Understanding the human brain requires linking observations across very different spatial scales, from molecules such as neurotransmitter receptors, to the architecture of cells, layers and fibers, to the macroscopic organization of whole-brain networks. Modern imaging and molecular techniques generate highly detailed information, but resulting datasets are typically large, heterogeneous, distributed across different repositories, and described in different anatomical reference systems. Consequently, researchers often struggle to combine them coherently. For example, a microscopy image, an MRI activation map and a connectivity matrix could all refer to the same brain area yet have incompatible coordinate spaces and file formats. Without a framework that anchors such data to consistently defined brain locations, the available information might remain inaccessible in practice, hindering integrative analyses, reproducible workflows and computational modeling. — Read More

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Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brain actively predicts upcoming visual inputs, and inference minimises the prediction errors. Recent studies have also shown that PC can be formulated as a discriminative model, where sensory inputs predict neural activities in a feedforward manner. However, experimental evidence suggests that the brain employs both generative and discriminative inference, while unidirectional PC models show degraded performance in tasks requiring bidirectional processing. In this work, we propose bidirectional PC (bPC), a PC model that incorporates both generative and discriminative inference while maintaining a biologically plausible circuit implementation. We show that bPC matches or outperforms unidirectional models in their specialised generative or discriminative tasks, by developing an energy landscape that simultaneously suits both tasks. We also demonstrate bPC’s superior performance in two biologically relevant tasks including multimodal learning and inference with missing information, suggesting that bPC resembles biological visual inference more closely. — Read More

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Predictive coding proposes that the brain constructs internal models of the world to continuously predict sensory input and uses resulting errors to refine these models. Over the past several decades, neurophysiological studies have reported activity patterns consistent with this view. However, varied definitions and inconsistent empirical evidence have raised questions about its validity and explanatory scope. In this review, we provide a historical overview of the predictive processing framework and evaluate its empirical support, particularly focusing on sensory prediction error signals. We argue that clarifying what information these signals represent is crucial, as they may appear similar in their responses yet reflect fundamentally different underlying computations. We then revisit predictive coding, highlighting alternative accounts of how sensory prediction error signals encode information. Finally, we outline key directions for future work, aiming to provide a constructive roadmap for the next phase of predictive processing research and to advance our understanding of the neuronal algorithms underlying perception and cognition. — Read More

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A study published in the Proceedings of the National Academy of Sciences by researchers at the University of Illinois Urbana-Champaign found that decision-making signals appear as early as the primary somatosensory cortex — the brain’s first cortical relay for touch and movement sensation — rather than emerging only in the frontal and premotor regions that classical models assigned as the seat of choice. The finding, which drew fresh coverage Monday from ScienceDaily and the university’s own Grainger College of Engineering, challenges the foundational assumption behind most modern artificial intelligence: that decisions are made at the top of a hierarchy, after information has flowed all the way up from the senses. — Read More

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A new study suggests the brain begins making decisions much earlier than scientists previously thought. Researchers found that even primary sensory regions are influenced by higher brain areas through rapid feedback loops, rather than simply passing information forward. This more dynamic view of brain function could help engineers design future AI systems that think more like biological brains while using far less power. — Read More

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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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