2,963 research outputs found

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

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    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

    Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

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    Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

    Real-time processing of physiological signals for feedback control

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    Extensive studies about neural mechanisms involved in insect flight control have been carried out. Adaptive control of locomotion requires integration of salient sensory cues with ongoing motor activity. During flight, inputs received by an organism through sensory organs are processed by the central nervous system (CNS) and the integrated output thus obtained plays a significant role in controlling the wing phase shifts and flight muscle depressor asymmetries associated with adaptive flight maneuvers. The resulting maneuvers, in turn, bring a change in the insect’s sensory environment, thereby closing the feedback loop. Research on insect flight has been carried out using immobile preparations (tethered) and mobile preparations (free flight – untethered). There are pros and cons associated with the tethered and the untethered approach. The tethered approach, however, provides an easier way to study the CNS and its role in motor control of flight. Insects such as locusts and moths exhibit pertinent wing phase shifts and asymmetries in depressor muscles. For locusts constant wing phase shifts and m97 (forewing first basalar depressor muscle) depressor asymmetries have been observed during adaptive flight maneuvers making this a useful system for creation of behaviorally appropriate visual feedback. A method that utilizes asymmetrical timing of bilateral depressor muscles, the forewing first basalars (m97), of the locust to close a visual feedback loop in a computer-generated flight simulator is presented here. The method converts the time difference between left and right m97s to analog voltage values. Analog voltage values can be acquired using an open-loop experimental protocol (visual motion controlled by the experimenter), or can be used to control closed-loop experiments (muscle activity controls the visual stimuli) experiments. We recorded electromyographic (EMG) activity from right and left m97 muscles. On testing this circuit with real animals, we were able to detect the spike time difference and convert it to voltage values. These voltage values were utilized to control the presentation of a stimulus in a closed-loop environment. The feedback circuit presented here may be used in conjunction with the flight simulator(s) to understand the neural mechanisms involved in control of insect flight and provide further understanding of general mechanisms of neural control of behaviour

    Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

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    This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

    Engineering derivatives from biological systems for advanced aerospace applications

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    The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

    SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal

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    How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

    Small business innovation research. Abstracts of completed 1987 phase 1 projects

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    Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

    Event-based neuromorphic stereo vision

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    Biomimetic vision-based collision avoidance system for MAVs.

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    This thesis proposes a secondary collision avoidance algorithm for micro aerial vehicles based on luminance-difference processing exhibited by the Lobula Giant Movement Detector (LGMD), a wide-field visual neuron located in the lobula layer of a locust’s nervous system. In particular, we address the design, modulation, hardware implementation, and testing of a computationally simple yet robust collision avoidance algorithm based on the novel concept of quadfurcated luminance-difference processing (QLDP). Micro and Nano class of unmanned robots are the primary target applications of this algorithm, however, it could also be implemented on advanced robots as a fail-safe redundant system. The algorithm proposed in this thesis addresses some of the major detection challenges such as, obstacle proximity, collision threat potentiality, and contrast correction within the robot’s field of view, to establish and generate a precise yet simple collision-free motor control command in real-time. Additionally, it has proven effective in detecting edges independent of background or obstacle colour, size, and contour. To achieve this, the proposed QLDP essentially executes a series of image enhancement and edge detection algorithms to estimate collision threat-level (spike) which further determines if the robot’s field of view must be dissected into four quarters where each quadrant’s response is analysed and interpreted against the others to determine the most secure path. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios in order to validate the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision (real-world), moving at 1.2 msˉ¹ with a successful avoidance rate of 90% processing at an extreme frequency of 120 Hz, that is much superior compared to the results reported in the contemporary related literature to the best of our knowledge.MSc by Researc
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