6 citations · 20 across the 12 of their papers we have counts for
15 papers
RoPE is Dead, Long Live RoPE: Towards Scalable Data-aware Positional Encodings
Jarod Lévy, Mathurin Videau, Jad Yehya +3
Transformers process tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural refinement. Rotary Position Embe…
Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings
Mingfang Zhang, Jarod Lévy, Cedric Rommel +9
Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brai…
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…
DANCE: Detect and Classify Events in EEG
Jarod Lévy, Hubert Banville, Jérémy Rapin +3
Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to known event onsets. However,…
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…
A foundation model of vision, audition, and language for in-silico neuroscience
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +5
Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. He…
Scaling and context steer LLMs along the same computational path as the human brain
Joséphine Raugel, Stéphane d'Ascoli, Jérémy Rapin +2
Recent studies suggest that the representations learned by large language models (LLMs) are partially aligned to those of the human brain. However, whether and why this alignment s…
TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +2
Historically, neuroscience has progressed by fragmenting into specialized domains, each focusing on isolated modalities, tasks, or brain regions. While fruitful, this approach hind…
Brain-to-Text Decoding: A Non-invasive Approach via Typing
Jarod Lévy, Mingfang Zhang, Svetlana Pinet +4
Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, these invasive devices entail risks inherent to neurosurgery.…
From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production
Mingfang Zhang, Jarod Lévy, Stéphane d'Ascoli +5
Humans effortlessly communicate their thoughts through intricate sequences of motor actions. Yet, the neural processes that coordinate language production remain largely unknown, i…
Scaling laws for decoding images from brain activity
Hubert Banville, Yohann Benchetrit, Stéphane d'Ascoli +2
Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural recordings? Here, we systematicall…
Decoding individual words from non-invasive brain recordings across 723 participants
Stéphane d'Ascoli, Corentin Bel, Jérémy Rapin +4
Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding…
A polar coordinate system represents syntax in large language models
Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla +2
Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural P…
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
Stéphane d'Ascoli, Sören Becker, Alexander Mathis +2
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution…