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A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.

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.

SUBMITTER: Pennington JR

PROVIDER: S-EPMC10191263 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.

Pennington Jacob R JR David Stephen V SV

PLoS computational biology 20230505 5

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 ...[more]

PMID: 37146065

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Project description:Auditory cortex encodes information about nonlinear combinations of spectro-temporal sound features. Convolutional neural networks (CNNs) provide an architecture for generalizable encoding models that can predict time-varying neural activity evoked by natural sounds with substantially greater accuracy than established models. However, the complexity of CNNs makes it difficult to discern the computational properties that support their improved performance. To address this limitation, we developed a method to visualize the tuning subspace captured by a CNN. Single-unit data was recorded using high channel-count microelectrode arrays from primary auditory cortex (A1) of awake, passively listening ferrets during presentation of a large natural sound set. A CNN was fit to the data, replicating approaches from previous work. To measure the tuning subspace, the dynamic spectrotemporal receptive field (dSTRF) was measured as the locally linear filter approximating the input-output relationship of the CNN at each stimulus timepoint. Principal component analysis was then used to reduce this very large set of filters to a smaller subspace, typically requiring 2-10 filters to account for 90% of dSTRF variance. The stimulus was projected into the subspace for each neuron, and a new model was fit using only the projected values. The subspace model was able to predict time-varying spike rate nearly as accurately as the full CNN. Sensory responses could be plotted in the subspace, providing a compact model visualization. This analysis revealed a diversity of nonlinear responses, consistent with contrast gain control and emergent invariance to spectrotemporal modulation phase. Within local populations, neurons formed a sparse representation by tiling the tuning subspace. Narrow spiking, putative inhibitory neurons showed distinct patterns of tuning that may reflect their position in the cortical circuit. These results demonstrate a conceptual link between CNN and subspace models and establish a framework for interpretation of deep learning-based models.

Dataset Information

A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.

Publications

A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.

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