CATNIP Lab is a computational and statistical neuroscience group. Our goal is to obtain an effective systems-level description of relevant neural dynamics in the context of cognitive functions and dysfunctions. To arrive at a model of neural computation tightly tied to biology and experimental observations, we work closely with experimental and clinical collaborators. We develop probabilistic methods for analyzing spatiotemporal neural and non-neural time series to infer neural dynamics models. To facilitate the scientific inference process, we develop real-time machine learning and control methods and design next-generation experiments.
See research page for details.
Recent News
Announcement
Universal approximation for dynamical systems
We have a new exciting line of work showing that the typical guarantees for univeral approximation for neural networks do not hold for recurrent systems in general. Abel’s new work...
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Announcement
Neuro-Cybernetics at Scale website lives
After the successful hosting of the new 3-day symposium in October 2025, the original website hosted at https://symposium.fchampalimaud.science/ has retired as it was part of the continuing cycle of Champalimaud...
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