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 fragmented software ecosystem. Current tools are siloed by recording modality and optimized for small-scale, in-memory workflows, limiting the use of massive, naturalistic datasets. Here, we introduce NeuralSet, a Python framework that efficiently unifies the processing of diverse neural recordings (including fMRI, M/EEG, and spikes) and complex experimental stimuli (such as text, audio, and video). By decoupling experimental metadata from lazy, memory-efficient data extraction, NeuralSet harmonizes standard neuroscientific preprocessing pipelines with pretrained deep learning embeddings. This approach provides a single PyTorch-ready interface that scales seamlessly from local prototyping to high-performance cluster execution. By eliminating manual data wrangling and ensuring full computational provenance, NeuralSet establishes a scalable, unified infrastructure for the next generation of neuro-AI research.
@article{arxiv.2605.03169,
title = {NeuralSet: A High-Performing Python Package for Neuro-AI},
author = {Jean-Rémi King and Corentin Bel and Linnea Evanson and Julien Gadonneix and Sophia Houhamdi and Jarod Lévy and Josephine Raugel and Andrea Santos Revilla and Mingfang Zhang and Julie Bonnaire and Charlotte Caucheteux and Alexandre Défossez and Théo Desbordes and Pablo Diego-Simón and Shubh Khanna and Juliette Millet and Pierre Orhan and Saarang Panchavati and Antoine Ratouchniak and Alexis Thual and Teon L. Brooks and Katelyn Begany and Yohann Benchetrit and Marlène Careil and Hubert Banville and Stéphane d'Ascoli and Simon Dahan and Jérémy Rapin},
journal= {arXiv preprint arXiv:2605.03169},
year = {2026}
}