AI AND NEUROSCIENCE: How Artificial Intelligence Is Revolutionising the Study of the Brain
- Marcela Emilia Silva do Valle Pereira Ma Emilia
- Oct 28
- 6 min read
🧠 AI and Neuroscience
We are living in an unprecedented moment in the history of science.Artificial Intelligence (AI) — born from inspiration in the human brain — now returns to its point of origin: as a tool to decode it.
What began as a biological metaphor to build machines capable of learning has become a symmetrical revolution: the brain inspires AI, and AI decodes the brain.
From neuroimaging labs to mental health clinics, algorithms map neural connections, predict disorders, and translate thoughts into digital signals.But ultimately: what is AI teaching neuroscience — and what does the brain still have to teach machines?
Based on high-level studies such as the Behavioural Insights Team (BIT, 2025) report, primers from Brain, Behavior, and Immunity (Zador et al., 2024), and advances compiled by the Stanford Emerging Technology Review (2025), this post is a primer — an in-depth introduction.
🤖 What Is Artificial Intelligence and Machine Learning in Neuroscience
AI refers to the automation of complex tasks that require “intelligence,” such as pattern recognition or decision-making.
Meanwhile, machine learning (ML) — a subset of AI — involves algorithms that learn from data, improving their performance without the need for reprogramming.
In neuroscience, these tools process massive volumes of information — fMRI images, EEG signals, genomic data — to reveal patterns that escape traditional analysis.
Among the most applied methods are:
Supervised ML: used to predict disease progression or classify Alzheimer’s types based on brain images.
Unsupervised ML: identifies hidden patterns and groups subtypes of mental disorders, such as schizophrenia, according to neural connectivity.
Neural networks and deep learning: inspired by the human brain, these systems process complex and temporal data, as in CNNs (images) and RNNs (EEG time series).
The primer by Zador et al. (2024, Brain, Behavior, and Immunity) highlights that AI “finds complex patterns that conventional statistics cannot.”
But there are risks: overfitting, bias, and the “black box” nature of models demand rigorous validation and human supervision.
🌐 The Convergence Between AI and Neuroscience: A Feedback Collaboration
For decades, neuroscience shaped AI.The so-called artificial neural networks were born from the study of how human neurons communicate through synapses, and deep learning reflects brain plasticity — connections that strengthen or weaken according to experience.
Now, the influence has reversed: AI processes billions of data points in seconds, revealing hidden patterns in fMRI, EEG, and PET scans that escape traditional human analysis.
A study in Nature Reviews Neuroscience (Vollenweider & Preller, 2024) defines this era as a “symmetrical revolution” — the feedback cycle between the brain and the machine.The BIT (2025) report reinforces this: behavioural insights are paving the way for AI to create metacognitive controllers, allowing machines to “think about thinking” — avoiding biases such as overthinking (excessive deliberative reasoning — System 2, slow and analytical — applied to simple problems) and hallucinations (plausible but false responses produced by LLMs).
🔬 How AI Is Helping Decode the Brain: Transformative Applications
AI tackles the brain’s complexity — 86 billion neurons generating multidimensional data.Here are some of the most transformative applications:
🧩 1. Predictive Neuroimaging and Early Diagnosis
Algorithms detect early signs of Alzheimer’s (92% accuracy, Harvard, 2024), Parkinson’s (using random forest models on pre-motor acoustics), or epilepsy (LSTM on EEG to predict seizures).
Multimodal models integrate fMRI, EEG, and PET, with tools like RapidASPECTS — AI that “reads” the brain in seconds — accelerating thrombectomy in acute ischemic stroke.GANs generate synthetic fMRI data to preserve privacy, reaching more than 95% accuracy in tumour detection (International Journal of Advanced Applied Sciences, 2025).
🧠 2. Brain–Computer Interfaces (BCIs) and Neuroengineering
Deep learning decodes motor intentions in real time.In 2025, the Stanford Neuralink Group enabled tetraplegic patients to move robotic arms with their thoughts — the algorithm learned faster through reinforcement learning.China’s BCI plan (2025–2030) focuses on spinal cord recovery within 72 hours after injury.
🧬 3. Modelling Consciousness and Behaviour
Generative AI now simulates the Default Mode Network (DMN) — linked to self-awareness — to study dissociative disorders at Imperial College London.
Spiking Neural Networks (SNNs) replicate cortical oscillations in EEGs to predict epileptic seizures.
The BIT (2025) proposes Neurosymbolic AI: uniting System 1 (intuitive, neural) and System 2 (logical, symbolic) in virtuous loops — inspired by human metacognition — to make AI more robust, efficient, and “human.”
