NeuralSet: A High-Performing Python Package for Neuro-AI
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | King, Jean-Rémi Bel, Corentin Evanson, Linnea Gadonneix, Julien Houhamdi, Sophia Lévy, Jarod Raugel, Josephine Revilla, Andrea Santos Zhang, Mingfang Bonnaire, Julie Caucheteux, Charlotte Défossez, Alexandre Desbordes, Théo Diego-Simón, Pablo Khanna, Shubh Millet, Juliette Orhan, Pierre Panchavati, Saarang Ratouchniak, Antoine Thual, Alexis Brooks, Teon L. Begany, Katelyn Benchetrit, Yohann Careil, Marlène Banville, Hubert d'Ascoli, Stéphane Dahan, Simon Rapin, Jérémy |
| author_facet | King, Jean-Rémi Bel, Corentin Evanson, Linnea Gadonneix, Julien Houhamdi, Sophia Lévy, Jarod Raugel, Josephine Revilla, Andrea Santos Zhang, Mingfang Bonnaire, Julie Caucheteux, Charlotte Défossez, Alexandre Desbordes, Théo Diego-Simón, Pablo Khanna, Shubh Millet, Juliette Orhan, Pierre Panchavati, Saarang Ratouchniak, Antoine Thual, Alexis Brooks, Teon L. Begany, Katelyn Benchetrit, Yohann Careil, Marlène Banville, Hubert d'Ascoli, Stéphane Dahan, Simon Rapin, Jérémy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NeuralSet: A High-Performing Python Package for Neuro-AI King, Jean-Rémi Bel, Corentin Evanson, Linnea Gadonneix, Julien Houhamdi, Sophia Lévy, Jarod Raugel, Josephine Revilla, Andrea Santos Zhang, Mingfang Bonnaire, Julie Caucheteux, Charlotte Défossez, Alexandre Desbordes, Théo Diego-Simón, Pablo Khanna, Shubh Millet, Juliette Orhan, Pierre Panchavati, Saarang Ratouchniak, Antoine Thual, Alexis Brooks, Teon L. Begany, Katelyn Benchetrit, Yohann Careil, Marlène Banville, Hubert d'Ascoli, Stéphane Dahan, Simon Rapin, Jérémy Neurons and Cognition 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. |
| title | NeuralSet: A High-Performing Python Package for Neuro-AI |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2605.03169 |