  The capabilities of transformer networks such as ChatGPT and other Large
Language Models (LLMs) have captured the world's attention. The crucial
computational mechanism underlying their performance relies on transforming a
complete input sequence - for example, all the words in a sentence - into a
long "encoding vector" that allows transformers to learn long-range temporal
dependencies in naturalistic sequences. Specifically, "self-attention" applied
to this encoding vector enhances temporal context in transformers by computing
associations between pairs of words in the input sequence. We suggest that
waves of neural activity traveling across single cortical areas or multiple
regions at the whole-brain scale could implement a similar encoding principle.
By encapsulating recent input history into a single spatial pattern at each
moment in time, cortical waves may enable temporal context to be extracted from
sequences of sensory inputs, the same computational principle used in
transformers.
