A Grand Unified Neuroalgorithmic Theory of Mind and Blueprint for Artificial General Intelligence (AGI)

The Brain’s Algorithm of Cognition and Affect, Its Logical Derivation from Neuroscientific First Principles, and Resynthesis into a Neuroalgorithmic Computational Architecture and Formalism for Artificial General Intelligence. Decades of neuroscientific research have produced a wealth of findings, yet a formal, overarching framework that integrates the brain's disparate components into a single, coherent algorithm has remained elusive. This paper addresses this gap by first deriving the brain's core cognitive-affective algorithm directly from a set of neuroscientific first principles. The methodology involves a step-by-step process where functional constraints define algorithmic roles, which are then mapped to established biological and theoretical principles. This derived algorithm forms the basis for a Resynthetic Architectural Design: a concrete computational architecture based on the same neuroalgorithmic principles as the brain. The resulting 12-component system provides a mechanistic unification of several foundational theories, including Predictive Coding, Global Workspace Theory, and the Somatic Marker Hypothesis. The model offers novel solutions to the symbol grounding and temporal credit assignment problems, assigns specific computational roles to neural oscillations, and provides a mechanistic basis for key event-related potentials (ERPs). By formalizing the brain's operating principles into a complete and computable system, this work provides a testable blueprint for Artificial General Intelligence (AGI), a new framework for computational psychiatry, and a concrete model for investigating long-standing questions in the philosophy of mind.