78 results found
Huang JV, Krapp HG, 2023, Fly H1-Cell Distance Estimation in a Monocular Virtual Reality Environment, Pages: 325-337, ISBN: 9783031388569
The ability of animals and robots to move through a given environment without colliding with any obstacles requires a robust distance estimation mechanism. Previous electrophysiological studies and work using a biohybrid fly-robot-interface (FRI) suggest that fly directional-selective interneurons may be involved in the neural control of collision-avoidance behaviour. We have set up a virtual reality (FlyVR) environment and studied the blowfly’s H1-cell, an interneuron analyzing visual wide-field motion, to access its distance-dependent responses that was discovered using the FRI. The results gathered under open-loop FlyVR conditions are in qualitative agreement with open- and closed-loop data obtained on the FRI. They suggest that the capability of flies to estimate distance may depend on the animal’s specific movement trajectory in combination with the receptive field properties of the H1-cell. Our findings in the fly motion vision pathway may inform the design of energy-efficient collision avoidance strategies for autonomous robotic systems.
Longden KD, Schuetzenberger A, Hardcastle BJ, et al., 2022, Impact of walking speed and motion adaptation on optokinetic nystagmus-like head movements in the blowfly <i>Calliphora</i>, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
Supple JA, Varennes-Phillit L, Gajjar-Reid D, et al., 2022, Generating spatiotemporal patterns of linearly polarised light a high frame rates for insect vision research, JOURNAL OF EXPERIMENTAL BIOLOGY, Vol: 225, ISSN: 0022-0949
Varennes L, Krapp HG, Viollet S, 2020, Two pursuit strategies for a single sensorimotor control task in blowfly, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
Huang JV, Wei Y, Krapp HG, 2019, A biohybrid fly-robot interface system that performs active collision avoidance., Bioinspir Biomim, Vol: 14, Pages: 065001-065001
We have designed a bio-hybrid fly-robot interface (FRI) to study sensorimotor control in insects. The FRI consists of a miniaturized recording platform mounted on a two-wheeled robot and is controlled by the neuronal spiking activity of an identified visual interneuron, the blowfly H1-cell. For a given turning radius of the robot, we found a proportional relationship between the spike rate of the H1-cell and the relative distance of the FRI from the patterned wall of an experimental arena. Under closed-loop conditions during oscillatory forward movements biased towards the wall, collision avoidance manoeuvres were triggered whenever the H1-cell spike rate exceeded a certain threshold value. We also investigated the FRI behaviour in corners of the arena. The ultimate goal is to enable autonomous and energy-efficient manoeuvrings of the FRI within arbitrary visual environments.
Varennes LP, Krapp HG, Viollet S, 2019, A novel setup for 3D chasing behavior analysis in free flying flies, JOURNAL OF NEUROSCIENCE METHODS, Vol: 321, Pages: 28-38, ISSN: 0165-0270
Yue X, Huang JV, Krapp HG, et al., 2018, An implantable mixed-signal CMOS die for battery-powered in vivo blowfly neural recordings, Microelectronics Journal, Vol: 74, Pages: 34-42, ISSN: 0026-2692
A mixed-signal die containing two differential input amplifiers, a multiplexer and a 50 KSPS, 10-bit SAR ADC, has been designed and fabricated in a 0.35 μm CMOS process for in vivo neural recording from freely moving blowflies where power supplied voltage drops quickly due to the space/weight limited insufficient capacity of the battery. The designed neural amplifier has a 66 + dB gain, 0.13 Hz-5.3 KHz bandwidth and 0.39% THD. A 20% power supply voltage drop causes only a 3% change in amplifier gain and 0.9-bit resolution degrading for SAR ADC while the on-chip data modulation reduces the chip size, rendering the designed chip suitable for battery-powered applications. The fabricated die occupies 1.1 mm2 while consuming 238 μW, being suitable for implantable neural recordings from insects as small as a blowfly for electrophysiological studies of their sensorimotor control mechanisms. The functionality of the die has been validated by recording the signals from identified interneurons in the blowfly visual system.
