A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

By Alexander J. E. Kell, Daniel L. K. Yamins, Erica N. Shook, Sam V. Norman-Haignere, Josh H. McDermott. Neuron, 2018

570 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/159d4bee124dabdb1b836174a2d9ef8425ff8eb1

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In Cognitive Neuroscience

Citations in the corpus

Deep neuroethology of a virtual rodent (2019, background): “lemented by neurobiological circuits, prominently in vision (Khaligh-Razavi & Kriegeskorte, 2014; Yamins et al., 2014; Kar et al., 2019), but also increasingly in other areas, including audition (Kell et al., 2018) and navigation (Banino et al., 2018; Cueva & Wei, 2018). Efforts to construct”
If deep learning is the answer, what is the question? (2020, background): “This is because contemporary deep networks are grounded in quasi-naturalistic sensory signals (such as image pixels 13 or auditory spectrograms 15 ) that allow them to perform tasks of far greater complexity than was previously possible.”
What shapes feature representations? Exploring datasets, architectures, and training (2020, background): “rained ones after ﬁnetuning on a new task [12, 23]. Furthermore, features in untrained models can explain substantial amounts of neural data, although trained models generally perform slightly better [44, 18, 39, 6, 5]. Overall, this prior work observes that untrained feature representations capture”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, background): “This includes links between convolutional neural networks and measured activity from the visual cortex [215], [216], [217] and auditory processing regions [218].”
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018, background): “, 2017), to auditory processing (Kell et al., 2018), and to the rodent tactile system (Zhuang et al.”
Dendritic cortical microcircuits approximate the backpropagation algorithm (2018, background): “…growing evidence demonstrates that deep neural networks outperform alternative frameworks in accurately reproducing activity patterns observed in the cortex (Lillicrap and Scott, 2013; Yamins et al., 2014; Khaligh-Razavi and Kriegeskorte, 2014; Yamins and DiCarlo, 2016; Kell et al., 2018).”
Routing Networks and the Challenges of Modular and Compositional Computation (2019, methodology): “Recently, Kell et al. [2018] have been able to replicate human cortical organization in the auditory cortex using a neural model with two distinct, task-dependent pathways corresponding to music and speech processing.”