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.
In the LGMD1 state-of-the-art models, e.g. @cite_4 @cite_20 @cite_25 @cite_35 , the lateral inhibition is always time-delayed relative to the excitation. However, the excitation has also been assumed to be time-delayed relative to the inhibition @cite_27 @cite_31 @cite_42 . With the similar idea in the modeling of collision-detecting neuron @cite_31 , we demonstrate the ON and OFF mechanism leads effects on different relatively delayed information in the LGMD2 neuron model: luminance increments will activate ON cells to elicit onset responses - the excitation is time advanced relative to the inhibition in ON pathway; otherwise, the excitation is time delayed relative to the inhibition in OFF pathway, i.e, OFF cells generate offset responses by brightness decrements.
@cite_35 @cite_4 @cite_42 @cite_27 @cite_31 @cite_25 @cite_20
1999123843 1982808961 2133741176 1984736547
In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons. Two Lobula Giant Movement Detectors (LGMDs) have been identified in the lobula region of the locust visual system: LGMD1 and LGMD2. LGMD1 had been successfully used in robot navigation to avoid impending collision. LGMD2 also responds to looming stimuli in depth, and shares most the same properties with LGMD1; however, LGMD2 has its specific collision selective responds when dealing with different visual stimulus. Therefore, in this paper, we propose a novel way to model LGMD2, in order to emulate its predicted bio-functions, moreover, to solve some defects of previous LGMD1 computational models. The mechanism of ON and OFF cells, as well as bio-inspired nonlinear functions, are introduced in our model, to achieve LGMD2's collision selectivity. Our model has been tested by a miniature mobile robot in real time. The results suggested this model has an ideal performance in both software and hardware for collision recognition. 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. 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.