@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} } @article {4255, title = {Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones}, journal = {Nature Neuroscience}, year = {2019}, month = {06/10/2019}, abstract = {

We report a difference between humans and macaque monkeys in the functional organization of cortical regions implicated in pitch perception. Humans but not macaques showed regions with a strong preference for harmonic sounds compared to noise, measured with both synthetic tones and macaque vocalizations. In contrast, frequency-selective tonotopic maps were similar between the two species. This species difference may be driven by the unique demands of speech and music perception in humans.

}, issn = {1097-6256}, doi = {10.1038/s41593-019-0410-7}, url = {https://www.nature.com/articles/s41593-019-0410-7}, author = {Sam V Norman-Haignere and Nancy Kanwisher and Josh H. McDermott and B. R. Conway} } @article {3573, title = {A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy}, journal = {Neuron}, volume = {98}, year = {2018}, month = {04/2018}, abstract = {

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy{\textemdash}primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems.

}, keywords = {auditory cortex, convolutional neural network, deep learning, deep neural network, encoding models, fMRI, Hierarchy, human auditory cortex, natural sounds, word recognition}, doi = {10.1016/j.neuron.2018.03.044}, url = {https://www.sciencedirect.com/science/article/pii/S0896627318302502}, author = {Alexander J. E. Kell and Daniel L K Yamins and Erica N Shook and Sam V Norman-Haignere and Josh H. McDermott} }