Modern AI architectures may look radically new, but their power comes down to a deceptively simple principle: dynamic, programmable weights. What once seemed like a mathematical shortcut in LSTMs and Transformers turns out to mirror a fundamental mechanism of the human brain—how it computes and updates prediction errors.

From basic oscillators to complex neural networks, this guide provides a framework for selecting and converting between different models.

Drug-resistant epilepsy often requires precise identification of seizure onset zones using SEEG recordings. This article presents a Variational Autoencoder–based deep learning framework that detects and interprets seizure onset phases with transparency, helping clinicians accelerate diagnosis and build trust in AI-assisted neurology.

Explore how a dancer’s brain responds to ballet movements. EEG research uncovers changes in brain activity during jumps, spins, and pliés.

Alzheimer’s disease disrupts brain networks and is marked by shifts in brain rhythms detectable by EEG long before significant cell death. Recognizing these changes is vital for early detection and targeted interventions.

How ChatGPT affects brain activity, memory, and ownership. Learn from a new MIT study on the cognitive impact of AI writing tools and discover smarter usage strategies.

This post presents a new modeling study by our team and collaborators that uncovers the neural mechanisms behind prediction error evaluation and precision weighting in the brain. Using a biophysically grounded Laminar Neural Mass Model (LaNMM), the work offers a plausible implementation of predictive coding principles through cross-frequency coupling across cortical layers.

Can 40-Hz tACS modulate brain connectivity? This EEG study with Starstim 8 and eLORETA investigates frontoparietal dynamics.