Description du sujet de thèse

Modelling dynamic scene processing in the visual cortex using spiking neural networks

The aim of this thesis project is to model the processing of dynamic scenes within the visual cortex of living organisms (for example, in human or non-human primates), particularly along the dorsal pathway. This modelling will be carried out using spiking neural networks that process videos (or data collected by event-based cameras). These neural networks will notably feature the ability to use learnable synaptic delays (see: Hammouamri et al., 2023).

The selected candidate should wish to work on an interdisciplinary project combining computational neuroscience, neurobiology and computer science. He/she must also have a solid background in programming and artificial intelligence, solid theoretical knowledge of the nervous system and a good level of English.

Bibliography:
Hammouamri, I., Khalfaoui-Hassani, I., & Masquelier, T. (2023). Learning delays in spiking neural networks using dilated convolutions with learnable spacings. arXiv preprint arXiv:2306.17670.

Contexte de travail

The thesis work will be carried out at the Cerco laboratory (CNRS, UMR 5549) under the co-supervision of Benoit Cottereau and Timothée Masquelier. It will be carried out in collaboration with IPAL laboratory (IRL 2955 in Singapore) and the National University of Singapore (NUS). The doctoral student will be required to collaborate with researchers, engineers and students from these three entities. He/she will be enrolled in the MITT doctoral school (Mathematics, Computer Sciences and Telecommunications of Toulouse, see: https://adum.fr/as/ed/edmitt/page.pl?page=contact).

Contraintes et risques

None.