For autonomous robots in dynamic environments mixed with human, it is vital to detect impending collision quickly and robustly. The biological visual systems evolved over millions of years may provide us efficient solutions for collision detection in complex environments. In the cockpit of locusts, two Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified which respond to looming objects rigorously with high firing rates. Compared to LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to dark moving objects against bright background in depth while not responding to light objects embedded in dark background - a similar situation which ground vehicles and robots are facing with. However, little work has been done on modeling LGMD2, let alone its potential in robotics and other vision-based applications. In this article, we propose a novel way of modeling LGMD2 neuron, with biased ON and OFF pathways splitting visual streams into parallel channels encoding brightness increments and decrements separately to fulfill its selectivity. Moreover, we apply a biophysical mechanism of spike frequency adaptation to shape the looming selectivity in such a collision-detecting neuron model. The proposed visual neural network has been tested with systematic experiments, challenged against synthetic and real physical stimuli, as well as image streams from the sensor of a miniature robot. The results demonstrated this framework is able to detect looming dark objects embedded in bright backgrounds selectively, which make it ideal for ground mobile platforms. The robotic experiments also showed its robustness in collision detection - it performed well for near range navigation in an arena with many obstacles. Its enhanced collision selectivity to dark approaching objects versus receding and translating ones has also been verified via systematic experiments.
To be more specific, when challenged by constant translations, a fixed number of photoreceptors in the retina are activated which makes the neuron susceptible to adaptation. However, when facing approach, an increasing number of photoreceptors will be activated which likely leads the neuron to overcome adaptation. The SFA suppresses neural response to translating stimuli but has little effect on approaching ones underlying its indispensable role of shaping the selectivity of LGMDs for looming over translating stimuli @cite_47 @cite_51 @cite_12 .
@cite_47 @cite_51 @cite_12
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Spike-frequency adaptation is the reduction of a neuron’s firing rate to a stimulus of constant intensity. In the locust, the Lobula Giant Movement Detector (LGMD) is a visual interneuron that exhibits rapid adaptation to both current injection and visual stimuli. Here, a reduced compartmental model of the LGMD is employed to explore adaptation’s role in selectivity for stimuli whose intensity changes with time. We show that supralinearly increasing current injection stimuli are best at driving a high spike count in the response, while linearly increasing current injection stimuli (i.e., ramps) are best at attaining large firing rate changes in an adapting neuron. This result is extended with in vivo experiments showing that the LGMD’s response to translating stimuli having a supralinear velocity profile is larger than the response to constant or linearly increasing velocity translation. Furthermore, we show that the LGMD’s preference for approaching versus receding stimuli can partly be accounted for by adaptation. Finally, we show that the LGMD’s adaptation mechanism appears well tuned to minimize sensitivity for the level of basal input. The LGMD2 belongs to a group of giant movement-detecting neurones which have fan-shaped arbors in the lobula of the locust optic lobe and respond to movements of objects. One of these neurones, the LGMD1, has been shown to respond directionally to movements of objects in depth, generating vigorous, maintained spike discharges during object approach. Here we compare the responses of the LGMD2 neurone with those of the LGMD1 to simulated movements of objects in depth and examine different image cues which could allow the LGMD2 to distinguish approaching from receding objects. In the absence of stimulation, the LGMD2 has a resting discharge of 10–40 spikes s−1 compared with <1 spike s−1 for the LGMD1. The most powerful excitatory stimulus for the LGMD2 is a dark object approaching the eye. Responses to approaching objects are suppressed by wide field movements of the background. Unlike the LGMD1, the LGMD2 is not excited by the approach of light objects; it specifically responds to movement of edges in the light to dark direction. Both neurones rely on the same monocular image cues to distinguish approaching from receding objects: an increase in the velocity with which edges of images travel over the eye; and an increase in the extent of edges in the image during approach. No dendritic spikes occur in the input fan of the lobular giant movement detector (LGMD) neurone. The action potentials are initiated at the point of thickening of the axon, which therefore represents the site of convergence of the retinotopic projection in the MD system. Previous work has shown that the site of decrement in response to repetitive visual stimulation is distal to this point. No change in spiking threshold in the LGMD could be demonstrated, and decrement in the number of LGMD action potentials is completely explained by the observed decrement of EPSPs recorded in the LGMD input dendritic fan. Possible postsynaptic mechanisms which might affect EPSP amplitude are excluded experimentally or shown to be improbable. Latency measurements during electrical stimulation in the second chiasma (which produces a decrementing EPSP in the fan) indicate that the pathway from the chiasma afferents to the LGMD fan is probably monosynaptic. By exclusion, the site of decrement appears to be located at the presynaptic terminal of that synapse. Generalization of habituation of the response to ON and OFF stimuli is demonstrated, showing that the presynaptic neurone at the labile synapse is an ON OFF unit. The greater part of the previously described sensitivity gradient on the retina, relative to the MD response, appears to be explicable by the geometry of the LGMD fan and of the retinotopic projection. We conclude that the LGMD is fed by a homogeneous population of ON OFF units running in the second optic chiasma, which form labile synapses on the input fan. 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 neural basis of behavior. On the other hand, artificial collision avoidance is a complex task, in which the algorithms used need to be fast to process the captured data and then perform real time decisions. Consequently, neurorobotic models may provide a foundation for the development of more effective and autonomous devices. In this paper, we will focus our attention in the Lobula Giant Movement Detector (LGMD), which is a visual neuron, located in the third layer of the locust optic lobe, that responds selectively to approaching objects, being responsible for trigger collision avoidance maneuvers in locusts. This selectivity of the LGMD neuron to approaching objects seems to result from the dynamics of the network pre-synaptic to this neuron. Tipically, this modelation is done by a conventional Difference of Gaussians (DoG) filter. In this paper, we propose the integration of a different model, an Inversed Difference of Gaussians (IDoG) filter, which preserves the different level of brightness in the captured image, enhancing the contrast at the edges. This change is expected to increase the performance of the LGMD model. Finally, a comparative analysis of both modelations, as well as its effect in the final response of the LGMD neuron, will be performed.