A neuro-inspired computational framework for AGI: predictive coding, active inference and free energy minimisation

This paper offers a conceptual synthesis of foundational principles from theoretical neuroscience [i.e., predictive coding, the Free Energy Principle (FEP) and variational inference] as a biologically grounded framework for artificial general intelligence (AGI). We adopt this predictive-coding and FEP perspective as our organising framework. It is one of several current accounts of neural computation and not all available evidence is consistent with it. These approaches characterise the brain as a hierarchical inference system that continuously updates beliefs and selects actions to minimise uncertainty and surprise. In contrast to conventional AI systems, biological agents engage in active, generative inference within dynamic, uncertain environments. We argue that this inference-based architecture and not behavioural outputs alone, plausibly underlies the adaptability, generalisation and resilience of natural intelligence. We outline a neuro-inspired computational framework built on hierarchical generative models, scalable variational inference (i.e., Variational Laplace) and Active Inference. Finally, we contrast this approach with dominant deep learning paradigms and discuss its hypothetical implications for building interpretable, adaptive and autonomous machine intelligence.