🤖 AI Summary
Systematic comparison between artificial vision models and human brain representational spaces remains methodologically fragmented and lacks standardized, reproducible frameworks.
Method: The authors developed an open-source Python toolbox that unifies over 600 cross-modal pretrained models with major neuroimaging datasets (e.g., NSD, THINGS), enabling end-to-end model–brain comparison—from model invocation and neural data acquisition to feature extraction, representational similarity analysis (RSA), and visualization. The toolbox implements a standardized neural representational alignment pipeline, supporting advanced neuroencoding techniques including searchlight analysis, linear encoding modeling, and variance decomposition.
Contribution/Results: This framework substantially improves reproducibility, flexibility, and scalability in model–brain comparative research. It has been adopted by multiple cognitive neuroscience laboratories and is advancing standardization at the intersection of computational neuroscience and artificial intelligence.
📝 Abstract
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.
