How Large Language Models Are Becoming Powerful New Tools for Probing the Human Brain

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Large language models are rapidly evolving from text generators into powerful neuroscience tools, helping researchers decode brain activity, predict neural responses, and test new theories of cognition while raising profound ethical questions about mental privacy and the nature of understanding.
In this article we explore how AI is used to reconstruct thoughts from neural data, model language areas of the brain, power next‑generation brain–computer interfaces, and reshape theories of cognition—alongside the technical hurdles and safeguards needed to use these capabilities responsibly.

Large language models (LLMs) and other deep learning systems are increasingly used as models of brains rather than just tools that sit outside neuroscience. By aligning internal representations in AI with patterns of neural activity, researchers can probe how the brain encodes language, imagery, and abstract concepts at a scale that was impossible a decade ago.

This convergence of neuroscience and AI is fueled by several trends: richer neural recording (high‑field fMRI, dense ECoG grids, Neuropixels probes), foundation models trained on trillion‑token corpora, and new analytical methods that bridge voxels, spikes, and high‑dimensional embeddings. The result is a rapidly growing toolkit for decoding brain activity, evaluating theories of cognition, and supporting patients via brain–computer interfaces (BCIs).


Researcher analyzing brain scans on multiple screens with AI visualizations
Figure 1. Neuroscientists increasingly pair brain imaging data with large AI models. Image credit: Pexels / Pavel Danilyuk.

Mission Overview: Why Use Large Language Models as Neuroscience Tools?

The core mission of this emerging field is to use powerful AI systems as computational hypotheses about how the brain represents and transforms information. Rather than building small, hand‑crafted models of a particular region, scientists now ask:

  • Do internal layers of LLMs align with activity patterns in specific cortical areas?
  • Can these models help decode what a person hears, reads, or imagines from brain signals?
  • Which architectural principles—self‑supervision, attention, predictive training—also seem to operate in biological circuits?
  • How can we harness these models to restore communication and movement in people with paralysis, while protecting mental privacy?

“Instead of designing models from first principles, we now test whether the best engineering systems happen to mirror the brain’s internal computations.”

— Imaging and AI researcher, paraphrasing themes from recent Neuron and Nature Neuroscience editorials


Technology: Brain Decoding with Deep Models

Brain decoding refers to inferring stimuli, intentions, or internal states from neural signals. LLMs and other foundation models have transformed this area by providing richly structured representational spaces that can be mapped to brain activity.

From fMRI Signals to Text Descriptions

Recent work (for example, from UT Austin and other labs) has shown that deep language models can support semantic reconstruction from non‑invasive fMRI. The workflow typically looks like this:

  1. Data collection: Participants listen to or read stories while undergoing fMRI scanning.
  2. Feature extraction: The same stories are fed through a pretrained LLM (e.g., GPT‑style transformer) to obtain contextual embeddings for each token or sentence.
  3. Encoding model: A regression model maps LLM embeddings to voxel‑level activity patterns in language‑responsive brain regions.
  4. Decoding model: At test time, brain activity is mapped back into the embedding space and then decoded into candidate text sequences, often guided by an LLM that scores linguistic plausibility.

These reconstructions are approximate and noisy, but they often capture the gist of what the participant is hearing or imagining, demonstrating that brain and model share a partially aligned representational geometry.

Vision: Reconstructing Images from Brain Signals

In the visual domain, researchers combine brain data with image‑generation models like diffusion networks. A typical approach involves:

  • Collecting fMRI data while subjects view thousands of natural images.
  • Mapping voxel activity to a latent space used by models such as Stable Diffusion or CLIP.
  • Conditioning the image generator on this latent code to recreate a synthetic approximation of the viewed image.

These reconstructions can capture broad layout, object categories, and sometimes even stylistic information, illustrating that modern generative models offer a useful scaffold for understanding visual cortex representations.

Illustration of human brain with digital patterns representing AI and data
Figure 2. AI models provide high‑dimensional feature spaces that can be aligned with neural activity patterns. Image credit: Pexels / Tara Winstead.

