Head: Timothée MASQUELIER
Our new team embodies the cross-pollination between Neuroscience and Artificial Intelligence (AI). We:
– employ state-of-the-art AI methods to effectively address outstanding neuroscience questions. For example deep learning tools are used to find patterns in vast amounts of fMRI and EEG data, and to relate them to stimuli, cognition, behavior and well being.
– use our knowledge of the brain to design improved AI algorithms and artificial neural network architectures, for example by incorporating spikes, feedback, oscillations, or more human-like representations.
Keywords
Artificial intelligence, machine learning, deep learning, neural networks, computational neuroscience, vision, audition, natural language processing.
Permanent research staff
Associated member
Engineers
Leslie MARIE-LOUISE
Raja KUMAR
Post-Doctoral fellows
Hugo CHATEAU-LAURENT
Benjamin DEVILLERS
Antoine GRIMALDI
Nicolas KUSKE
Mitja NIKOLAUS
Jacob SCHWENK
Doctoral students
Grégoire AUDRY
Jan Erik BELLINGRATH
Roland BERTIN-JOHANNET
Tomas DE UDAETA
Adrien MARQUE
Leopold MAYTIE
Sabine MUZELLEC
Ulysse RANCON
Representative publications
- Hammouamri I, Khalfaoui-Hassani I, Masquelier T (2024) Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings. In: ICLR, pp 1–12 Available at: https://openreview.net/forum?id=4r2ybzJnmN.
- Alamia, A., Gordillo, D., Chkonia, E., Roinishvili, M., Cappe, C., & Herzog, M. H. (2024) Oscillatory traveling waves provide evidence for predictive coding abnormalities in schizophrenia. Biological Psychiatry.
- Ahuja, N.Y. Rodriguez, A.K. Ashok, T. Serre, T. Desrochers, D. Sheinberg (2024) Monkeys engage in visual simulation to solve complex problems. Current Biology.
- Linsley, I.F. Rodriguez, T. Fel, M. Arcaro, S. Sharma, M. Livingstone & T. Serre (2024) Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex. Neural Information Processing Systems.
- Alamia, A., Terral, L., D’ambra, M. R., & VanRullen, R. (2023). Distinct roles of forward and backward alpha-band waves in spatial visual attention. Elife, 12, e85035.
- Boutin, V., Fel, T., Singhal, L., Mukherji, R., Nagaraj, A., Colin, J., & Serre, T. (2023). Diffusion models as artists: are we closing the gap between humans and machines?. In Proceedings of the 40th International Conference on Machine Learning (pp. 2953-3002). https://arxiv.org/pdf/2301.11722
- Fang W, Chen Y, Ding J, Yu Z, Masquelier T, Chen D, Huang L, Zhou H, Li G, Tian Y (2023) SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence. Sci Adv 9 Available at: https://www.science.org/doi/10.1126/sciadv.adi1480.
- Thomas, Fel, Boutin, V., Béthune, L., Cadene, R., Moayeri, M., Andéol, L., … & Serre, T. (2023) A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation. In Thirty-seventh Conference on Neural Information Processing Systems. https://arxiv.org/pdf/2306.07304
- Ozcelik, F., & VanRullen, R. (2023). Brain Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion. Scientific Reports, 13(1), 15666.
- Truong, M. A. Khalid and A. Delorme (2023) « Deep learning applied to EEG data with different montages using spatial attention, » 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye, 2023, pp. 2587-2593, doi: 10.1109/BIBM58861.2023.10385525. https://ieeexplore.ieee.org/document/10385525
- VanRullen, R., & Kanai, R. (2021). Deep learning and the global workspace theory. Trends in Neurosciences, 44(9), 692-704.
- Truong, D., Milham, M., Makeig, S., Delorme A. (2021) Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021, pp. 1039-1042, doi: 10.1109/EMBC46164.2021.9630708. IEEE website. arxiv preprint.