The 2013 International Joint Conference on Neural Networks (IJCNN) 2013
DOI: 10.1109/ijcnn.2013.6707010
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Modeling disinhibition within a layered structure of the LGMD neuron

Abstract: Due to their relatively simple nervous system, insects are an excellent way through which we can investigate how visual information is acquired and processed in order to trigger specific behaviours, as flight stabilization, flying speed adaptation, collision avoidance responses, among others. From the behaviours previously mentioned, we are particularly interested in visually evoked collision avoidance responses. These behaviors are, by necessity, fast and robust, making them excellent systems to study the neu… Show more

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Cited by 4 publications
(6 citation statements)
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“…The experimental data is adapted from [101]. model encoding onset and offset responses by luminance increments and decrements, adapted from [114], (b) a modified LGMD1 model for multiple looming objects detection, adapted from [233], (c) a simplified LGMD1 model for collision avoidance of an UAV, adapted from [188], (d) a modified LGMD1 model with enhancement of collision selectivity, adapted from [133,132], (e) a modified LGMD1 model with a new layer for noise reduction and spikingthreshold mediation, adapted from [198,197], (f) an LGMD1 neural network based on the modelling of elementary motion detectors for collision detection in ground vehicle scenarios, adapted from [91]. Based on this LGMD1 modelling theory, a good number of models have been produced during the past two decades; these works have not only been extending and consolidating the LGMD1's original functionality for looming perception, but also investigating the possible applications to mobile machines like robots and vehicles.…”
Section: Computational Models and Applicationsmentioning
confidence: 99%
See 2 more Smart Citations
“…The experimental data is adapted from [101]. model encoding onset and offset responses by luminance increments and decrements, adapted from [114], (b) a modified LGMD1 model for multiple looming objects detection, adapted from [233], (c) a simplified LGMD1 model for collision avoidance of an UAV, adapted from [188], (d) a modified LGMD1 model with enhancement of collision selectivity, adapted from [133,132], (e) a modified LGMD1 model with a new layer for noise reduction and spikingthreshold mediation, adapted from [198,197], (f) an LGMD1 neural network based on the modelling of elementary motion detectors for collision detection in ground vehicle scenarios, adapted from [91]. Based on this LGMD1 modelling theory, a good number of models have been produced during the past two decades; these works have not only been extending and consolidating the LGMD1's original functionality for looming perception, but also investigating the possible applications to mobile machines like robots and vehicles.…”
Section: Computational Models and Applicationsmentioning
confidence: 99%
“…10. These computational models consist of new methods to enhance the collision selectivity to approaching objects [133], new layers to reduce environmental noise [198,197], and etc.There are also researches on corresponding applications for cars [124,91] and mobile robots [43], as well as implementations in hardware like field-programmable gate array (FPGA) [132].…”
Section: Computational Models and Applicationsmentioning
confidence: 99%
See 1 more Smart Citation
“…Silva et al [31], [32] proposed a modified LGMD architecture integrating two previous LGMD models to implement features such as noise immunity proposed in [18], [33] and direction detection proposed in [19], the optimized model is validated against a set of test cases (collision scenarios), where a successful filtering of isolated excitations is performed to prevent the perturbations from contributing to the excitation of the LGMD cell. Silva goes on to demonstrate that the neural architecture introduced in [18] was unsatisfactory when tested for shrinking stimuli whereas the model in [19] detected obstacle motion direction in depth.…”
Section: Introductionmentioning
confidence: 99%
“…Computationally modeling the fascinating collisiondetecting neurons such as LGMD1 and LGMD2 will not only deepen our understanding of the visual pathways in locusts, but also shed lights to vision systems for future robots. In the past decades, LGMD1 neuron has been modeled and tested in vehicles and robots for collision detection [3], [14]- [20]. On the other hand, for LGMD2 in juvenile locusts, although it shows unique selectivity on dark looming objects against bright background, yet very little LGMD2 modeling work has been done in the past [21], [22].…”
Section: Introductionmentioning
confidence: 99%