6 citations · 9 across the 12 of their papers we have counts for
12 papers
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Yohann Benchetrit, Marlène Careil, Simon Dahan +3
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain deco…
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments
Simon Dahan, Gabriel Bénédict, Logan Z. J. Williams +4
Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for brain computer interfaces (BCI) or ne…
Applications of interpretable deep learning in neuroimaging: a comprehensive review
Lindsay Munroe, Mariana da Silva, Faezeh Heidari +5
Clinical adoption of deep learning models has been hindered, in part, because the black-box nature of neural networks leads to concerns regarding their trustworthiness and reliabil…
Cortical Surface Diffusion Generative Models
Zhenshan Xie, Simon Dahan, Logan Z. J. Williams +2
Cortical surface analysis has gained increased prominence, given its potential implications for neurological and developmental disorders. Traditional vision diffusion models, while…
Spatio-Temporal Encoding of Brain Dynamics with Surface Masked Autoencoders
Simon Dahan, Logan Z. J. Williams, Yourong Guo +2
The development of robust and generalisable models for encoding the spatio-temporal dynamics of human brain activity is crucial for advancing neuroscientific discoveries. However,…
The Multiscale Surface Vision Transformer
Simon Dahan, Logan Z. J. Williams, Daniel Rueckert +1
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges…
Surface Analysis with Vision Transformers
Simon Dahan, Logan Z. J. Williams, Abdulah Fawaz +2
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design lim…
Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces
Simon Dahan, Hao Xu, Logan Z. J. Williams +10
Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attenti…
Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
Simon Dahan, Abdulah Fawaz, Logan Z. J. Williams +6
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design lim…
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Simon Dahan, Logan Z. J. Williams, Daniel Rueckert +1
The study of functional brain connectivity (FC) is important for understanding the underlying mechanisms of many psychiatric disorders. Many recent analyses adopt graph convolution…