A bio-inspired computational model of motion detection

This project is an innovative undertaking combining insights of computational neuroscience, neuroethology, computer vision and robotics. 
It aims to developp a biologically-inspired computationally simple model of complex motion detection that could be used to drive generic actuators for robotics and other applications. Specifically, we will investigate the extent to which a a computational model of biological constrained neural systems of insects, with an empgasis in the locust, is able to effectively and robustly detect collisions and trigger evasion maneuvres in an efficient manner.

Our neuronal model will include components for course stabilization and collision avoidance. The former is derived from the fly opto-motor system. The latter will use a well-described insect collision detection system that consists of the locust Lobula Giant Movement Detector (LGMD) and its postsynaptic partner, the Descending Contralateral Movement Detector (DCMD).
In order to develop a very realistic model of the LGMD neuron,we will also collect physiological recordings from the LGMD pathway to complex visual stimuli and, posteriorly, we will analyse the neuronal responses obtained.

The experiments will be conducted in robots (khepera robot, DRK) to show the effictiveness, feasibility and robustness of the proposed model, and that the model is able to achieve autonomous navigation.

For the success of this project, the team involved gathers expertise in computational neuroscience, computer vision, robotics, insect physiology and neurowthology.

People involved in this project: 
Project status: 
Past or closed project