The temporality of information is crucial to our understanding of the world. Synchronization between different events guides our perception and our actions in many tasks. For example, speech understanding is improved by lip-reading in a context of synchronization between visual and sound perception. In the field of artificial intelligence, spike neural networks offer a paradigm inspired by the functioning of the human brain, which is based on the synchronization between neuronal impulses. These neural networks are likely to be more efficient than the classical neural networks used in the field of machine learning, and less costly in terms of hardware. They also offer new possibilities for processing temporal data and analyzing synchronizations. The MODPULS project aims at studying the possibilities and the limits of the use of spike neural networks for the analysis of temporal data related to synchronization, rhythm, and human movement. We propose to create a set of temporal and rhythmic data of different natures and complexities, combining audio, video and human motion data. The implementation of synchronization analysis tasks using spike neural networks will offer opportunities for innovative scientific contributions in the field of artificial intelligence. The fine analysis of temporal data also opens the field to numerous applications, notably in the human sciences, for example through the analysis of musical rhythms, but also in the medical field through the therapeutic analysis of social synchronizations.

The ModPuls project is funded by ANR (ANR-22-CE38-0006-01).