5 papers
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Joséphine Raugel, Maximilian Seitzer, Marc Szafraniec +6
Backpropagation is the core learning mechanism underlying deep learning. However, whether and how this algorithm is implemented in the brain remains highly debated. In particular,…
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…
Disentangling the Factors of Convergence between Brains and Computer Vision Models
Joséphine Raugel, Marc Szafraniec, Huy V. Vo +5
Many AI models trained on natural images develop representations that resemble those of the human brain. However, the factors that drive this brain-model similarity remain poorly u…