@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 {5428,
	title = {Model metamers reveal divergent invariances between biological and artificial neural networks},
	journal = {Nature Neuroscience},
	year = {2023},
	month = {10/2023},
	abstract = {<p>Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated {\textquoteleft}model metamers{\textquoteright}, stimuli whose activations within a model stage are matched to those of a natural stimulus. Metamers for state-of-the-art supervised and unsupervised neural network models of vision and audition were often completely unrecognizable to humans when generated from late model stages, suggesting differences between model and human invariances. Targeted model changes improved human recognizability of model metamers but did not eliminate the overall human{\textendash}model discrepancy. The human recognizability of a model{\textquoteright}s metamers was well predicted by their recognizability by other models, suggesting that models contain idiosyncratic invariances in addition to those required by the task. Metamer recognizability dissociated from both traditional brain-based benchmarks and adversarial vulnerability, revealing a distinct failure mode of existing sensory models and providing a complementary benchmark for model assessment.</p>
},
	issn = {1097-6256},
	doi = {10.1038/s41593-023-01442-0},
	url = {https://www.nature.com/articles/s41593-023-01442-0},
	author = {Feather, Jenelle and Leclerc, Guillaume and M{\k a}dry, Aleksander and McDermott, Josh H.}
}
