  Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve NLP models. Mappings of an NLP model's representations of and the
brain activities evoked by linguistic input are typically deployed to reveal
this symbiosis. However, two critical problems limit its advancement: 1) The
model's representations (artificial neurons, ANs) rely on layer-level
embeddings and thus lack fine-granularity; 2) The brain activities (biological
neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e.,
voxel/region) and thus lack integrations and interactions among brain
functions. To address those problems, in this study, we 1) define ANs with
fine-granularity in transformer-based NLP models (BERT in this study) and
measure their temporal activations to input text sequences; 2) define BNs as
functional brain networks (FBNs) extracted from functional magnetic resonance
imaging (fMRI) data to capture functional interactions in the brain; 3) couple
ANs and BNs by maximizing the synchronization of their temporal activations.
Our experimental results demonstrate 1) The activations of ANs and BNs are
significantly synchronized; 2) the ANs carry meaningful linguistic/semantic
information and anchor to their BN signatures; 3) the anchored BNs are
interpretable in a neurolinguistic context. Overall, our study introduces a
novel, general, and effective framework to link transformer-based NLP models
and neural activities in response to language and may provide novel insights
for future studies such as brain-inspired evaluation and development of NLP
models.
