ArticleFrontiers in neuroinformatics2021
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- The orbitofrontal-anterior temporal network complements the attention network for where to look in social situations.eNeuro · 2026Article
- Dynamic Representation of Multidimensional Object Properties in the Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- When knowledge interferes with perception: Neural mechanisms of the semantic amplification of visual false memory.bioRxiv : the preprint server for biology · 2026Article
- Exploring the role of micro-valence in the phenomenal space: insights from similarity judgments and deep learning models.Neuroscience of consciousness · 2026Article
- Article
- Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images.bioRxiv : the preprint server for biology · 2025Article
- The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Dimensions underlying the representational alignment of deep neural networks with humans.Nature machine intelligence · 2025Article
- High-level visual representations in the human brain are aligned with large language models.Nature machine intelligence · 2025Article
- Distributed representations of behaviour-derived object dimensions in the human visual system.Nature human behaviour · 2024Article
- Maintenance and transformation of representational formats during working memory prioritization.Nature communications · 2024Article
- Graspable foods and tools elicit similar responses in visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training.iScience · 2024Article
- Distributed representations of behavior-derived object dimensions in the human visual system.bioRxiv : the preprint server for biology · 2024Article
- Graspable foods and tools elicit similar responses in visual cortex.bioRxiv : the preprint server for biology · 2024Article
- Article
- Recurrent connectivity supports higher-level visual and semantic object representations in the brain.Communications biology · 2023Article
- Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Extracting and visualizing hidden activations and computational graphs of PyTorch models with TorchLens.Scientific reports · 2023Article
- TorchLens: A Python package for extracting and visualizing hidden activations of PyTorch models.bioRxiv : the preprint server for biology · 2023Article
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2 authors.
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Abstract
Over the past decade, deep neural network (DNN) models have received a lot of attention due to their near-human object classification performance and their excellent prediction of signals recorded from biological visual systems. To better understand the function of these networks and relate them to hypotheses about brain activity and behavior, researchers need to extract the activations to images across different DNN layers. The abundance of different DNN variants, however, can often be unwieldy, and the task of extracting DNN activations from different layers may be non-trivial and error-prone for someone without a strong computational background. Thus, researchers in the fields of cognitive science and computational neuroscience would benefit from a library or package that supports a user in the extraction task. THINGSvision is a new Python module that aims at closing this gap by providing a simple and unified tool for extracting layer activations for a wide range of pretrained and randomly-initialized neural network architectures, even for users with little to no programming experience. We demonstrate the general utility of THINGsvision by relating extracted DNN activations to a number of functional MRI and behavioral datasets using representational similarity analysis, which can be performed as an integral part of the toolbox. Together, THINGSvision enables researchers across diverse fields to extract features in a streamlined manner for their custom image dataset, thereby improving the ease of relating DNNs, brain activity, and behavior, and improving the reproducibility of findings in these research fields.
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