Huang JV, Wei Y, Krapp HG, 2018, Active Collision Free Closed-Loop Control of a Biohybrid Fly-Robot Interface, 7th International Conference on Biomimetic and Biohybrid Systems, Living Machines (LM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 213-222, ISSN: 0302-9743
Longden KD, Wicklein M, Hardcastle BJ, et al., 2017, Spike Burst Coding of Translatory Optic Flow and Depth from Motion in the Fly Visual System, CURRENT BIOLOGY, Vol: 27, Pages: 3225-+, ISSN: 0960-9822
Huang J, Krapp H, 2017, Neuronal Distance Estimation by a Fly-Robot Interface, Jiaqi Huang
Huang JV, Krapp HG, 2017, Neuronal Distance Estimation by a Fly-Robot Interface, Editors: Mangan, Cutkosky, Mura, Verschure, Prescott, Lepora, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, ISBN: 978-3-319-63536-1
Swart P, Wicklein M, Sykes D, et al., 2016, A quantitative comparison of micro-CT preparations in Dipteran flies, SCIENTIFIC REPORTS, Vol: 6, ISSN: 2045-2322
Hardcastle BJ, Krapp HG, 2016, Evolution of Biological Image Stabilization, CURRENT BIOLOGY, Vol: 26, Pages: R1010-R1021, ISSN: 0960-9822
Hardcastle B, Schwyn DA, Bierig K, et al., 2016, Integration of Multiple Visual Inputs in the Blowfly, Perception, Vol: 45, Pages: 700-701, ISSN: 1468-4233
Huang JV, Wang Y, Krapp HG, 2016, Wall Following in a Semi-closed-loop Fly-Robotic Interface, 5th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 85-96, ISSN: 0302-9743
Krapp HG, 2015, How a fly escapes the reflex trap, NATURE NEUROSCIENCE, Vol: 18, Pages: 1192-1194, ISSN: 1097-6256
Mokso R, Schwyn DA, Walker SM, et al., 2015, Four-dimensional <i>in vivo</i> X-ray microscopy with projection-guided gating, SCIENTIFIC REPORTS, Vol: 5, ISSN: 2045-2322
Huang JV, Krapp HG, 2015, Closed-Loop Control in an Autonomous Bio-hybrid Robot System Based on Binocular Neuronal Input, 4th International Conference on Biomimetic and Biohybrid Systems (Living Machines), Publisher: SPRINGER-VERLAG BERLIN, Pages: 164-174, ISSN: 0302-9743
Gremillion G, Humbert JS, Krapp HG, 2014, Bio-inspired modeling and implementation of the ocelli visual system of flying insects, BIOLOGICAL CYBERNETICS, Vol: 108, Pages: 735-746, ISSN: 0340-1200
Krapp HG, 2014, Sensory Integration: Neuronal Filters for Polarized Light Patterns, CURRENT BIOLOGY, Vol: 24, Pages: R840-R841, ISSN: 0960-9822
Longden KD, Muzzu T, Cook DJ, et al., 2014, Nutritional State Modulates the Neural Processing of Visual Motion, CURRENT BIOLOGY, Vol: 24, Pages: 890-895, ISSN: 0960-9822
Walker SM, Schwyn DA, Mokso R, et al., 2014, In vivo time- resolved microtomography reveals the mechanics of the blowfly flight motor, PLoS Biology, Vol: 12, Pages: 1-12, ISSN: 1544-9173
Dipteran flies are amongst the smallest and most agile of flying animals. Their wings are driven indirectly by large power muscles, which cause cyclical deformations of the thorax that are amplified through the intricate wing hinge. Asymmetric flight manoeuvres are controlled by 13 pairs of steering muscles acting directly on the wing articulations. Collectively the steering muscles account for <3% of total flight muscle mass, raising the question of how they can modulate the vastly greater output of the power muscles during manoeuvres. Here we present the results of a synchrotron-based study performing micrometre-resolution, time-resolved microtomography on the 145 Hz wingbeat of blowflies. These data represent the first four-dimensional visualizations of an organism's internal movements on sub-millisecond and micrometre scales. This technique allows us to visualize and measure the three-dimensional movements of five of the largest steering muscles, and to place these in the context of the deforming thoracic mechanism that the muscles actuate. Our visualizations show that the steering muscles operate through a diverse range of nonlinear mechanisms, revealing several unexpected features that could not have been identified using any other technique. The tendons of some steering muscles buckle on every wingbeat to accommodate high amplitude movements of the wing hinge. Other steering muscles absorb kinetic energy from an oscillating control linkage, which rotates at low wingbeat amplitude but translates at high wingbeat amplitude. Kinetic energy is distributed differently in these two modes of oscillation, which may play a role in asymmetric power management during flight control. Structural flexibility is known to be important to the aerodynamic efficiency of insect wings, and to the function of their indirect power muscles. We show that it is integral also to the operation of the steering muscles, and so to the functional flexibility of the insect flight motor.
Huang JV, Krapp HG, 2014, A predictive model for closed-loop collision avoidance in a fly-robotic interface, Pages: 130-141, ISSN: 0302-9743
Here we propose a control design for a calibrated fly-brain-robotic interface. The interface uses the spiking activity of an identified visual interneuron in the fly brain, the H1-cell, to control the trajectory of a 2-wheeled robot such that it avoids collision with objects in the environment. Control signals will be based on a comparison between predicted responses - derived from the known robot dynamics and the H1-cell responses to visual motion in an isotropic distance distribution - and the actually observed spike rate measured during movements of the robot. The suggested design combines two fundamental concepts in biological sensorimotor control to extract task-specific information: active sensing and the use of efference copies (forward models). In future studies we will use the fly-robot interface to investigate multisensory integration. © 2014 Springer International Publishing.
