Modelling LGMD2 visual neuron system

Fu, Qinbing; Yue, Shigang

doi:10.1109/mlsp.2015.7324313

Cited by 19 publications

(34 citation statements)

References 16 publications

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“…With respect to the biological findings of ON and OFF pathways in many animals including the various kinds of flies, the second layer of Lamina consists of rectifying transient cells separating visual signals into parallel channels. Although this structure has not yet been found in locusts, recent studies on the LGMD1 and the LGMD2 may evidence similar ON (onset)and OFF (offset) mechanisms in the locust visual systems[68,67,69,72,176].…”

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confidence: 84%

“…Schematic of a seminalLGMD2-based visual neural network, adapted from[69,72]:the model processes visual information with ON and OFF mechanisms that encode brightness increments and decrements, separately: the ON channels are rigorously suppressed to realise the LGMD2's specific looming selectivity to dark objects. In this model, the excitations are delayed in the OFF pathway and the inhibitions are delayed in the ON pathway, due to the ON (onset) and OFF (offset) mechanisms.…”

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confidence: 99%

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

Wang

et al. 2019

Artificial Life

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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.

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

Wang

et al. 2019

Artificial Life

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“…More specifically, both locusts [10,11,12,13] and flies [14] possess the amazing ability to perceive impending collisions in complex and dynamic scenes. These biological visual systems provide inspiration to computational modellers to build neuromorphic collision detectors in mobile robotic applications [15,16,17,18,19,20,21], ground vehicles [22,23,24,25] and UAVs [26,7].…”

Section: Introductionmentioning

confidence: 99%

“…It has also been applied to computational models of direction-selective neurons [43] and small target movement detectors [44,45] in the fly's preliminary visual systems. Our previous partial studies have demonstrated the potential of such polarity pathways in modelling LGMD2 looming detectors [20,21]. In this paper, we apply the bioplausible ON and OFF pathways to model the LGMD1 looming detector and more importantly investigate its underlying functionality and characteristics.…”

Section: Introductionmentioning

confidence: 99%

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

Peng

et al. 2018

Neural Networks

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Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector - the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.

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“…Therefore, the proposed neural network is not suitable for perceiving collision. However, it is in complementary with the looming-detecting neuron models which can not extract directional motion cues from movements on four cardinal orientations [9], [23], [24], [26]. Second, on the aspect of translation on directions of frontto-back/back-to-front (Fig.…”

Section: B Challenged Against Synthetic Stimulimentioning

confidence: 99%

Modeling direction selective visual neural network with ON and OFF pathways for extracting motion cues from cluttered background

Yue

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The nature endows animals robust vision systems for extracting and recognizing different motion cues, detecting predators, chasing preys/mates in dynamic and cluttered environments. Direction selective neurons (DSNs), with preference to certain orientation visual stimulus, have been found in both vertebrates and invertebrates for decades. In this paper, with respect to recent biological research progress in motion-detecting circuitry, we propose a novel way to model DSNs for recognizing movements on four cardinal directions. It is based on an architecture of ON and OFF visual pathways underlies a theory of splitting motion signals into parallel channels, encoding brightness increments and decrements separately. To enhance the edge selectivity and speed response to moving objects, we put forth a bio-plausible spatial-temporal network structure with multiple connections of same polarity ON/OFF cells. Each pair-wised combination is filtered with dynamic delay depending on sampling distance. The proposed vision system was challenged against image streams from both synthetic and cluttered real physical scenarios. The results demonstrated three major contributions: first, the neural network fulfilled the characteristics of a postulated physiological map of conveying visual information through different neuropile layers; second, the DSNs model can extract useful directional motion cues from cluttered background robustly and timely, which hits at potential of quick implementation in visionbased micro mobile robots; moreover, it also represents better speed response compared to a state-of-the-art elementary motion detector.

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Modelling LGMD2 visual neuron system

Cited by 19 publications

References 16 publications

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

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

Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation

Modeling direction selective visual neural network with ON and OFF pathways for extracting motion cues from cluttered background

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