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About

Ulysses Bernardet is a pioneering researcher in computational neuroscience and neurorobotics, whose work bridges the gap between biological neural systems and artificial agents. His key research areas include neural modeling, allostatic control, and bio-inspired robotics, with a particular focus on understanding how simple neural circuits generate complex behaviors. Bernardet's most influential contribution is his development of an artificial moth model for chemical source localization, which simulates the optomotor anemotactic search behavior of real moths and has garnered 122 citations. He also made significant strides in understanding non-linear neuronal responses, as demonstrated by his study of the locust Lobula Giant Movement Detector (LGMD) neuron, which revealed emergent properties in afferent networks. Bernardet created the "iqr" tool, a multi-level simulation platform for brain and behavior that has been widely adopted (32 citations). His work on allostatic control in robots, comparing rodent behavior to robotic regulation, has been cited over 60 times collectively, showcasing his interdisciplinary approach. Notably, Bernardet developed the Roboser system, an autonomous interactive music composition platform, and has explored implicit interactions in artificial social agents, reflecting his broad impact across neuroscience, robotics, and human-computer interaction.