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.
LGMDs are well-known to respond better to looming than translating stimuli which are independent of velocity, size and direction of motion @cite_12 @cite_43 . In such collision-sensitive neurons, there are many mechanisms cooperating to shape their specific selectivities @cite_51 . For LGMDs, two intrinsic processes have been well-studied which decline firing rate elicited during rapid change or sustained stimuli. The first is FFI as mentioned above, which mainly deals with transient luminance change over a large field. On the aspect of sustained stimuli, the biophysical mechanism - spike frequency adaptation also takes part in mediating looming selectivity in the spiking initiation zone (SIZ in Fig. ). When stimulated with constant stimuli, the neuron initially responds with a high spike rate then decays down to a lower steady state frequency @cite_46 . Actually, the neural circuitry of LGMD2 shaping collision avoidance must be tuned to approaching rather than translating visual stimuli, as only the former type should reliably evoke collision-avoidance behaviors.
@cite_43 @cite_46 @cite_51 @cite_12
2100478972 2053822645 1984736547 1975147998
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. 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. 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. The lobula giant movement detector (LGMD) neuron of locusts has been shown to preferentially respond to objects approaching the eye of a locust on a direct collision course. Computer simulations of the neuron have been developed and have demonstrated the ability of mobile robots, interfaced with a simulated LGMD model, to avoid collisions. In this study, a model of the LGMD neuron is presented and the functional parameters of the model identified. Models with different parameters were presented with a range of automotive video sequences, including collisions with cars. The parameters were optimised to respond correctly to the video sequences using a range of genetic algorithms (GAs). The model evolved most rapidly using GAs with high clone rates into a form suitable for detecting collisions with cars and not producing false collision alerts to most non-collision scenes.