Sabatier Q, Krapp HG, Tanaka RJ, 2014, Dynamic optimisation for fly gaze stabilisation based on noisy and delayed sensor information, 13th European Control Conference (ECC), Publisher: IEEE, Pages: 1783-1788
Mokso R, Marone F, Irvine S, et al., 2013, Advantages of phase retrieval for fast x-ray tomographic microscopy, JOURNAL OF PHYSICS D-APPLIED PHYSICS, Vol: 46, ISSN: 0022-3727
Ejaz N, Krapp HG, Tanaka RJ, 2013, Closed-loop response properties of a visual interneuron involved in fly optomotor control, FRONTIERS IN NEURAL CIRCUITS, Vol: 7
Huang JV, Krapp HG, 2013, Miniaturized electrophysiology platform for fly-robot interface to study multisensory integration, Pages: 119-130, ISSN: 0302-9743
To study multisensory integration, we have designed a fly-robot interface that will allow a blowfly to control the movements of a mobile robotic platform. Here we present successfully miniaturized recording equipment which meets the required specifications in terms of size, gain, bandwidth and stability. Open-loop experiments show that despite its small size, stable recordings from the identified motion-sensitive H1-cell are feasible when: (i) the fly is kept stationary and stimulated by external motion of a visual pattern; (ii) the fly and platform are rotating in a stationary visual environment. Comparing the two data sets suggests that rotating the fly or the pattern, although resulting in the same visual motion stimulus, induce slightly different H1-cell response. This may reflect the involvement of mechanosensory systems during rotations of the fly. The next step will be to use H1-cell responses for the control of unrestrained movements of the robot under closed-loop conditions. © 2013 Springer-Verlag Berlin Heidelberg.
Yue X, Krapp HG, Drakakis EM, 2013, An output code offset-free comparator for SAR ADCs based on non-linear preamplifier and CMOS inverters, Microelectronics Journal, Vol: 44, Pages: 414-420, ISSN: 0026-2692
Ejaz N, Tanaka RJ, Krapp HG, 2012, Static versus adaptive gain control strategy for visuo-motor stabilization, Pages: 107-119, ISSN: 0302-9743
Biological principles of closed-loop motor control have gained much interest over the last years for their potential applications in robotic system. Although some progress has been made in understanding of how biological systems use sensory signals to control reflex and voluntary behaviour, experimental platforms are still missing which allow us to study sensorimotor integration under closed-loop conditions. We developed a fly-robot interface (FRI) to investigate the dynamics of a 1-DoF image stabilization task. Neural signals recorded from an identified visual interneuron were used to control a two-wheeled robot which compensated for wide-field visual image shifts caused by externally induced rotations. We compared the frequency responses of two different controllers with static and adaptive feedback gains and their performance and found that they offer competing benefits for visual stabilization. In future research will use the FRI to study how different sensor systems contribute towards robust closed-loop motor control. © 2012 Springer-Verlag.
Saleem AB, Longden KD, Schwyn DA, et al., 2012, Bimodal optomotor response to plaids in blowflies: mechanisms of component selectivity and evidence for pattern selectivity., J Neurosci, Vol: 32, Pages: 1634-1642
Many animals estimate their self-motion and the movement of external objects by exploiting panoramic patterns of visual motion. To probe how visual systems process compound motion patterns, superimposed visual gratings moving in different directions, plaid stimuli, have been successfully used in vertebrates. Surprisingly, nothing is known about how visually guided insects process plaids. Here, we explored in the blowfly how the well characterized yaw optomotor reflex and the activity of identified visual interneurons depend on plaid stimuli. We show that contrary to previous expectations, the yaw optomotor reflex shows a bimodal directional tuning for certain plaid stimuli. To understand the neural correlates of this behavior, we recorded the responses of a visual interneuron supporting the reflex, the H1 cell, which was also bimodally tuned to the plaid direction. Using a computational model, we identified the essential neural processing steps required to capture the observed response properties. These processing steps have functional parallels with mechanisms found in the primate visual system, despite different biophysical implementations. By characterizing other visual neurons supporting visually guided behaviors, we found responses that ranged from being bimodally tuned to the stimulus direction (component-selective), to responses that appear to be tuned to the direction of the global pattern (pattern-selective). Our results extend the current understanding of neural mechanisms of motion processing in insects, and indicate that the fly employs a wider range of behavioral responses to multiple motion cues than previously reported.
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