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 recent years, the ON and OFF visual pathways have been found in motion detection circuitry of not only insects like the drosophila @cite_36 @cite_13 , but also vertebrates like the rabbit @cite_30 , which reveal the fundamental principle of processing visual information - signals are separated into parallel ON and OFF pathways encoding brightness increments (onset events) in ON channels and decrements (offset events) in OFF channels respectively @cite_23 @cite_21 @cite_24 @cite_5 . As a matter of fact, such a structure has been asserted to play an irreplaceable role underlying separated pathways in motion detection circuit @cite_30 . Although there is little evidence that such pathways exist in locusts, as early in 1970s, LGMDs were proposed to be fed by a homogeneous population of ON and OFF cells in their pre-synaptic areas @cite_44 . As depicted in Fig. , the signals conveyed in subfield A of LGMDs dendritic tree were put forward to be mediated by such polarity cells @cite_42 .
@cite_30 @cite_36 @cite_21 @cite_42 @cite_24 @cite_44 @cite_23 @cite_5 @cite_13
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In both vertebrates and invertebrates, evidence supports separation of luminance increments and decrements (ON and OFF channels) in early stages of visual processing ([Hartline, 1938][1]; [, 2010][2]); however, less is known about how these parallel pathways are recombined to encode form and motion. In Drosophila , genetic knockdown of inputs to putative ON and OFF pathways and direct recording from downstream neurons in the wide-field motion pathway reveal that local elementary motion detectors exist in pairs that separately correlate contrast polarity channels, ON with ON and OFF with OFF ([, 2013][3]). However, behavioral responses to reverse-phi motion of discrete features reveal additional correlations of the opposite signs ([, 2011][4]). We here present intracellular recordings from feature detecting neurons in the dragonfly that provide direct physiological evidence for the correlation of OFF and ON pathways. These neurons show clear polarity selectivity for feature contrast, responding strongly to targets that are darker than the background and only weakly to dark contrasting edges. These dark target responses are much stronger than the linear combination of responses to ON and OFF edges. We compare these data with output from elementary motion detector-based models ([, 2011][5]; [, 2011][4]), with and without stages of strong center-surround antagonism. Our data support an alternative elementary small target motion detector model, which derives dark target selectivity from the correlation of a delayed OFF with an un-delayed ON signal at each individual visual processing unit ([, 2008][6], [2009][7]). @PARASPLIT [1]: #ref-14 [2]: #ref-19 [3]: #ref-20 [4]: #ref-5 [5]: #ref-11 [6]: #ref-41 [7]: #ref-39 In the fly Drosophila melanogaster , photoreceptor input to motion vision is split into two parallel pathways as represented by first-order interneurons L1 and L2 ([, 2007][1]; [, 2010][2]). However, how these pathways are functionally specialized remains controversial. One study ([, 2011][3]) proposed that the L1-pathway evaluates only sequences of brightness increments (ON-ON), while the L2-pathway processes exclusively brightness decrements (OFF-OFF). Another study ([, 2011][4]) proposed that each of the two pathways evaluates both ON-ON and OFF-OFF sequences. To decide between these alternatives, we recorded from motion-sensitive neurons in flies in which the output from either L1 or L2 was genetically blocked. We found that blocking L1 abolishes ON-ON responses but leaves OFF-OFF responses intact. The opposite was true, when the output from L2 was blocked. We conclude that the L1 and L2 pathways are functionally specialized to detect ON-ON and OFF-OFF sequences, respectively. @PARASPLIT [1]: #ref-17 [2]: #ref-11 [3]: #ref-6 [4]: #ref-4 Summary Recent experiments have shown that motion detection in Drosophila starts with splitting the visual input into two parallel channels encoding brightness increments (ON) or decrements (OFF). This suggests the existence of either two (ON-ON, OFF-OFF) or four (for all pairwise interactions) separate motion detectors. To decide between these possibilities, we stimulated flies using sequences of ON and OFF brightness pulses while recording from motion-sensitive tangential cells. We found direction-selective responses to sequences of same sign (ON-ON, OFF-OFF), but not of opposite sign (ON-OFF, OFF-ON), refuting the existence of four separate detectors. Based on further measurements, we propose a model that reproduces a variety of additional experimental data sets, including ones that were previously interpreted as support for four separate detectors. Our experiments and the derived model mark an important step in guiding further dissection of the fly motion detection circuit. 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.