Technology: LLMs as Computational Models of Language Areas

One of the most striking findings of the last few years is that layers in large language models can predict human brain responses to sentences with high accuracy. Projects from MIT, Stanford, and other groups have reported that certain transformer layers correlate strongly with activity in temporal and frontal language networks.

Encoding Models and Predictivity

Researchers build encoding models that predict neural activity from text features. The pipeline usually includes:

  1. Present sentences or stories to participants during fMRI, MEG, or ECoG recording.
  2. Pass the same text through an LLM and extract layer‑wise representations.
  3. For each brain region or electrode, train a simple linear model to predict neural responses from these representations.
  4. Measure how well the model generalizes to held‑out text, using metrics such as correlation or explained variance.

High predictivity suggests that the model’s internal representation captures information similar to that represented in the corresponding brain area, even if the underlying implementation (neurons vs. attention heads) is very different.

“The brain seems to care about the computational solutions that work for language, not the particular hardware that implements them.”

— Paraphrasing themes in work by Evelina Fedorenko and colleagues on language and AI models

Limitations and Open Questions

  • Biological realism: Transformers ignore many biological constraints: spiking dynamics, Dale’s law, metabolic costs.
  • Developmental trajectory: LLMs are trained on static text, whereas humans learn language through multi‑modal interaction and social feedback.
  • Grounding: Language models largely lack sensorimotor grounding, raising questions about whether they capture meaning in the same way brains do.

Despite these gaps, LLMs serve as useful candidate models that neuroscientists can empirically test and refine, similar to how deep convolutional nets became de facto models for ventral visual cortex.


Scientific Significance: Theories of Cognition and Hierarchical Processing

The success of deep neural networks has reinvigorated long‑standing theories about how the brain builds abstract representations and predictions from sensory input.

Hierarchical Representations

Both cortex and deep networks appear to organize information hierarchically:

  • Early visual areas and shallow layers encode local edges, textures, and phonemes.
  • Intermediate areas and layers capture objects, syntactic patterns, and phrases.
  • Higher‑order cortex and deeper layers support semantics, long‑range dependencies, and task goals.

By comparing representational similarity matrices (RSMs) across brain regions and model layers, scientists test whether AI hierarchies mirror cortical ones.

Predictive Coding and Self‑Supervision

Many modern models are trained with self‑supervised objectives, such as predicting the next token or missing patches in an image. This resonates with predictive coding theories, which propose that the brain continually predicts incoming sensory inputs and updates internal models based on prediction errors.

Researchers test these ideas by:

  • Comparing error signals in models to mismatch responses in EEG/MEG (e.g., mismatch negativity paradigms).
  • Studying how surprise and uncertainty in language models correlate with neural markers of prediction error.
  • Examining whether attention mechanisms resemble top‑down modulation in cortical circuits.

Inspiration for New Experiments

AI systems also inspire new experimental designs. For example, adversarial examples and synthetically generated stimuli let researchers probe the boundaries of perception and language understanding in highly controlled ways, beyond what naturalistic datasets provide.

3D rendering of a human brain connected to a digital network
Figure 3. Hierarchical, predictive computation is a shared theme in brain and AI research. Image credit: Pexels / ThisIsEngineering.

Technology: Brain–Computer Interfaces Powered by LLMs

Brain–computer interfaces (BCIs) translate neural signals into external actions such as moving a cursor, controlling a robotic limb, or producing text and speech. LLMs now play a key role in the language and communication branch of BCI research.

Decoding Intended Speech

In several high‑profile demonstrations from academic groups and companies (for example, work published in Nature and NEJM), researchers implanted electrodes over speech motor cortex in individuals with severe paralysis. They then:

  1. Recorded neural activity while the participant attempted to speak or silently mouthed words.
  2. Trained a neural decoder to map these patterns to phonemes, characters, or word probabilities.
  3. Used an LLM as a language prior to correct errors, fill in likely words, and produce fluent sentences in real time.

