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

SUBMITTER: Pennington JR 

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

REPOSITORIES: biostudies-literature