NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-R\'emi King, Corentin Bel, Linnea Evanson, Julien Gadonneix, Sophia Houhamdi, Jarod L\'evy, Josephine Raugel, Andrea Santos Revilla, Mingfang Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre D\'efossez, Th\'eo Desbordes, Pablo Diego-Sim\'on, Shubh Khanna

TL;DR
NeuralSet is a Python package that unifies processing of diverse neural data and stimuli, enabling scalable, efficient neuro-AI research with full provenance and compatibility with deep learning workflows.
Contribution
It introduces NeuralSet, a framework that integrates various neural recordings and stimuli processing into a scalable, memory-efficient, PyTorch-compatible system for neuro-AI research.
Findings
Supports multiple neural data modalities including fMRI, M/EEG, and spikes.
Provides a unified, scalable interface compatible with deep learning workflows.
Eliminates manual data wrangling and ensures full computational provenance.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
