@conference {4525, title = {Using task-optimized neural networks to understand why brains have specialized processing for faces }, booktitle = {Computational and Systems Neurosciences}, year = {2020}, month = {02/2020}, address = {Denver, CO, USA}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @conference {4524, title = {Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks }, booktitle = {Conference on Cognitive Computational Neuroscience}, year = {2020}, month = {09/2020}, address = {Berlin, Germany}, author = {Dobs, Katharina and Alexander J. E. Kell and Julio Martinez-Trujillo and Michael Cohen and Nancy Kanwisher} } @article {4187, title = {Deep neural network models of sensory systems: windows onto the role of task constraints}, journal = {Current Opinion in Neurobiology}, volume = {55}, year = {2019}, month = {01/2019}, pages = {121 - 132}, abstract = {

Sensory neuroscience aims to build models that predict neural responses and perceptual behaviors, and that provide insight into the principles that give rise to them. For decades, artificial neural networks trained to perform perceptual tasks have attracted interest as potential models of neural computation. Only recently, however, have such systems begun to perform at human levels on some real-world tasks. The recent engineering successes of deep learning have led to renewed interest in artificial neural networks as models of the brain. Here we review applications of deep learning to sensory neuroscience, discussing potential limitations and future directions. We highlight the potential uses of deep neural networks to reveal how task performance may constrain neural systems and behavior. In particular, we consider how task-optimized networks can generate hypotheses about neural representations and functional organization in ways that are analogous to traditional ideal observer models.

}, issn = {09594388}, doi = {10.1016/j.conb.2019.02.003}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0959438818302034}, author = {Alexander J. E. Kell and Josh H. McDermott} } @article {4507, title = {Invariance to background noise as a signature of non-primary auditory cortex}, journal = {Nature Communications}, volume = {10}, year = {2019}, month = {09/2019}, abstract = {

Despite well-established anatomical differences between primary and non-primary auditory cortex, the associated representational transformations have remained elusive. Here we show that primary and non-primary auditory cortex are differentiated by their invariance to real-world background noise. We measured fMRI responses to natural sounds presented in isolation and in real-world noise, quantifying invariance as the correlation between the two responses for individual voxels. Non-primary areas were substantially more noise-invariant than primary areas. This primary-nonprimary difference occurred both for speech and non-speech sounds and was unaffected by a concurrent demanding visual task, suggesting that the observed invariance is not specific to speech processing and is robust to inattention. The difference was most pronounced for real-world background noise {\textendash} both primary and non-primary areas were relatively robust to simple types of synthetic noise. Our results suggest a general representational transformation between auditory cortical stages, illustrating a representational consequence of hierarchical organization in the auditory system.

}, doi = {10.1038/s41467-019-11710-y}, url = {http://www.nature.com/articles/s41467-019-11710-y}, author = {Alexander J. E. Kell 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} } @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} }