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Many deep neural network architectures loosely based on brain networks haverecently been shown to replicate neural firing patterns observed in the brain.One of the most exciting and promising novel architectures, the Transformerneural network, was developed without the brain in mind. In this work, we showthat transformers, when equipped with recurrent position encodings, replicatethe precisely tuned spatial representations of the hippocampal formation; mostnotably place and grid cells. Furthermore, we show that this result is nosurprise since it is closely related to current hippocampal models fromneuroscience. We additionally show the transformer version offers dramaticperformance gains over the neuroscience version. This work continues to bindcomputations of artificial and brain networks, offers a novel understanding ofthe hippocampal-cortical interaction, and suggests how wider cortical areas mayperform complex tasks beyond current neuroscience models such as languagecomprehension.

Relating transformers to models and neural representations of the hippocampal formation