ArticlePLoS biology2023
Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.
Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Privileged representational axes in biological and artificial neural networks.Nature human behaviour · 2026Article
- Review
- Developmental changes in similarity between neural representations of mental arithmetic and artificial neural networks.iScience · 2026Article
- Rethinking hierarchy: the auditory system as an integrated cortical-subcortical network.Nature reviews. Neuroscience · 2026Review
- Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.PLoS computational biology · 2026Article
- Optimized feature gains explain and predict successes and failures of human selective listening.Nature human behaviour · 2026Article
- From sound to source: Human and model recognition of environmental sounds.bioRxiv : the preprint server for biology · 2026Article
- Deep Sound Synthesis Matched to Brain Activity Recapitulates Preferential Responses to Speech and Music.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Article
- Neural encoding of auditory processing in schizophrenia.Frontiers in neuroscience · 2026Article
- 101 Dalmatians: a multimodal naturalistic fMRI dataset in typical development and congenital sensory loss.Scientific data · 2025Article
- A deep learning framework for understanding cochlear implants.bioRxiv : the preprint server for biology · 2025Article
- Learning spatial hearing via innate mechanisms.PLoS computational biology · 2025Article
- Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion.The Journal of international medical research · 2025Article
- The detection of algebraic auditory structures emerges with self-supervised learning.PLoS computational biology · 2025Article
- Deep neural networks explain spiking activity in auditory cortex.PLoS computational biology · 2025Article
- Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation.PLoS biology · 2025Article
- Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.Scientific reports · 2025Article
- A large annotated dataset of vocalizations by common marmosets.Scientific data · 2025Article
- A hierarchy of processing complexity and timescales for natural sounds in the human auditory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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4 authors.
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Abstract
Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.
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