulink. The results of these simulations show robust performance of this insect-inspired model, achieving high prey capture success even within complex background clutter, low contrast and high relative speed of pursued prey. Additionally, these results show that inclusion of facilitation not only substantially improves success for even short-duration pursuits, it also enhances the ability to ‘attend’ to one target in the presence of distracters. This inspect-inspired system has a relatively simple image processing strategy compared to state-of-the-art trackers developed recently for computer vision applications. Traditional machine vision approaches incorporate elaborations to handle challenges and non-idealities in the natural environments such as local flicker and illumination changes, and non-smooth and non-linear target trajectories. Therefore, the question arises as whether this insect inspired tracker can match their performance when given similar challenges? I investigated this question by testing both the efficacy and efficiency of this insect-inspired model in open-loop, using a widely-used set of videos recorded under natural conditions. I directly compared the performance of this model with several state-of-the-art engineering algorithms using the same hardware, software environment and stimuli. This insect-inspired model exhibits robust performance in tracking small moving targets even in very challenging natural scenarios, outperforming the best of the engineered approaches. Furthermore, it operates more efficiently compared to the other approaches, in some cases dramatically so. Computer vision literature traditionally test target tracking algorithms only in open-loop. However, one of the main purposes for developing these algorithms is implementation in real-time robotic applications. Therefore, it is still unclear how these algorithms might perform in closed-loop real-world applications where inclusion of sensors and actuators on a physical robot results in additional latency which can affect the stability of the feedback process. Additionally, studies show that animals interact with the target by changing eye or body movements, which then modulate the visual inputs underlying the detection and selection task (via closed-loop feedback). This active vision system may be a key to exploiting visual information by the simple insect brain for complex tasks such as target tracking. Therefore, I implemented this insect-inspired model along with insect active vision in a robotic platform. I tested this robotic implementation both in indoor and outdoor environments against different challenges which exist in real-world conditions such as vibration, illumination variation, and distracting stimuli. The experimental results show that the robotic implementation is capable of handling these challenges and robustly pursuing a target even in highly challenging scenarios.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Chasing control in male blowflies : behavioural performance and neuronal responses

Trischler C. Chasing control in male blowflies : behavioural performance and neuronal responses. Bielefeld (Germany): Bielefeld University; 2008

Scientific and technological progress. Advantages and disadvantages

The theme under consideration is divided into two parts: The History of Telephone and Innovations in Telephone Communications. In the past, people relied on letters to learn about what was going on in the lives of their friends or family members. The first electrical telegraph was constructed by Sir William Cooke. Another telegraph was developed and patented in the USA in 1837 by Samuel Morse. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2807