Abstract

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.

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