DAN - Distributed Autonomous Neuron Theory

In this video, I introduce the Distributed Autonomous Neuron (DAN) Theory, a first-principles theoretical framework that fundamentally challenges how we understand both biological brains and artificial intelligence For decades, AI has relied on a "central processor" metaphor, scaling up global parameter servers and synchronous batches. The DAN Theory proves mathematically why this approach—including current transformer-based LLMs—is provably insufficient to achieve biological-equivalent general intelligence (AGI) Instead, this framework models cognition as the emergent product of a massively parallel, self-modifying graph where each neuron operates as an autonomous probabilistic agent. No single neuron knows the global thought it participates in; it only predicts, propagates, and continuously self-corrects using bidirectional feedback Read the Full Preprints: A Graph-Theoretic Model of Cognition, Memory, and Emergent Meaning https://drive.google.com/file/d/1DoB4... Mathematical Proofs, Formal Resolution of Open Problems, and a Complete Theoretical Framework: https://drive.google.com/file/d/1DfFC... Note: This video was generated using original research PDFs from NotebookLLM. All research content rights are reserved to the author #ai #agi #neuroscience #neuralnetworks #superposition #research