Description

The current models retrieving data from event-based cameras reconstruct pixel images on which image processing algorithms can beapplied. Thus, the asynchronous and event-driven nature is ultimately poorly considered. A model based on spiking neural networkswould follow the dynamics of the inputs and would lead to innovative algorithms that surpass current processing chains.

Skills required

AI, neural networks, image processing Python programming

Bibliography

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• Cuadrado, J., Rançon, U., Cottereau, B. R., Barranco, F., & Masquelier, T. (2023). Optical flow estimation from event-based camerasand spiking neural networks. Frontiers in Neuroscience, 17, 1160034.
• Nazari, S., Amiri, M. An accurate and fast learning approach in the biologically spiking neural network. Sci Rep 15, 6585 (2025).https://doi.org/10.1038/s41598-025-90113-0
° Rançon, U., Cuadrado-Anibarro, J., Cottereau, B. R., & Masquelier, T. (2022). Stereospike: Depth learning with a spiking neuralnetwork. IEEE Access, 10, 127428-127439.
• Simon Chane, Camille, Ernst Niebur, Ryad Benosman, and Sio-Hoi Ieng (2024) An event-based implementation of saliency-basedvisual attention for rapid scene analysis. IEEE Transactions on Cognitive and Developmental Systems, 1 13.https://doi.org/10.1109/TCDS.2024.3480153.
• Vicente-Sola, A., Manna, D.L., Kirkland, P., Di Caterina, G., Bihl, T.J. (2025) Spiking Neural Networks for event-based actionrecognition: A new task to understand their advantage, Neurocomputing 611, https://doi.org/10.1016/j.neucom.2024.128657

Keywords

Spiking Neural Networks, Event-based camera