The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence
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Abstract (arXiv)
In the last decade, artificial intelligence (AI) models inspired by the brain have made unprecedented progress in performing real-world perceptual tasks like object classification and speech recognition. Recently, researchers of natural intelligence have begun using those AI models to explore how the brain performs such tasks. These developments suggest that future progress will benefit from increased interaction between disciplines. Here we introduce the Algonauts Project as a structured and quantitative communication channel for interdisciplinary interaction between natural and artificial intelligence researchers. The project's core is an open challenge with a quantitative benchmark whose goal is to account for brain data through computational models. This project has the potential to provide better models of natural intelligence and to gather findings that advance AI. The 2019 Algonauts Project focuses on benchmarking computational models predicting human brain activity when people look at pictures of objects. The 2019 edition of the Algonauts Project is available online: http://algonauts.csail.mit.edu/.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 4; 2024: 2; 2025: 1; 2026: 1.
11 papers.
- 2023The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenescited by 34
- 2023Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)cited by 20
- 2021The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motioncited by 17
- 2023Brain Decodes Deep Netscited by 14
- 2022Visio-Linguistic Brain Encodingcited by 13
- 2025Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)cited by 5
- 2022Net2Brain: A Toolbox to compare artificial vision models with human brain responsescited by 1
- 2023Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Picturescited by 1
- 2024Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEGcited by 1
- 2024Bayesian Comparisons Between Representationscited by 1
- 2026MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encodingcited by 1
- 2026MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding28 May 2026
- 2025Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)23 Jul 2025
- 2024Bayesian Comparisons Between Representations13 Nov 2024
- 2024Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG5 Sep 2024
- 2023Brain Decodes Deep Nets3 Dec 2023
- 2023Exploration and Comparison of Deep Learning Architectures to Predict Brain Response to Realistic Pictures11 Sep 2023
- 2023Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)17 Jul 2023
- 2023The Algonauts Project 2023 Challenge: How the Human Brain Makes Sense of Natural Scenes9 Jan 2023
- 2022Net2Brain: A Toolbox to compare artificial vision models with human brain responses20 Aug 2022
- 2022Visio-Linguistic Brain Encoding18 Apr 2022
- 2021The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion28 Apr 2021