Building foundation models of human state to understand brains, bodies, and environments
nubrain - A foundation model for neural decoding
Building the world's largest dataset of human brain activity.
Constellation Systems, Inc | LinkedIn
Constellation Systems, Inc | 618 followers on LinkedIn. Building foundation models of human state for AI, neurotech, and anything that interacts with people. | A new platform for human-centered intelligence. Multimodal models, novel data, and a long-term vision. We’ll share more when the time is right.
Foundation Model Predicts Brain Responses to Visual and Auditory Stimuli | Elisa Cascardi posted on the topic | LinkedIn
Thrilled to share this work with the world! Today, we're releasing a foundation model that predicts how the human brain responds to almost any sight or sound -- and replace the need for human scans to significantly fast-track neuroscience and clinical research. 🧠 With this model, we can simulate brain responses to advance our understanding of the brain -- without the need for costly human brain scans 🌐 By using improved understanding of how efficient our brains perceive the world around us, we can guide the development of more advanced AI systems 👩⚕️ With computer-simulated experimentation, we can now speedup clinical research to diagnose neurological diseases and find treatments faster We've open sourced the model and code for researchers to use and build on, and an interactive demo for you to learn more -- see below! 📄 Paper: https://lnkd.in/e7cbunJp 💻 Code: https://lnkd.in/ebwBVuJp ▶️ Demo: https://lnkd.in/eEUVxP4S 🤗 Model: https://lnkd.in/e2T8nPJP So thrilled to be a part of this team with Stéphane d'Ascoli Jean-Rémi King Jérémy RAPIN Yohann Benchetrit Teon Brooks Katelyn Begany Joséphine Raugel Hubert Banville and for the great teamwork with Diego Marcos Dominic Giardini bringing this research to life! #neuroscience #AI #aiforscience #opensource #neuroAI
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
Understanding how the human brain processes the world around us is one of the greatest open challenges in neuroscience. Breakthroughs here could transform how we understand and treat neurological conditions affecting hundreds of millions of people — and improve AI systems by directly guiding their development from neuroscientific principles.
The Global Brain Algorithm
<p>This is a collection of explanations on the global brain algorithm. We explain core concepts in simple terms that help to understand the algorithms as a whole.</p>
The Fabric and the Brain
Articulating agent ecologies with high-personality planetary computation
The FCLA-SFGA Modes: A Comprehensive Architecture of Autistic Cognitive States
Understanding the Six Operational Modes of the Fixed-Coordinate Layered Architecture and Spatial-Field Generative Architecture
The brain in the machine: How AI could help explain how we think | IBM
Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.
Introducing TRIBE v2: AI Model Predicts Human Brain Responses | AI at Meta posted on the topic | LinkedIn
Today we're introducing TRIBE v2, a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity. It enables zero-shot predictions for new subjects, languages, and tasks, consistently outperforming standard modeling approaches. We’re releasing the model, codebase, paper, and an interactive demo to help researchers advance neuroscience, apply brain insights to build better AI, and use computational simulation to speed up breakthroughs in neurological disease diagnosis and treatment. Try the demo and learn more here: https://go.meta.me/tribe2 | 175 comments on LinkedIn
Two Realities: Native Modes, Learned Social Systems, and the Foundations of Autistic Cognition
Part 1: The Architecture Overview
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Open-source software reveals complete 3D architecture of brain cells
The neurons in our brain that underlie thought connect to each other using tiny branch-like structures on their surfaces known as dendritic spines. Now scientists at Columbia's Zuckerman Institute and ...
A foundation model of vision, audition, and language for in-silico neuroscience | Research - AI at Meta
Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of...
Grounded world models in biological organisms and future embodied AI
Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data. However, the resulting systems remain largely based on passive training regimes where linguistic regularities create the scaffold onto which information from other modalities is attached. Conversely, neuroscience and cognitive science suggest that biological intelligence is organized in the opposite way, where grounded world models acquired through interaction with the environment provide the semantic scaffold to which language is attached. Here, we illustrate five examples of neural circuits supporting grounded world modelling, which underlie navigation in physical and conceptual spaces, affordance-based perception and interaction with objects, active perception and exploratory learning, allostatic control and emotion, and the distinction between self- and world-generated outcomes. These examples highlight several features largely missing from current embodied AI, including the role of intrinsic dynamics as a foundation for learning, the centrality of action in aligning these dynamics with the external world, the prominence of autonomous experience and open-ended learning over passive assimilation of externally provided data, and the fact that early predictive and control mechanisms scaffold higher cognitive abilities such as reasoning, conceptual navigation, planning, imagination, understanding others' minds, and communication. Finally, we discuss whether and how principles derived from biological systems may inform future embodied AI, including training regimes based on social interaction to construct world models that are not only grounded but also socially shared and aligned with human norms and values.
560 – Ecological Cognition II: Resonance
560 What the heck does the brain do in Ecological Dynamics, if it isn’t computing, processing, or representing? An introduction to the concept of Resonance.Download link
