Cracking the neural code for word recognition in convolutional neural networks.

ABSTRACT: Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these unit

SUBMITTER: Agrawal A 

PROVIDER: S-EPMC11410253 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature