Bersch Domenic, Vilas Martina G, Saba-Sadiya Sari, Schaumlöffel Timothy, Dwivedi Kshitij, Sartzetaki Christina, Cichy Radoslaw M, Roig Gemma
Department of Computer Science, Goethe Universität, Frankfurt am Main, Germany.
The Hessian Center for Artificial Intelligence, Darmstadt, Germany.
Front Neuroinform. 2025 May 6;19:1515873. doi: 10.3389/fninf.2025.1515873. eCollection 2025.
In cognitive neuroscience, the integration of deep neural networks (DNNs) with traditional neuroscientific analyses has significantly advanced our understanding of both biological neural processes and the functioning of DNNs. However, challenges remain in effectively comparing the representational spaces of artificial models and brain data, particularly due to the growing variety of models and the specific demands of neuroimaging research. To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. Net2Brain provides functionalities in four key areas. First, it offers access to over 600 DNNs trained on diverse tasks across multiple modalities, including vision, language, audio, and multimodal data, organized through a carefully structured taxonomy. Second, it provides a streamlined API for downloading and handling popular neuroscience datasets, such as the NSD and THINGS dataset, allowing researchers to easily access corresponding brain data. Third, Net2Brain facilitates a wide range of analysis options, including feature extraction, representational similarity analysis (RSA), and linear encoding, while also supporting advanced techniques like variance partitioning and searchlight analysis. Finally, the toolbox integrates seamlessly with other established open source libraries, enhancing interoperability and promoting collaborative research. By simplifying model selection, data processing, and evaluation, Net2Brain empowers researchers to conduct more robust, flexible, and reproducible investigations of the relationships between artificial and biological neural representations.
在认知神经科学中，深度神经网络（DNN）与传统神经科学分析方法的整合显著推进了我们对生物神经过程以及DNN功能的理解。然而，在有效比较人工模型和脑数据的表征空间方面仍存在挑战，特别是由于模型种类不断增加以及神经成像研究的特定要求。为应对这些挑战，我们推出了Net2Brain，这是一个基于Python的工具箱，它提供了一个将DNN纳入神经科学研究的端到端管道，涵盖数据集下载、大量模型选择、特征提取、评估和可视化。Net2Brain在四个关键领域提供功能。首先，它提供了对600多个在多种模态（包括视觉、语言、音频和多模态数据）的各种任务上训练的DNN的访问，这些DNN通过精心构建的分类法进行组织。其次，它提供了一个简化的应用程序编程接口（API），用于下载和处理流行的神经科学数据集，如NSD和THINGS数据集，使研究人员能够轻松访问相应的脑数据。第三，Net2Brain促进了广泛的分析选项，包括特征提取、表征相似性分析（RSA）和线性编码，同时还支持方差划分和探照灯分析等先进技术。最后，该工具箱与其他成熟的开源库无缝集成，增强了互操作性并促进了合作研究。通过简化模型选择、数据处理和评估，Net2Brain使研究人员能够对人工和生物神经表征之间的关系进行更稳健、灵活和可重复的研究。