LLMs help stabilize noisy neural signals, allowing communication rates approaching conversational speeds—often tens of words per minute—where previous systems managed only a few words per minute.

Text Generation from Non‑Invasive Signals

Non‑invasive approaches using EEG or fMRI are less precise but safer and more scalable. Here too, LLMs are used to:

  • Constrain output to syntactically and semantically plausible sentences.
  • Disambiguate similar neural patterns by anchoring them in context.
  • Provide an interface layer—turning raw decoded tokens into polished messages, emails, or commands.

Relevant Tools and Devices

While invasive BCI hardware is strictly clinical or experimental, some consumer‑oriented neurotech and AI hardware can help students and practitioners explore related ideas. For example:

  • Neurosky MindWave Mobile 2 EEG Headset – a popular, entry‑level EEG headset for educational neurofeedback and brain‑computer interface experiments.
  • Raspberry Pi 4 Model B – a compact, low‑power compute platform often used to prototype real‑time signal processing and machine learning pipelines at the edge.

Scientific Significance and Ethics: Mental Privacy and the Nature of Understanding

As models become better at decoding internal states, the stakes for mental privacy and consent grow higher. Current systems require cooperative subjects, specialized equipment, and hours of calibration; they cannot read arbitrary thoughts on demand. Nonetheless, many ethicists argue that now is the time to establish clear principles.

Mental Privacy and Neuro‑Rights

  • Informed consent: Participants must understand what can be decoded from their data, how it will be stored, and who can access it.
  • Purpose limitation: Data collected for medical or research purposes should not be repurposed for surveillance or commercial profiling.
  • Right to cognitive liberty: Several countries and organizations are exploring “neurorights” frameworks aimed at protecting individuals from unauthorized interference with their mental states.

“We are moving from protecting what people say and do to protecting what they think and intend. The legal toolkit must catch up.”

— Paraphrased perspective inspired by neuroethicists such as Rafael Yuste and Nita Farahany

Are AI Systems Brain‑Like, or Just Useful?

There is an active debate about whether models like GPT should be considered brain‑like at all. Key viewpoints include:

  • Engineering view: LLMs are tools; if they predict neural data, that is convenient but not evidence of shared mechanisms.
  • Computationalist view: Similar input–output behavior and representational structure suggest convergence on related computational principles.
  • Embodied cognition view: Without sensorimotor grounding and lived experience, LLMs lack crucial aspects of human understanding, even if they align with some cortical statistics.

Neuroscience provides empirical tests for these positions by examining where model–brain alignments hold and where they break.


Milestones: Key Results and Emerging Benchmarks

The field is evolving quickly, with new preprints and datasets appearing almost monthly. Some representative milestones include:

  • Natural language decoding from fMRI: Systems that reconstruct approximate narrative content from non‑invasive brain scans while participants listen to podcasts or stories.
  • Sentence‑level brain predictivity benchmarks: Large datasets that quantify how well different LLM architectures explain cortical responses to naturalistic language.
  • High‑rate speech BCIs: Invasive systems that enable people with paralysis to communicate at tens of words per minute via decoded speech, assisted by LLMs.
  • Cross‑modal decoding: Experiments where text models help interpret activity from vision or auditory cortex, hinting at shared semantic spaces.

Community Resources

To track new developments, many researchers follow:


Challenges: Technical, Conceptual, and Societal

Despite impressive progress, using LLMs as neuroscience tools faces substantial challenges.

Technical Hurdles

  • Data scarcity and noise: High‑quality neural data are expensive and time‑consuming to collect; fMRI is slow and coarse, EEG is noisy, and invasive recordings are rare and ethically constrained.
  • Overfitting and generalization: With limited subjects and sessions, models can overfit individual idiosyncrasies rather than capturing general principles.
  • Model transparency: LLMs are high‑dimensional and hard to interpret; mapping their internal states onto brain activity does not automatically explain mechanisms.

