Why Brain-Native AI?
Brain-native AI does not mean copying the brain directly. It means using principles from biological intelligence — sparse activity, local learning, predictive processing, recurrence, memory, and relational structure — as design constraints for new AI systems.
Research Programs
Our core theoretical focuses.
Sparse Representation
We study how information can be represented through sparse active patterns rather than only dense embeddings.
Read More →Predictive Coding
We study predictive coding as a learning direction where systems update through prediction error rather than only global backpropagation.
Read More →Relational State
We study how AI systems can bind entities, roles, events, and context into reusable internal state.
Read More →Memory & Continual Learning
We study how systems can retain, update, forget, and reuse information across time without simply replaying all prior context.
Read More →Neuroscience-Inspired Learning
We use neuroscience as inspiration for architectural constraints, not as a claim that our systems replicate the brain.
Read More →Human-AI Interaction
We study how better memory, state, and context can support more natural interaction between humans and AI systems.
Read More →Current Projects
Specific, trackable implementations of our theoretical programs.
CMP — Conversational Meaning Patterns
A research project exploring sparse recurrent representation, relational state, and predictive learning for Yudi AI's brain-native framework.
View CMP Research →Experiments & Logs
We treat experiments, failures, and corrections as part of the research process. Our research pages and blog posts document what worked, what failed, what changed, and what remains open.
Collaborate With Us
Interested in collaborating on predictive coding, sparse representation, cognitive architectures, memory, or human-AI interaction?
Collaborate With Us