🧫 4. Omics and Personalisation
Genomic–EEG integration predicts Alzheimer’s risk (the APOE-ε4 gene).The combination of fMRI and behavioural data diagnoses ASD (Autism Spectrum Disorder) with 85% accuracy (Journal of Clinical Medicine, 2025).
💡 Note: GANs are two competing neural networks — one generates realistic fake data (Generator), while the other tries to detect the forgeries (Discriminator) — resulting in perfect synthetic brain images for training AI without privacy risks.
🧩 What AI Teaches Us About the Brain — and Vice Versa
At its core, all these applications reveal something profound:
AI doesn’t just observe the brain — it interacts with it.
It builds mathematical models that mirror what we are, how we think, and what we feel.
Designing AI reveals the brain’s unique properties:
⚡ Energy Efficiency: the brain consumes only 20 watts and inspires neuromorphic computing — chips that function like neurons.
🔁 Contextual Plasticity: the brain interprets with emotion and values — while AI learns from static patterns, it still struggles with empathy and humour (MIT Cognitive Systems Lab, 2025).
🧩 Networks of Meaning: only the brain operates on multiple levels (sensory, emotional, symbolic) — the foundation of symbiotic AI, where humans and machines share continuous learning (The Psychology of AI, Sarkar, 2025).
John R. Taylor (Introduction to AI – Neuroscience) sums it up perfectly:
“AI will only be intelligent when it understands what it means to feel.”
⚖️ Ethics and Cognitive Dilemmas in the Neuro-AI Era
But advances don’t come without risks.As AI penetrates deeper into neuroscience, more ethical dilemmas arise: who should have access to brain data?How can cognitive privacy be guaranteed?
Key risks identified include:
Black Box and Bias: non-transparent decisions; feedback loops creating chat chambers — “flattering AI” that reinforces biased beliefs and polarises dialogue (BIT, 2025).
Cognitive Data Harvesting: the collection of emotions and memories for manipulation — “mining the human mind” (BMC Neuroscience, 2024).
Digital Cognitive Atrophy: dependency on AI reducing introspection, memory, and creativity (École Polytechnique).
Interpersonal Relationships: interaction with AI is redefining how humans communicate and connect, recalibrating expectations of intimacy and emotional tolerance.
These risks expose the fine line between cognitive assistance and cognitive substitution.
Therefore, the future of NeuroAI must follow fundamental principles:
✅ Transparency: explainable and interpretable algorithms.
✅ Cerebral Privacy: neural data as an extension of human intimacy.
✅ Human Cooperation: the human being as the centre and purpose of technology — never a replacement.
🌱 The Future: Digital Twin Brain and Conscious AI
The research horizon points toward something that, until recently, seemed like science fiction: the Digital Twin Brain — a personalised digital copy of the human brain used to predict reactions, test medications, and model diseases.
Projects at the Allen Institute and ETH Zurich are already mapping billions of simulated synapses in real time.
In parallel, the field of NeuroAI seeks to build systems that not only imitate cognition but learn from real neurological principles — incorporating emotion, bodily perception, and intuition as computational variables.
Perhaps the future of neuroscience is not only to study the human brain, but to understand the mind as a hybrid phenomenon — biological and technological.
✨ Conclusion – The Mind as a Shared Territory
The AI–neuroscience fusion redefines intelligence.
Technology doesn’t just observe the brain — it helps us understand the mind.If AI learned from the brain how to think, now it is the brain that learns from AI how to observe itself.
🌐 “The future of intelligence is not artificial — it is shared.”
Between algorithms and synapses, what emerges is not competition,but an evolutionary partnership between mind and machine.
📚 Selected References
Behavioural Insights Team (2025). AI & Human Behaviour: Augment, Adopt, Align, Adapt.
Taylor, J. R. (2025). Introduction to AI and Neuroscience.
Sarkar, A. (2025). The Psychology of AI.
Zador, A. et al. (2024). Machine Learning in Neurobiology. Brain, Behavior, and Immunity.
Frontiers in Digital Health (2025). AI in Multimodal Neuroimaging.
MIT Cognitive Systems Lab (2025). Emotion as a Missing Variable in AI Models.
BMC Neuroscience (2024). Ethical Challenges of AI in Neural Research.
Polytechnique Insights (2024). Generative AI and Cognitive Atrophy.
Stanford Emerging Technology Review (2025). NeuroAI and Human Symbiosis.
ETH Zurich & Allen Institute (2025). Digital Twin Brain Project.
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