Brain-optimized extraction of complex sound features that drive continuous auditory perception.

ABSTRACT: Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match the neural representation of sound. Here, we postulate that constructing a data-driven neural model of auditory perception, with a minimum of theoretical assumptions about the relevant sound features, could provide an alternative approach and possibly a better match to the neural responses. We collected electrocorticography recordings from six patients who watched a long-duration feature film. The raw movie soundtrack was used to train an artificial neural network model to predict the associated neural responses. The model achieved high predict

SUBMITTER: Berezutskaya J 

PROVIDER: S-EPMC7363106 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature