# Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

arXiv: https://arxiv.org/abs/2303.14871 · HTML: https://mlsanity.com/paper/2303.14871/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives](https://mlsanity.com/paper/1909.01380/) (2019)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [Finding Syntax in Human Encephalography with Beam Search](https://mlsanity.com/paper/1806.04127/) (2018)
- [Disentangling Syntax and Semantics in the Brain with Deep Networks](https://mlsanity.com/paper/2103.01620/) (2021)
- [Blackbox meets blackbox: Representational Similarity and Stability Analysis of Neural Language Models and Brains](https://mlsanity.com/paper/1906.01539/) (2019)
- [Inducing brain-relevant bias in natural language processing models](https://mlsanity.com/paper/1911.03268/) (2019)
- [Linking artificial and human neural representations of language](https://mlsanity.com/paper/1910.01244/) (2019)
- [Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses](https://mlsanity.com/paper/2106.05426/) (2021)
- [Does injecting linguistic structure into language models lead to better alignment with brain recordings?](https://mlsanity.com/paper/2101.12608/) (2021)
- [Does the brain represent words? An evaluation of brain decoding studies of language understanding](https://mlsanity.com/paper/1806.00591/) (2018)
- [Robust Evaluation of Language-Brain Encoding Experiments](https://mlsanity.com/paper/1904.02547/) (2019)
- [Identifying Necessary Elements for BERT's Multilinguality](https://mlsanity.com/paper/2005.00396/) (2020)
- [VisBERT: Hidden-State Visualizations for Transformers](https://mlsanity.com/paper/2011.04507/) (2020)
- [Connecting Neural Response measurements & Computational Models of language: a non-comprehensive guide](https://mlsanity.com/paper/2203.05300/) (2022)

## Built on by (most cited first, 9 of 9)

- [When Brain-inspired AI Meets AGI](https://mlsanity.com/paper/2303.15935/) (2023)
- [Brain-like Functional Organization within Large Language Models](https://mlsanity.com/paper/2410.19542/) (2024)
- [BrainMAP: Learning Multiple Activation Pathways in Brain Networks](https://mlsanity.com/paper/2412.17404/) (2024)
- [Decoding Linguistic Representations of Human Brain](https://mlsanity.com/paper/2407.20622/) (2024)
- [Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks](https://mlsanity.com/paper/2510.03351/) (2025)
- [LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models](https://mlsanity.com/paper/2410.20053/) (2024)
- [Uncovering Brain-Like Hierarchical Patterns in Vision-Language Models through fMRI-Based Neural Encoding](https://mlsanity.com/paper/2510.16870/) (2025)
- [A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks](https://mlsanity.com/paper/2510.24342/) (2025)
- [Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes](https://mlsanity.com/paper/2512.24901/) (2025)

## Abstract (arXiv)

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.