Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway.

ABSTRACT: Recognising an object involves rapid visual processing and activation of semantic knowledge about the object, but how visual processing activates and interacts with semantic representations remains unclear. Cognitive neuroscience research has shown that while visual processing involves posterior regions along the ventral stream, object meaning involves more anterior regions, especially perirhinal cortex. Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented as activation patterns across features. In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive acc

SUBMITTER: Devereux BJ 

PROVIDER: S-EPMC6045572 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature