<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 Global Brain Argument: Nodes, Computroniums and the Ai Megasystem (Target Paper for Special Issue)
The Global Brain Argument contends that many of us are, or will be, part of a global brain network that includes both biological and artificial intelligences (AIs), such as generative AIs ...
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.
Embracing the suboptimal organization of the human brain
Human brain architecture is guiding brain-inspired artificial intelligence (AI) and has been treated as an optimal template, whose deviations could mark different psychiatric and neurological conditions. We argue this premise is wrong: under any single goal (e.g., minimal wiring cost or maximal communication efficiency), the human connectome is suboptimal. Instead, its organization reflects multi-objective trade-offs navigated over evolution and development under biological and environmental constraints. For psychopathology, atypical trajectories are not distances from an ideal brain but reweighted compromises in the same trade-off space. For neuro-AI, directly duplicating the brain’s connectivity risks copying its irrelevant compromises. Treating brains and models as products of multi-objective optimization and co-tuning relevant objectives offers a more powerful framework for interpreting clinical phenotypes and designing next-generation AI.
NeuroAI
Neuroscience, cognitive science, and AI are all questing for principles that help generalization. Learn more through a live, synchronous program designed for focused, hands-on learning.
How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Neuroscientists weigh in on that tectonic change.
The Fabric and the Brain
Articulating agent ecologies with high-personality planetary computation
nubrain - A foundation model for neural decoding
Building the world's largest dataset of human brain activity.
Mathematical methods and human thought in the age of AI
Artificial intelligence (AI) is the name popularly given to a broad spectrum of computer tools designed to perform increasingly complex cognitive tasks, including many that used to solely be the...
Scaling Managed Agents: Decoupling the brain from the hands
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Danilo Bzdok | Mila
Danilo Bzdok is a computer scientist and medical doctor by training with a unique dual background in systems neuroscience and machine learning algorithms. After training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland) and Harvard Medical School, Bzdok completed two doctoral degrees, one in neuroscience at Forschungszentrum Jülich in Germany, and another in computer science (machine learning statistics) at INRIA–Saclay and the Neurospin brain imaging centre in Paris. Danilo is currently an associate professor at McGill University’s Faculty of Medicine and a Canada CIFAR AI Chair at Mila – Quebec Artificial Intelligence Institute. His interdisciplinary research centres around narrowing knowledge gaps in the brain basis of human-defining types of thinking in order to uncover key computational design principles underlying human intelligence.
The Umwelt Representation Hypothesis: rethinking Universality
Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. We argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis, which proposes that alignment arises not from convergence toward a single global optimum but from overlap in the ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in the ecological constraint space rather than as a search for a single optimal world model.
AI Data Centers Will Be Obsolete (Geometric Reasoning Explained)
Neuropeek — Accelerating the Neuro-AI convergence
Where brain data becomes shared knowledge. A community-driven platform federating neuroscience datasets, models, and tools to accelerate discovery.
Constellation
Building foundation models of human state to understand brains, bodies, and environments
