1 citations · 2 across the 6 of their papers we have counts for
7 papers
CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
Gabriel Mahuas, Victoria Shevchenko, Ugo Tanielian +2
Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converg…
The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail
Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell +2
Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data. We posit that these models should capture each subject's cognitive performance from their f…
ReBaPL: Repulsive Bayesian Prompt Learning
Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma +3
Prompt learning has emerged as an effective technique for fine-tuning large-scale foundation models for downstream tasks. However, conventional prompt learning methods are prone to…
Diagrammatic expansion for the mutual-information rate in the realm of limited statistics
Tobias Kühn, Gabriel Mahuas, Ulisse Ferrari
Neurons in sensory systems encode stimulus information into their stochastic spiking response. The mutual information has been extensively applied to these systems to quantify the…
Strong, but not weak, noise correlations are beneficial for population coding
Gabriel Mahuas, Thomas Buffet, Olivier Marre +2
Neural correlations play a critical role in sensory information coding. They are of two kinds: signal correlations, when neurons have overlapping sensitivities, and noise correlati…
A small-correlation expansion to quantify information in noisy sensory systems
Gabriel Mahuas, Olivier Marre, Thierry Mora +1
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with comple…
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
Gabriel Mahuas, Giulio Isacchini, Olivier Marre +2
Generalized linear models are one of the most efficient paradigms for predicting the correlated stochastic activity of neuronal networks in response to external stimuli, with appli…