@article {5027, title = {A neural population selective for song in human auditory cortex}, journal = {Current Biology}, year = {2022}, month = {02/2022}, abstract = {

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.

}, 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} } @proceedings {4373, title = {Metamers of neural networks reveal divergence from human perceptual systems}, year = {2019}, month = {11/2019}, address = {Vancouver, Canada}, abstract = {

Deep neural networks have been embraced as models of sensory systems, instantiating representational transformations that appear to resemble those in the visual and auditory systems. To more thoroughly investigate their similarity to biological systems, we synthesized model metamers {\textendash} stimuli that produce the same responses at some stage of a network{\textquoteright}s representation. We generated model metamers for natural stimuli by performing gradient descent on a noise signal, matching the responses of individual layers of image and audio networks to a natural image or speech signal. The resulting signals reflect the invariances instantiated in the network up to the matched layer. We then measured whether model metamers were recognizable to human observers {\textendash} a necessary condition for the model representations to replicate those of humans. Although model metamers from early network layers were recognizable to humans, those from deeper layers were not. Auditory model metamers became more human-recognizable with architectural modifications that reduced aliasing from pooling operations, but those from the deepest layers remained unrecognizable. We also used the metamer test to compare model representations. Cross-model metamer recognition dropped off for deeper layers, roughly at the same point that human recognition deteriorated, indicating divergence across model representations. The results reveal discrepancies between model and human representations, but also show how metamers can help guide model refinement and elucidate model representations.

}, url = {https://papers.nips.cc/paper/9198-metamers-of-neural-networks-reveal-divergence-from-human-perceptual-systems}, author = {Jenelle Feather and Alex Durango and Ray Gonzalez and Josh H. McDermott} } @article {4556, title = {Representational similarity precedes category selectivity in the developing ventral visual pathway}, journal = {NeuroImage}, volume = {197}, year = {2019}, month = {Jan-08-2019}, pages = {565 - 574}, issn = {10538119}, doi = {10.1016/j.neuroimage.2019.05.010}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31077844}, author = {Cohen, Michael A. and Dilks, Daniel D. and Kami Koldewyn and Weigelt, Sarah and Jenelle Feather and Alexander J. E. Kell and Keil, Boris and Fischl, Bruce and Z{\"o}llei, Lilla and Lawrence Wald and Rebecca Saxe and Nancy Kanwisher} } @proceedings {4389, title = {Untangling in Invariant Speech Recognition}, year = {2019}, month = {11/2019}, address = {Vancouver, Canada}, abstract = {

Encouraged by the success of deep convolutional neural networks on a variety of visual tasks, much theoretical and experimental work has been aimed at understanding and interpreting how vision networks operate. At the same time, deep neural networks have also achieved impressive performance in audio processing applications, both as sub-components of larger systems and as complete end-to-end systems by themselves. Despite their empirical successes, comparatively little is understood about how these audio models accomplish these tasks.In this work, we employ a recently developed statistical mechanical theory that connects geometric properties of network representations and the separability of classes to probe how information is untangled within neural networks trained to recognize speech. We observe that speaker-specific nuisance variations are discarded by the network{\textquoteright}s hierarchy, whereas task-relevant properties such as words and phonemes are untangled in later layers. Higher level concepts such as parts-of-speech and context dependence also emerge in the later layers of the network. Finally, we find that the deep representations carry out significant temporal untangling by efficiently extracting task-relevant features at each time step of the computation. Taken together, these findings shed light on how deep auditory models process their time dependent input signals to carry out invariant speech recognition, and show how different concepts emerge through the layers of the network.

}, author = {Cory Stephenson and Jenelle Feather and Suchismita Padhy and Oguz Elibol and Hanlin Tang and Josh H. McDermott and SueYeon Chung} }