Assistive algorithms are widely used in brain-computer interfaces (BCIs), but their effects on neural representations remain unclear. Here, the authors show that adaptive BCIs lead the brain to learn ...

Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated da...

Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL) have achieved unprecedented success on many problem domains. During RL, these model...

A high-bandwidth neuromotor interface offers performant out-of-the-box generalization across people.

Four different perspectives

Cognitive health research is a field dedicated to understanding how our brains work when we think, remember, and learn as we go through life. When combined with medical research and rehabilitation…

Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Current solutions rely on iterative routines like Markov Chain ...

Bayesian and frequentist inference are two fundamental paradigms in statistical estimation. Bayesian methods treat hypotheses as random variables, incorporating priors and updating beliefs via Bayes' ...

Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. W...

Early studies on necessary and sufficient neural populations were done on simple invertebrate circuits. Does this logic work for complex outputs?

Wide-ranging investigation says impact on work likely to be profound, but opinion on risk of human extinction varies

YouTube video by MAIN Conference

Author summary Evolution has shaped neural circuits in the brain to support complex tasks and behavior. In doing so, single neurons have developed intriguing coding properties such as heterogeneous an...

Compositionality is believed to be fundamental to intelligence. In humans, it underlies the structure of thought, language, and higher-level reasoning. In AI, compositional representations can enable ...

The goal of machine learning is generalization. While the No Free Lunch Theorem states that we cannot obtain theoretical guarantees for generalization without further assumptions, in practice we obser...

Symbolic systems are powerful frameworks for modeling cognitive processes as they encapsulate the rules and relationships fundamental to many aspects of human reasoning and behavior. Central to these ...