@article {5460,
	title = {Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions},
	journal = {PLOS Biology},
	volume = {21},
	year = {2023},
	month = {12/2023},
	pages = {e3002366},
	abstract = {<p>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.</p>
},
	doi = {10.1371/journal.pbio.3002366},
	url = {https://dx.plos.org/10.1371/journal.pbio.3002366},
	author = {Tuckute, Greta and Feather, Jenelle and Boebinger, Dana and McDermott, Josh H.},
	editor = {Poeppel, David}
}
@article {5027,
	title = {A neural population selective for song in human auditory cortex},
	journal = {Current Biology},
	year = {2022},
	month = {02/2022},
	abstract = {<p>How is music represented in the brain? While neuroimaging has revealed some spatial segregation between responses to music versus other sounds, little is known about the neural code for music itself. To address this question, we developed a method to infer canonical response components of human auditory cortex using intracranial responses to natural sounds, and further used the superior coverage of fMRI to map their spatial distribution. The inferred components replicated many prior findings, including distinct neural selectivity for speech and music, but also revealed a novel component that responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features, was located near speech and music-selective responses, and was also evident in individual electrodes. These results suggest that representations of music are fractionated into subpopulations selective for different types of music, one of which is specialized for the analysis of song.</p>
},
	issn = {09609822},
	doi = {10.1016/j.cub.2022.01.069},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0960982222001312},
	author = {Norman-Haignere, Sam V. and Jenelle Feather and Boebinger, Dana and Brunner, Peter and Ritaccio, Anthony and Josh H. McDermott and Schalk, Gerwin and Nancy Kanwisher}
}
