ArticleImaging neuroscience (Cambridge, Mass.)2025
Alignment of auditory artificial networks with massive individual fMRI brain data leads to generalisable improvements in brain encoding and downstream tasks.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Human brain data: a valuable and open-ended resource for artificial intelligence.Frontiers in human neuroscience · 2026Article
- Article
- Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes.bioRxiv : the preprint server for biology · 2025Article
- Behavioral imitation with artificial neural networks leads to personalized models of brain dynamics during videogame play.Imaging neuroscience (Cambridge, Mass.)Article
- Gamer in the scanner: Event-related analysis of fMRI activity during retro videogame play guided by automated annotations of game content.Imaging neuroscience (Cambridge, Mass.)Article
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5 authors.
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Abstract
Artificial neural networks trained in the field of artificial intelligence (AI) have emerged as key tools to model brain processes, sparking the idea of aligning network representations with brain dynamics to enhance performance on AI tasks. While this concept has gained support in the visual domain, we investigate here the feasibility of creating auditory artificial neural models directly aligned with individual brain activity. This objective raises major computational challenges, as models have to be trained directly with brain data, which is typically collected at a much smaller scale than data used to train AI models. We aimed to answer two key questions: (1) Can brain alignment of auditory models lead to improved brain encoding for novel, previously unseen stimuli? (2) Can brain alignment lead to generalisable representations of auditory signals that are useful for solving a variety of complex auditory tasks? To answer these questions, we relied on two massive datasets: a deep phenotyping dataset from the Courtois neuronal modelling project, where six subjects watched four seasons (36 h) of the
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