Conceptual Pitfalls

  • Reverse inference: Inferring mental states from brain activity is already risky; adding AI models can create an illusion of precision that outstrips what the data warrant.
  • Anthropomorphism: Because LLMs produce human‑like text, it is easy to ascribe them human‑like understanding or consciousness without robust evidence.
  • Benchmark myopia: Optimizing models for a particular neural benchmark can hide other important aspects of cognition that are not captured by that dataset.

Societal and Regulatory Challenges

Governments and institutions are only beginning to adapt policy to neuro‑AI. Important questions include:

  • Who owns neural data and derived models?
  • How should insurers, employers, or legal systems be allowed—or forbidden—to use brain‑derived information?
  • What safeguards are needed when neuro‑AI tools move from research labs into clinics and consumer products?
Surgeon or researcher in operating room using advanced imaging and computer tools
Figure 4. Translating neuro‑AI systems from lab to clinic requires careful validation and regulation. Image credit: Pexels / Anna Shvets.

Practical On‑Ramps: How Researchers and Students Can Get Involved

For those interested in this intersection of AI and neuroscience, several practical steps can accelerate learning.

Core Skill Areas

  • Machine learning foundations: Linear models, regularization, representation learning, and evaluation metrics.
  • Neuroimaging methods: Basics of fMRI, EEG/MEG, and ECoG, including preprocessing and artifact removal.
  • Model–brain comparison techniques: Encoding/decoding models, representational similarity analysis (RSA), and cross‑validated predictive modeling.

Recommended Reading and Courses

  • Online courses in computational neuroscience (e.g., offerings from Coursera or edX in collaboration with universities).
  • Review articles on deep learning and the brain in journals like Neuron, Nature Neuroscience, and Trends in Cognitive Sciences.
  • Technical blogs and open‑source repositories for code implementing neural encoding/decoding pipelines.

Helpful Hardware and Books


Conclusion: A New Lens on Brains—and on AI

Using large language models as neuroscience tools is more than an intriguing crossover; it is reshaping how we study minds. By providing detailed, testable hypotheses about internal representations, LLMs allow neuroscientists to ask sharper questions about what, where, and how information is encoded in the brain.

At the same time, neuroscience offers constraints and benchmarks that keep AI research honest: a model that predicts behavior and neural data across many tasks is more likely to be capturing something fundamental about cognition. The feedback loop is mutually reinforcing—better AI begets better brain models, which in turn inspire more brain‑like AI.

As this synergy deepens, thoughtful governance, transparent communication, and robust ethical frameworks will be essential. The goal is a future where neuro‑AI tools advance medicine, deepen our scientific understanding, and respect the inviolable dignity of human thought.


Additional Resources and Future Directions

For readers who want to follow cutting‑edge developments closely, consider the following strategies:

  • Subscribe to newsletters or feeds that curate neuro‑AI papers, such as topic‑specific digests on arXiv or institutional mailing lists.
  • Attend online seminars and conferences (e.g., Cosyne, NeurIPS workshops on cognitive modeling, or SfN symposia on AI and the brain).
  • Engage with professional communities on LinkedIn or Slack groups devoted to computational neuroscience and machine learning.

Looking ahead, several trends are especially promising:

  • Multi‑modal foundation models: Joint training on text, images, audio, and video may yield representations that align even more closely with multisensory cortical areas.
  • Closed‑loop neuro‑AI systems: Real‑time interaction between brain signals and adaptive AI models could enable more precise stimulation therapies and BCIs.
  • Personalized models: Combining large pretrained LLMs with individualized neural calibration could lead to bespoke prosthetics for language and memory.

Whether you approach from the AI side or the neuroscience side, this is a uniquely rich moment to contribute to understanding one of the central questions in science: how physical brains give rise to flexible, meaningful thought—and how artificial systems might mirror, differ from, or augment that process.


References / Sources

Selected accessible references and resources (not exhaustive):

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