Cracking the neural code for word recognition in convolutional neural
  networks
Aakash Agrawal Stanislas Dehaene
Learning to read places a strong challenge on the visual system. Years of
expertise lead to a remarkable capacity to separate highly similar letters and
encode their relative positions, thus distinguishing words such as FORM and
FROM, invariantly over a large range of sizes and absolute positions. How
neural circuits achieve invariant word recognition remains unknown. Here, we
address this issue by training deep neural network models to recognize written
words and then analyzing how reading-specialized units emerge and operate
across different layers of the network. With literacy, a small subset of units
becomes specialized for word recognition in the learned script, similar to the
"visual word form area" of the human brain. We show that these units are
sensitive to specific letter identities and their distance from the blank space
at the left or right of a word, thus acting as "space bigrams". These units
specifically encode ordinal positions and operate by pooling across low and
high-frequency detector units from early layers of the network. The proposed
neural code provides a mechanistic insight into how information on letter
identity and position is extracted and allow for invariant word recognition,
and leads to predictions for reading behavior, error patterns, and the
neurophysiology of reading.