# NeuralSet: A High-Performing Python Package for Neuro-AI

arXiv: https://arxiv.org/abs/2605.03169 · HTML: https://mlsanity.com/paper/2605.03169/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2026.

## Built on by (most cited first, 5 of 5)

- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale](https://mlsanity.com/paper/2608.25204/) (2026)
- [Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings](https://mlsanity.com/paper/2608.18114/) (2026)
- [Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images](https://mlsanity.com/paper/2605.28693/) (2026)
- [Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models](https://mlsanity.com/paper/2608.11656/) (2026)

## Abstract (arXiv)

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.