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 addition, the receding stimuli will bring about a decreasing number of stimulated photoreceptors which also causes adaptation @cite_12 @cite_47 . As a result of that, the SFA is a reduction of neurons' firing rate to a stimulus of constant intensity, which makes it ideal in shaping LGMD2' looming selectivity for approach over recession and translation. There are some computational roles of SFA, one of which is acting as a high-pass filter on largely stationary inputs @cite_12 @cite_46 . More broadly, such a selective mechanism has also been proved useful in the neural processing of not only visual, but also auditory and electro-sensory systems @cite_12 .
@cite_46 @cite_47 @cite_12
2100478972 1977814411 2133741176 1984736547
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. Slow Feature Analysis (SFA) extracts slowly varying features from a quickly varying input signal [1]. It has been successfully applied to modeling the visual receptive fields of the cortical neurons. Sufficient experimental results in neuroscience suggest that the temporal slowness principle is a general learning principle in visual perception. In this paper, we introduce the SFA framework to the problem of human action recognition by incorporating the discriminative information with SFA learning and considering the spatial relationship of body parts. In particular, we consider four kinds of SFA learning strategies, including the original unsupervised SFA (U-SFA), the supervised SFA (S-SFA), the discriminative SFA (D-SFA), and the spatial discriminative SFA (SD--SFA), to extract slow feature functions from a large amount of training cuboids which are obtained by random sampling in motion boundaries. Afterward, to represent action sequences, the squared first order temporal derivatives are accumulated over all transformed cuboids into one feature vector, which is termed the Accumulated Squared Derivative (ASD) feature. The ASD feature encodes the statistical distribution of slow features in an action sequence. Finally, a linear support vector machine (SVM) is trained to classify actions represented by ASD features. We conduct extensive experiments, including two sets of control experiments, two sets of large scale experiments on the KTH and Weizmann databases, and two sets of experiments on the CASIA and UT-interaction databases, to demonstrate the effectiveness of SFA for human action recognition. Experimental results suggest that the SFA-based approach (1) is able to extract useful motion patterns and improves the recognition performance, (2) requires less intermediate processing steps but achieves comparable or even better performance, and (3) has good potential to recognize complex multiperson activities. 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.