Cognitive neuroscience faces a paradox: neural data are abundant, yet conceptual synthesis has stalled because dominant contrast-based approaches show where activity differs but not how cognitive operations relate or transform. Here, we propose a generative-transformational logic grounded in AI and neural geometry, treating cognition as lawful mappings among neural states. Generative models can learn latent transformations linking states across tasks, contexts, and individuals. Because transformation success is testable, this framework enables counterfactual simulation and connects data-driven modeling with theory-driven inference. It moves cognitive neuroscience from mapping correlates toward algorithmic explanations of how the brain generates and reorganizes cognition over time. — Read More

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For decades, neuroscientists have known that specific regions in the brain’s left hemisphere are responsible for processing language. However, a new study by MIT researchers shows that language processing also occurs in many other parts of the brain.

Using functional magnetic resonance imaging (fMRI) data from more than 700 people, the researchers identified 17 additional regions of the brain that appear to play a role in language. These regions are scattered across the brain, including parts of the cerebellum, hippocampus, and cerebral cortex, and they make up about 5 percent of the total volume of the adult brain — about the size of a large strawberry. — Read More

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Last year, we introduced Brain2Qwerty v1, research that uses AI to decode brain activity into text without any surgical implant. Now we’re sharing the next step: Brain2Qwerty v2, the highest-performing end-to-end pipeline capable of real-time sentence decoding from non-invasive brain recordings, approaching levels of accuracy previously exclusive to techniques that require brain surgery.

To help accelerate neuroscience breakthroughs, we’re releasing the full training code for Brain2Qwerty v1 and v2, and our partner, the Basque Center on Cognition, Brain, and Language (BCBL), is releasing the v1 dataset. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions that prevent them from communicating. Invasive procedures like stereotactic electroencephalography and electrocorticography have shown that a neuroprosthesis feeding signals to an AI decoder can restore communication, but they’re difficult to scale. Our noninvasive approach can help bridge that gap. — Read More

Speaking may be more about what the brain hears and feels than how it moves—a finding that could transform speech recovery after stroke.

A new study suggests that learning and remembering speech relies more on how the brain processes sounds and sensations than on the areas that control mouth and face movements. The discovery could reshape speech therapy and help improve future brain-based communication technologies.

The study, conducted by researchers at McGill University and the Yale School of Medicine, could reshape scientific understanding of how speech is learned and influence the design of future speech recognition and brain-based communication technologies. — Read More

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The relationship between the geometry of neural representations and the task being performed is a central question in neuroscience. The primate prefrontal cortex (PFC) is a primary focus of inquiry, as it can encode information with geometries that either rely on past experience or are experience agnostic. One hypothesis is that PFC representations should evolve with learning, from a format that supports exploration of all possible task rules to a format that minimizes the encoding of task-irrelevant features and supports generalization. Here we test this idea by recording neural activity from the macaque PFC when learning a new rule (‘XOR rule’) from scratch. We show that PFC representations progress from being high dimensional, nonlinear and randomly mixed to low dimensional and rule selective. Upon generalizing the rule to new stimuli, these representations further evolve into an abstract, stimulus-invariant geometry. These findings reconcile previously conflicting accounts of PFC function by demonstrating how neural representations adapt across distinct stages of learning. — Read More

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Over a thousand children visit Boston Children’s Hospital every day. Many get clear diagnoses and begin treatment, but a small subset of pediatric visitors with rare illnesses never get diagnoses at all. That has started to change with the help of AI.

New research from the hospital’s center for rare diseases and the AI company OpenAI reveals that off-the-shelf AI tools can help identify which errors in patients’ genomes might be causing the children’s diseases.

The findings, announced Thursday in the New England Journal of Medicine’s AI-focused publication, NEJM AI, show that OpenAI’s o3 Deep Research model helped clarify 18 diagnoses for children who had struggled to find causes for their illnesses and symptoms. — Read More

Researchers identified a new class of traveling brain waves that rotate over space and time. The study reveals that these vortex-like waves are driven by a unique, circular “merry-go-round” architectural layout of neurons in the sensory cortex.

Operating globally, these spiral waves synchronize activity across hemispheres, between sensory and motor networks, and down into deep subcortical structures—acting as a spatiotemporal clock to coordinate sensation, predict sequences, and guide voluntary physical action. — Read More

Like a stadium full of sports fans doing the wave, neurons coordinate their electrical signals in rhythmic patterns that sweep across the cortex, the brain’s outermost layer. Recent studies in humans and animals have shown these patterns, called traveling waves, can take on complex shapes, among them a rotating spiral that has been observed during deep sleep, memory retrieval, and other brain processes. A new study has now captured the fast-spinning waves spanning whole brains, offering clues to how they’re organized and what they might do.

The study, published today in Science, examined the brains of mice using multiple recording and imaging methods to reveal brainwide patterns that unite disparate regions from the cortex to the deep brain. The research suggests rotating waves have a key role in coordinating the flow of information across the brain to support perception and behavior. It also offers an explanation for the waves’ spiral pattern by showing that they move along a circular path laid by axons—the long projections of neurons. — Read More

Today we’re gonna announce something a little weird and a little crazy, but also spectacular and filled with hope.

… We’re building a bold new kind of machine to reimagine the foundations of healthcare and our relationships to our bodies.

… It starts by stepping into a shallow pool of golden light. You then begin to descend into the water. Your body passes through a ring of underwater sensors, each acting like a dolphin, using its echolocation. The sensors send ultrasonic sound waves through your body from every angle. With enough waves, and enough angles, we form an image of what’s happening inside your body. — Read More

Elon Musk says AGI by 2026. Sam Altman says the end of the decade. Dario Amodei at Anthropic described systems “better than almost all humans at almost everything” by 2026 or 2027. Shane Legg of Google DeepMind gives roughly 50% odds for minimal AGI by 2028. Jensen Huang says 2029. Ray Kurzweil, who first published his prediction in 2005, holds firm at 2029 for AGI and has since moved his broader singularity timeline to around 2032. Yann LeCun thinks AGI is decades away, not years. Geoffrey Hinton says somewhere between 2028 and 2043.

These are not random guesses from random people. These are the individuals building, funding, and directing the most consequential AI systems on Earth. And their estimates span a range of nearly 40 years. So the question is not who is right, because nobody knows.

What reasoning produces each of these dates? Once you understand the math underneath the predictions, you understand what each person actually believes about the future, and what assumptions they are making that they rarely explain in public. — Read More