Encoding of speech in convolutional layers and the brain stem based on language experience.

ABSTRACT: Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computations of spoken language representations and propose several new challenges to this paradigm. The proposed technique is based on a similar principle that underlies electroencephalography (EEG): averaging of neural (artificial or biological) activity across neurons in the time domain, and allows to compare encoding of any acoustic property in the brain and in intermediate convolutional layers of an artificial neural network. Our approach allows a direct comparison of responses to a phonetic property in the brain and in deep neural networks that r

SUBMITTER: Begus G 

PROVIDER: S-EPMC10119295 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature