Abstract
Abstract
Locusts possess a collision-sensitive pathway in their visual system culminating in a neuron, the lobula giant movement detector (LGMD), which preferentially responds to looming stimuli. The LGMD is also notable for its reduced or absent response to other stimuli, such as translating objects, wide-field motion, and incoherent images of looming objects. While experimental and modelling work have both shed light on the various mechanisms underpinning these properties, broad examinations of how these mechanisms interact with each other and with diverse stimulus types have been limited. To address this, we developed a model incorporating mechanisms from recent literature, and subjected to an array of looming stimuli with varying size-to-speed ratio, polarity (OFF and ON), and coherence; wide-field background motion, translating stimuli, and trajectory changes were also examined. The model showed quantitative and qualitative fidelity to biological data in its replication of a wide variety of stimulus responses, with its preference for incoherent visual stimuli being a notable exception. Based on investigations with various inhibition types removed, it was shown that lateral inhibition predominantly suppressed wide-field motion responses. Global inhibition both normalized growing excitation over the course of looming, and improved recognition of looming stimuli against moving backgrounds. Feedforward inhibition was found to have diverse roles, including shaping peak response time and its variability, and improving coherence selectivity. The success of the model in replicating multiple stimuli also showed the plausibility of several underlying assumptions, including sharing of input between the LGMD and multiple other neuron types.
Author summary
The locust LGMD is a key visual neuron that detects looming objects approaching on a collision course. We modelled this neuron and the network of neurons associated with it to test hypotheses about the structure of the network, and to better understand functions of its various parts. Our hypotheses included that the neurons which excite the LGMD and those which inhibit it share the same inputs, keeping the network simpler. Both the LGMD and other neurons in our model behaved similarly to real life in multiple situations, supporting our hypothesis. By examining the model LGMD outputs with various mechanisms removed from the model, we determined the roles and functions of these mechanisms in recognizing and responding to impending collisions. Two separate mechanisms—global and lateral inhibition—were required to detect collisions when a moving background was present, a task vital to a locust in flight. Another mechanism—feedforward inhibition—showed potential to make escapes from predators more timely but less predictable, and to help in distinguishing true colliding objects from similar background noise. Overall, our model furthers understanding of how locusts visually detect and respond to collisions, and provides a useful platform for future work.