ArticlePLoS computational biology2023
A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex.Nature neuroscience · 2026Article
- Temporal integration in human auditory cortex is predominantly yoked to absolute time.Nature neuroscience · 2025Article
- Temporal recurrence as a general mechanism to explain neural responses in the auditory system.Communications biology · 2025Article
- Interference of Mid-level Speech and Noise Statistics Underlies Human Speech Recognition Sensitivity in Natural Environmental Noise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Deep neural networks explain spiking activity in auditory cortex.PLoS computational biology · 2025Article
- Reduced Neural Responses to Natural Foreground versus Background Sounds in the Auditory Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Modelling neural coding in the auditory midbrain with high resolution and accuracy.Nature machine intelligence · 2025Article
- Subcortical origin of nonlinear sound encoding in auditory cortex.Current biology : CB · 2024Article
- A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses.PLoS computational biology · 2024Article
- Cortical determinants of loudness perception and auditory hypersensitivity.bioRxiv : the preprint server for biology · 2024Article
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2 authors.
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Abstract
Convolutional neural networks (CNNs) can provide powerful and flexible models of neural sensory processing. However, the utility of CNNs in studying the auditory system has been limited by their requirement for large datasets and the complex response properties of single auditory neurons. To address these limitations, we developed a population encoding model: a CNN that simultaneously predicts activity of several hundred neurons recorded during presentation of a large set of natural sounds. This approach defines a shared spectro-temporal space and pools statistical power across neurons. Population models of varying architecture performed consistently and substantially better than traditional linear-nonlinear models on data from primary and non-primary auditory cortex. Moreover, population models were highly generalizable. The output layer of a model pre-trained on one population of neurons could be fit to data from novel single units, achieving performance equivalent to that of neurons in the original fit data. This ability to generalize suggests that population encoding models capture a complete representational space across neurons in an auditory cortical field.
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